Cloud data warehouses are at the heart of digital transformation because they require no hardware, are infinitely scalable, and you only pay data resources you consume. However, that’s not the whole story.
Azure Synapse, Amazon Redshift, Google Big Query and Snowflake all require real-time data integration and lifecycle automation to realize their full potential. Yet these two capabilities are not included, forcing you to hand-code ETL scripts to close the gaps. As a result, your developers are constrained and your data transfers are constricted, compromising your initial ROI.
Take a deep dive into data warehouse automation (DWA) to know its’ history, drivers and evolving capabilities. Learn how you can reduce the dependency on ETL scripting, advance your user experience, implementation, maintenance and updates of your data warehouse and data mart environments with Qlik Compose™.
This whitepaper will outline the key tenets of a Data Analytics Platform (DAP) and illustrate how your business can adopt cloud technologies to de¬sign a fit-for-purpose solution that is cost efficient and scalable. A DAP can help your business ingest raw data, transform it, and use it for reporting, analytics, and visualizations - all at scale - giving your users the ability to draw out the relevant insights that inform better decisions.
If you are reading this eBook, you are probably considering or have already selected Google BigQuery as your modern data warehouse in the cloud — way to go. You are ahead of the curve (and your competitors with their outdated on-premise databases)! Now what?
Whether you’re a data warehouse developer, data architect or manager, business intelligence specialist, analytics professional, or tech-savvy marketer, you now need to make the most of that platform to get the most out of your data! With the growing masses of data being produced by sources as diverse as they are plentiful, a data-driven smart solution is needed. That’s where BigQuery and this eBook come in handy!
A proof of concept (PoC) is a framework of tests to determine if a product will function as you envision, and if it will truly provide long-term value that merits an investment in technology and resources. These tests should not attempt to build an entire solution; they’re what confirm or deny that the entire solution should be implemented. A PoC is an implementation on a micro scale, that can demonstrate that the larger project can be done. The proof of concept should take minimal time and if it works, it creates a starting point for the development project as a whole.
You've selected Amazon Redshift as your cloud data warehouse - now what? Read "Optimizing Amazon Redshift" to understand how to connect the dots in your data and start focusing on what’s really important - finding answers to your business' most challenging questions. Includes tried and tested best practices from Matillion's data transformation experts, who help Amazon Redshift users on a daily basis.
You've selected Snowflake as your cloud data warehouse - now what? Migrating all of your business’ data from different locations and into Snowflake (and in the format you need) is difficult. Read "Optimizing Snowflake" to understand how to get the most out of your data and the insights you seek. Includes tried and tested best practices from Matillion's data transformation experts, who help Snowflake users migrate and transform their data on a daily basis.
Changes in data warehousing result in changes and developments in the supporting processes, applications and technologies. As such the origin, growth and decline of ETL can be mapped directly against data warehousing innovations. In this eBook we review pivotal moments in data warehousing history to understand the changes in ETL, ultimately resulting in the shift from E-T-L to E-L-T for modern cloud-based data warehouses.
How do you modernize your data warehouse? Start with implementing a cloud data warehouse that can keep pace with your growing data needs, and then explore solutions that are purpose-built to work with them.
How can businesses consolidate all their data? Centralizing all data within a data warehouse can prove effective for a number of use cases. While this approach may work for some businesses, others may require advanced scalability, accessibility, and a need to better control costs. A data lake, which allows all data types in any volumes to be stored and made available without the need to transform it before being ready for analysis, can address these unique requirements by providing a cost-effective resource for scaling, storing and accessing large volumes of diverse data types.
With Matillion ETL, our customers and partners are able to make analytics-ready data available across their organizations in a fraction of the time it took before, allowing them to spend more time using data for business innovation. Read on to find out how cloud-native data transformation can help you reduce costs, centralize data sources for easier reporting, scale ETL workflows and processes, and prepare data for machine learning models.
In “A Radical Guide to Data Analytics Mastery,” you’ll discover how easy it is to switch to a modern analytics approach. Use the spreadsheet skills you’ve already got — but take advantage of repeatability, transparency, and all the other great features of workflows.
In “How to Make Workflows and Influence People: A Trusted Guide to Empowering Your Organization to Solve with Alteryx,” we’ll show you how to be your company’s touchstone for data truth as a data champion. You’ll learn how to:
• Win over your boss. We’ll walk you through the components of a great Alteryx pitch and give you supporting stats and success stories.
• Energize your fellow analysts. We’ve included a handy package of resources to light their data fire.
• Convince your major stakeholders. We’ll tell you what they care about most and how to connect that to an Alteryx solution.
The move to modern data architecture is fueled by a number of converging trends – the rise of advanced data analytics and AI, the Internet of Things, edge computing, and cloud. Both IT and business managers need to constantly ask whether their data environments are robust enough to support the increasing digitization of their organizations. Over the past year, requirements for data environments have been driven largely by cost considerations, efficiency requirements, and movement to the cloud. Download this special report for emerging best practices and key considerations today.
This report highlights the current and future state of data engineering and DataOps, including analysis on:
The adoption rate of cloud data platforms and what it means for the future of data use
The most challenging aspects of the cloud data management process
Handling sensitive data in an increasingly regulated environment
What organizations and data teams need to remain competitive in our evolving data ecosystem
To read the full findings and what the implications for organizations adopting a multi-cloud strategy, download the Immuta Data Engineering Survey: 2021 Impact Report.
Now, more than ever, the ability to pivot and adapt is a key characteristic of modern companies striving to position themselves strongly for the future. Download this year’s Data Sourcebook to dive into the key issues impact enterprise data management today and gain insights from leaders in cloud, data architecture, machine learning, data science and analytics.
By observing how users and applications regularly consume data and limiting or stopping any abnormal consumption of data in real time, we get very close to a true Zero Trust posture. Going further and creating a governance model so that data requests must flow through it ensures an even tighter mechanism of control.
Whether through intentional malicious acts or simple negligence, people are the biggest threat to your enterprise data. Download this white paper to reveal the top 5 threats to your data, along with strategic measures you can take to eliminate & prevent exposure.
This exhaustive review of Vertica 10 from Constellation Research goes beyond a general product overview. If you are a technology buyer considering modernizing your data warehouse, then this report is a must read as part of your evaluation process.
Data and analytics can supercharge a company’s success and establish competitive advantages in any industry. Accordingly, companies are thinking more holistically about their analytics strategies. Inevitably, two questions surface: What is the best approach to analytics? And what technologies and capabilities are vital for an enterprise-level environment? Download this white paper today to understand the three specific necessities that consistently factor into the success of modern enterprise analytics.
Understanding your business doesn’t need to be this hard. Data analysts should be spending most of their time generating insights, not struggling to get the data points they need. Watch “Analysis-Ready Data at Your Fingertips” to learn how Stitch can dramatically increase your analytics team productivity with a fully-managed data pipeline. This brief session also provides a step-by-step overview of how to get data integration up and running in minutes.
The five SQL query optimization tips in this e-book comprise a method for tuning your SQL Server queries for higher speed and better performance. By monitoring wait time, reviewing the execution plan, gathering object information, finding the driving table and identifying performance inhibitors, database professionals like you can improve performance in your database environment.
Database professionals agree – SQL Server performance tuning is hard. And on top of that, it never stops because complex database environments are always changing with upgrades, application updates and queries. It often feels like as soon as you get one query optimized, there’s another one right behind it that’s eating CPU time or clogging
memory or otherwise slowing down the entire database. Then, add to that, the instances when the latest SQL Server version itself has made performance worse instead of making it better as promised.
If you get into a car, how do you know if the car is fast or not? You hold down the gas pedal, and you time how long it takes before you're breakin' the law.
Now what about SQL Server: how do you know if yours is fast...or a clunker? Database performance tuners need to know three metrics about their SQL Server: how fast it's going, how hard it's working, and how big it is. Brent Ozar will explain where to get those numbers, and what normal ranges are. You'll learn why advice is so different, depending on the kind of server you're driving.
This white paper addresses key methods for successfully managing today’s complex database infrastructures, including balancing key business metrics, understanding the challenges DBAs face, and finding the right tools to monitor and manage the database environment.
A slow relational database can substantially impact the performance of the applications it supports. Users may issue thousands of transactions every minute, which are serviced by perhaps dozens of redundant web and application servers – and a single database. The relational database must preserve consistency and availability, making it a highly centralized asset. It concentrates the transactions and places a great deal of pressure on the database stack to operate at optimal levels of performance and availability. This is why the database is so critical, and it’s also why the DBAs who manage it are more than average administrators.
To successfully manage these complex database environments, one must balance key business
Discover the essentials of optimizing SQL Server management within your organization. Read our e-book and learn to assess your SQL Server environment, establish effective backup and recovery, and maintain SQL Server management optimization.
SQL Server performance is slow. Users are complaining. Your boss wants to know what's going on and what you can do to improve SQL Server performance. Where do you start? Which SQL Server query should you investigate first? What can you tune? More importantly, what can you tune without touching code?
In this on-demand session, we'll look at the entire SQL Server holistically, from the "hardware" allocated to the machine down to individual SQL Server query plans. We'll cover what tools are provided out of the box, from Performance Monitor to Query Store, that you can use to spot the bottlenecks on your system.
Then we'll talk about what you can do to improve SQL Server performance. Will throwing hardware at the problem hide it until you can put in a real fix? Or do you need to roll up your sleeves and rewrite some common, poorly performing queries? What is the tradeoff? Armed with this knowledge, not only will you be able to identify what's broken, but you'll also be able to give y
Download this special research report today to learn about the latest trends in SQL Server environments, including the evolving data landscape, pressing challenges and the increasing movement towards cloud databases amongst the members of PASS, the world’s largest community of data professionals leveraging the Microsoft data platform.
Gone are the days of the Oracle or SQL Server shop. Just when you’ve mastered one approach to database management and monitoring, business decides to cut costs by adopting the cloud and open-source databases. As if those massive changes weren’t enough, the shift toward a DevOps culture, in which companies can remain competitive by accelerating release cycles, is also becoming more prevalent.
With the inherent risks that database upgrades often bring, business needs assurances that such upgrades combine minimum risk to data integrity with minimum downtime, low cost and flexibility. SharePlex meets those expectations by enabling a safe way to upgrade your databases to Oracle 19c, whether using Enterprise Edition or Standard Edition 2, regular instance, RAC or Exadata, on-premises or to the cloud. The combination of SharePlex reliability for moving data safely, support for multiple use cases and low cost ensures SharePlex is the tool of choice for your Oracle upgrades.
Migrate your on-premises Oracle databases to leading cloud service providers using Quest® tools and safely minimize downtime, ensure data integrity, manage costs, monitor and optimize performance and perform ongoing replication.
Today, it’s no longer a question of moving IT to the cloud — it’s about choosing the best way to do it for your particular business requirements.
Since databases are critical to business operations, DBAs have great responsibility in keeping their organization up and running. For modern, IT-dependent businesses, this means ensuring high availability (HA) and disaster recovery (DR).
The goal of DevOps is to reduce the time between change request and change implementation, and to eliminate the unplanned work of break-fixing. That means removing any steps that do not contribute to that goal and automating others where possible, until developer and IT operations teams reach a defined and repeatable process. In the context of ERP, the trick is to accomplish that within the organization’s tolerance for the risk that accompanies change.
Are you thinking about moving your Oracle databases to the cloud or making the transition to Database as a Service (DBaaS)?
With cloud computing vendors offering more services at lower prices, the barriers to spinning up cloud resources are diminishing. But there are few black-and-white questions in technology and even fewer in business, which is why smart companies look at all the shades of grey in an innovation like the cloud-based database before they commit on a large scale.
This study, sponsored by Quest Software, includes the views and experiences of 285 IT decision makers, representing a fairly broad sample of company types and sizes. The survey found that databases continue to expand in size and complexity, while at the same time, more enterprises are turning to cloud-based resources to keep information highly available.
As your organization’s data becomes more and more critical, you need a way to ensure it’s never compromised by unscheduled downtime – due to a system crash or malfunction – or scheduled downtime – due to patches or upgrades to Oracle, the operating system, or applications, and storage replacement.
Migrating data from one platform to another requires a lot of planning. Some traditional migration methods are easy to use, but they only work for migrations on the same platform.
Quest® SharePlex® can replicate data across platforms, from Oracle to SQL Server, with next to no downtime, offering a flexible, low-cost alternative.
If you're planning on migrating your on-premises production database to the cloud, while keeping test or development environments refreshed and in sync, this on-demand webcast is for you.
Now that Oracle has deprecated Streams, Oracle Database Advanced Replication and Change Data Capture in Oracle Database 12c, they want you to buy Oracle GoldenGate. But this replacement is extremely expensive and leaves you vulnerable to downtime.
What if you could replace Streams with an affordable alternative that doesn’t expose you to risk? With SharePlex® data replication, you get even more functionality to avoid downtime and data loss than GoldenGate provides – all for a fraction of the price. See how you can achieve high availability, improve database performance and more with a more powerful and cost-effective replacement for Streams.
As your organization works to lower costs, one initiative can make a major difference: a database migration. Migration to hardware that is less expensive to purchase and operate, migration to less costly operating systems and migration away from older versions of Oracle that may require expensive extended support contracts can save you a lot of money. But without the right approach, migrations can cause costly downtime and data loss.
This e-book examines the four most common roadblocks posed by data preparation, with potential solutions for overcoming them so that business analysts can spend more time on data analysis. Readers will take away insights into overcoming the roadblocks in their own organization.
In this on-demand webcast, we discuss why Toad® Data Point is the “perfect” replacement for Brio and demonstrate the Toad Data Point Workbook interface, which was developed with the help of long-time Brio users. “Perfect” because that’s exactly how a few Brio users have described it to us.
Accurate data is imperative for an organization to conduct cost effective decision making, marketing promotions, mailings, database bloat impacting performance, storage and more. Like everything else, change is constant for your data. There is a need to cleanse and validate data when received and on a regular basis to not waste resources. Unfortunately, cleansing and validating data is difficult with the native SQL Server toolset. T-SQL, Integration Services, PowerShell and .NET all include a framework to validate strings, but significant programming logic is necessary. These technologies will not completely validate, cleanse, match and de-duplicate data. How do we leverage the SQL Server tool set to achieve these goals?
Melissa has a variety of tools available to clean, validate and enhance the Contact dimension in your SQL Server data warehouse. Specifically, Melissa’s suite of SSIS Data Quality Components can be leveraged for this task. The Melissa SSIS components are plug and play; you simply drag and drop the components onto the Data Flow, configure the component properties, and you are ready to go. There is no coding required.
To successfully make the journey to a data-driven enterprise, businesses are under pressure to extract more value from their data in order to be more competitive, own more market share and drive growth. This means they have to make their data work harder by getting insights faster while improving data integrity and resiliency, leverage automaton to short cycle times and reduce human error, and adhere to data privacy regulations. DataOps opens the path to delivering data through the enterprise as its needed, while maintaining its quality and viability. In this thought leadership paper, we will provide perspectives on the advantages DataOps gives to stakeholders across the enterprise, including database administrators, data analysts, data scientists, and c-level executives.
The data landscape is evolving and growing exponentially, requiring organizations to find new ways to streamline and simplify their database estate management. The traditional methods of database management are manual, slow, complex, and expensive. Managing IT complexity demands significant process overhead for executing even basic tasks, such as adding additional capacity or upgrading software—adding unwanted friction to IT operations and slowing the business. Research shows that half of data management implementations use both on-premises and cloud environments, furthering database sprawl and management complexity.
Delivering high levels of performance is a requirement for IT environments that rely heavily on mission- and business-critical databases. This is especially important in dynamic environments where data growth is constant and continuous accessibility is a requirement.
Managing traditional legacy databases often involves multiple solutions that are expensive, time consuming, and slow, with significant storage and compute requirements. These roadblocks often lead to IT inefficiencies, inconsistent performance, and a cumbersome and expensive database estate.
The critical role of data as fuel for the growing digital economy is elevating data managers, DBAs, and data analysts into key roles within their organizations. In addition, this rapid change calls for a three-pronged approach that consists of expanding the use of more flexible cloud computing strategies, growing the automation of data environments, and increasing the flow of data and collaboration through strategies such as DevOps and DataOps. Download this special report today to better understand the emerging best practices and technologies driving speed and scalability in modern database management.
With financial services organizations under pressure to act quickly, responsibly and accurately to change, data analytics and Business Intelligence (BI) professionals have been instrumental in helping businesses remain resilient and accelerate decision-making.
To understand more about how they’re adapting to the new world of financial services and enabling innovation, Exasol has interviewed and surveyed professionals from financial services organizations in FTSE 100 and Fortune 500 lists to reveal which strategies they are implementing today.
To enable high performance, database teams should implement a core set of industry best practices. This ‘kernel’ of Database DevOps leads to better alignment between database and application teams and increases the throughput of higher quality releases. As a result, software teams can meet customer demands quicker and gain a competitive advantage over organizations where the database is a bottleneck.
This whitepaper describes four key practices for Database teams to implement when planning to follow a DevOps approach, and includes examples of successful customers.
See how Oracle Database 19c and Db2 11.5 compare to one another on cost and benefits for transactional deployments.
Discover how data management vendors stack up in Forrester’s recent wave. Then, dive into strengths like AI, real-time capabilities, and hybrid support.
Oracle, EDB, Move, Migration, Decision, Tips, Workload, Database, PostgreSQL, Postgres, Cloud, IBM, AWS, Microsoft, tools, schema, data, application logic, test, partners
PostgreSQL is an incredibly popular relational database management system. This white paper discusses in detail: skill set needs, setup and configuration, multi-model architecture, DevOps and production.
Learn more about the partnership between IBM and Cloudera and how their solutions can help prepare you for the Journey to AI. Their suite of offerings are described in detail, including: Cloudera Data Platform, Cloudera Data Flow, IBM DataOps, IBM Big Replicate, IBM Db2 Big SQL and many others.
Db2 is powered by AI and built for AI. This eBook explores what that means with an in depth look at the technologies supporting it.
Across industries, organizations are moving more workloads to the cloud. Elasticity is a key reason for this shift. With the cloud, you can scale resources faster, and only pay for the resources you use. Check out this infographic to learn more.
The Institute for Business Value analyzed the qualities of leading businesses related to data management. This infographic analyzes what defines a leader, how leaders are using data management, and what the benefits have been at a company-wide level.
This solution brief for IBM Db2 Warehouse on Cloud covers several of its benefits like elastic scaling on storage and compute, BLU Acceleration’s high-performance, high availability and resilience, built-AI, and multicloud deployments. It also shares use cases related to modernization, IoT, data marts, and data science.
Finding the right cloud data management solution for your business can be difficult due to the number of potential vendors and seemingly similar offerings. Without digging deeper to uncover the details, you run the risk of selecting a solution that can result in exorbitant hidden fees, unmet service level agreements (SLAs) or vendor lock in.
There are two layers to choosing a cloud data management solution. The first is choosing the right cloud with the right pricing structure. The second is a cloud provider with enterprise support ready for multicloud deployments and artificial intelligence (AI).
Multiple data sources, deployments, and uses necessitate that databases be part of an integrated data platform that brings all data together, providing greater context upon which to base analytics and easing access to specific data different roles within the enterprise require.In addition, containerizing database functionalities allows them to be deployed as microservices wherever needed alongside machine learning and data science capabilities. Today, 57% of organizations use containers with 89% expected to by 2021. IBM Db2 databases benefit from containerization through IBM Cloud Pak for Data—an integrated multicloud data platform built on Red Hat OpenShift.
Effectively using and managing information is critical to pursuing new business opportunities, attracting and retaining customers, and streamlining operations. However, these needs create an array of workload challenges and increase demands on underlying IT infrastructure and database systems that are often not up to the task.
The question is, how will you solve for these challenges? Will you allocate more staff to keep up with patches, add-ons and continual tuning required by existing systems, or simply ignore the potential insights that lie in this wealth of new data? Many businesses are facing this challenge head-on by seeking out new solutions that leverage artificial intelligence (AI) as well as multiple capabilities and deployment options from on-premises, public and private clouds to innovate their data infrastructure and business.
The proliferation of data is creating new opportunities for businesses to better understand their customers, their industry and their own operations. But as the various formats, sources and deployments of data grows exponentially, how can businesses optimize this wealth of new data while remaining compatible with existing systems?
Successful AI relies on a number of factors including a large corpus of data, the requisite algorithms, expert data scientists with appropriate skills, and appropriate compute resources. The latter means not just physical and virtual server infrastructure, but also data management and database software designed to support high-performance data processing and analytics.
Data management is a critical enabler of machine learning projects because it helps overcome challenges such as accessing and preparing data, which can be a significant barrier to success. The results of 451 Research’s Voice of the Enterprise: AI & Machine Learning survey, conducted with people directly involved in AI and ML initiatives, illustrates the point: 33% of respondents cited accessing and preparing data as a barrier the use of machine learning, and 15% cited it as the most significant barrier.
Kafka performance relies on implementing continuous intelligence and real-time analytics. It is important to be able to ingest, check the data, and make timely business decisions.
Autoscaling is the process of automatically increasing or decreasing the computational resources delivered to a cloud workload based on need. This typically means adding or reducing active servers (instances) that are leveraged against your workload within an infrastructure. The promise of autoscaling is that workloads should get exactly the cloud computational resources they require at any given time, and you only pay for the server resources you need, when you need them. Autoscaling provides the elasticity that customers require for their big data workloads, but it can also lead to exorbitant runaway waste and cost.
Observability is an extremely popular topic these days. What's driving this interest? Why is observability needed? What is the difference between observability and monitoring?
Whether today’s organizations sink or swim in data depends on their ability to transform boundless data streams into timely, contextual insights relevant to their business operations. Continuous intelligence is the next big evolution in software architecture that helps organizations accelerate their digital transformation while contending with the inundation of data.
Voice technology is transforming customer experiences. It’s now more important than ever to be able to understand customers as they vocalize their wants, needs and preferences and expect their desired outcome in an instant.
Once a novelty, voice-enabled assistants are now an everyday presence in our lives – whether they reside in the operating system of a mobile phone, the dashboard of a vehicle, or in the intricate wiring of a smart speaker. Not only are they more commonplace, they are increasingly expected to work beyond the basics. This guide provides an overview of how voice assistants are designed, trained and built, in order to enrich customer experiences and bring a competitive edge to the businesses that create them.
If a SaaS company cannot deliver fast analytics to thousands of users simultaneously through cloud applications, the business is in trouble. Companies must be able to deliver high-quality, fast insights to customers. That requires an underlying data warehouse and data analytics platform that can keep up—which can be hard with existing solutions. Too often, companies must limit the amount of data analyzed to deliver acceptable response times, such as limiting analytics to a few months of data instead of a year or more.
Existing data warehouse solutions were not designed for today’s SaaS companies. But a hybrid cloud data warehouse is, by providing sub second queries across petabytes of data. Learn more about how Yellowbrick is designed for SaaS companies in this eBook.
Data Lakes are effective low-cost repositories for vast amounts of data. But when it comes to delivering business insights, data lakes are slow and provide unreliable analytics at scale.
Are you or your business users struggling with getting insights quickly from your data lake? Learn how you can save hours of wasted time digging into your data lake with Yellowbrick.
There’s a wide range of reasons why many organizations are deciding to modernize their data architectures. But they all agree on one thing: by using data more effectively, more widely, and more deeply, they can improve and optimize business and decision-making processes that will help them stay competitive in the emerging digital economy.
There is no doubt that cloud computing has become mainstream and organizations are strategically utilizing it to modernize their applications for a data driven architecture. With this maturity, the digital transformation and cloud adoption has become a lot more manageable than ever before. New trends are emerging to support a variety of use cases in hybrid and multi-cloud environments.
Organizations continue to struggle with integrating data quickly enough to support the needs of business stakeholders, who need integrated data faster and faster with each passing day. Traditional data integration technologies have not been able to solve the fundamental problem, as they deliver data in scheduled batches, and cannot support many of today’s rich and complex data types.
Within recent years, the ability and need to develop flexible and scalable high-performance environments has radically changed the methods and processes for reporting, business intelligence, and analytics. As organizations opt for cloud computing platforms and migrate their data and applications to a hybrid cloud environment, they utilize multiple platforms to support new and increasing data types for analytics.
Mainframe computers deliver mission-critical applications with strong performance, reliability and security. But unlocking insights comes at a cost. Are you looking to extract more value from your mainframe data – while offloading
processing to less expensive platforms, especially to the cloud?
Modern data analytics have the potential to reinvent your business. But to take advantage, IT has to reinvent how they move, store and process data. And integration is a big challenge.
Unternehmen erzeugen jeden Tag riesige Mengen verschiedenster Daten und Dokumente, die querbeet in den unterschiedlichsten Systemen abgelegt werden. Benötigte Informationen werden so von den Anwendern häufig nur mit großem Aufwand gefunden.
If you’re going to stay ahead of the curve and your own schedule, you’ll need to revolutionize finance processes in FP&A, accounting, tax, and audit teams with data science and analytics.
What drives digital transformation success? In “The Essential Guide to Analytic Process Automation,” discover how the convergence of analytics, data science, and process automation is accelerating successful digital transformation and fueling business outcomes.
In this whitepaper, Eckerson Group discusses how to get maximum value from data lakes and how Qlik’s Data Integration Platform helps businesses get the most value out of their data lakes quickly, accurately, and with the agility to respond to shifting business needs.
See how Guardium Insights gives security professionals the ability to quickly create data security and audit reports, monitor activity in on-premises and DBaaS sources, and take action from a central location.
Cloud-based technologies help increase agility, competitiveness and innovation, but can also add complexity, limited visibility and slow reporting, allowing threats and vulnerabilities to go undetected. Learn how Guardium Data Protection can help provide the data security your business needs to thrive by discovering and classifying your data, simplifying compliance, monitoring user activity and detecting threats.
Is your data security practice all that it should be? This ebook looks at five of the most prevalent and avoidable data security missteps organizations are making today, and how these "common pitfalls" can result in potentially disastrous attacks.
With ransomware attacks and data breaches on the rise, it is critical that today’s business leaders take action to ensure that their most critical data is protected. Data encryption should be the first and last line of defense—encoding your sensitive data and rendering it unusable in the event of a data breach.
IBM Security Guardium Data Encryption and IBM Security Guardium Key Lifecycle Manager can help protect your data no matter where it resides, on-premises and across cloud environments, in applications, containers and Teradata environments. Our best-in-class solutions allow you to encrypt and tokenize your data; create, rotate and manage all of your encryption keys; and manage user access policies.
Businesses are embracing hybrid multicloud-based deployment models in order to gain agility and drive their organizations forward. But such a deployment can increase the attack surface, potentially resulting in a host of new data security and compliance challenges.
Learn how IBM Security Guardium—with broad visibility and monitoring, actionable insights and remediation controls—can help you take a smarter, integrated approach to safeguarding critical data across hybrid, multicloud environments.
This whitepaper highlights how organizations can adopt a next generation data security strategy that bridges security technologies deployed across heterogeneous and highly distributed environments. Organizations are lacking a centralized view of their data security risk posture, compounded by the complexity of managing security across distributed environments. This lack of visibility results in an ineffective way of prioritizing alerts and assessing the business impact of lost or stolen data assets. Taking a holistic approach affords organizations a comprehensive view of existing security risks to sensitive data.
As firms face a growing list of data protection regulations and customers become more knowledgeable about their privacy rights, developing a data privacy competence has never been more important. Sustained compliance delivers a number of benefits, but firms with reactive and siloed privacy tactics will fail to capitalize on them. Forrester Consulting evaluates the state of enterprises’ data privacy compliance in a shifting regulatory landscape by surveying global enterprise decision makers with responsibility over privacy or data protection. This report analyzes how they are evolving to meet the heightened data protection and privacy expectations of legislators and consumers, along with the benefits they can expect from a holistic data privacy approach.
This Leadership Compass from analyst firm KuppingerCole provides an overview of the market for database and big data security solutions along with guidance and recommendations for finding the sensitive data protection products that best meet client’s requirements. The report examines a broad range of technologies, vendor product and service functionality, relative market shares, and innovative approaches to implementing consistent and comprehensive data protection across the enterprise.
The Forrester Wave™: Data Security Portfolio Vendors, Q2 2019, is a key industry report for helping security and risk professionals understand and assess the data security solution landscape, and how these solutions can address their business and technology challenges. Download the report to learn why Forrester believes IBM Security Guardium is a good fit “for buyers seeking to centrally reduce and manage data risks across disparate database environments”. The IBM Security Guardium portfolio empowers organizations to meet critical data security needs by delivering comprehensive visibility, actionable insights and real-time controls throughout the data protection journey.
There are constant challenges in the payments industry ranging from preventing fraud, verifying identities, and decreasing risk. Other items to grapple with include scaling a solution to deal with rapid growth and meeting strict SLAs while, of course, containing costs. Plus, to remain competitive, new solutions will require development, such as those that deliver value from customer payment data.
The global payments industry is being disrupted by digitalization. A confluence of trends in technology, business, global regulations, and consumer behavior is redefining how payment transactions are executed. The industry is witnessing rapid innovation growth across the value chain, not to mention disintermediation and fragmentation.
A strong data management foundation is essential for effectively scaling AI and machine learning programs to evolve into a core competence of the business. Download this special report for the key steps to success.
Download the GSMA White Paper and discover how you can overcome the challenge of siloed data and embrace machine learning capabilities to stay ahead of competition.
Data lakes have become the primary place where business data lands. That’s because they give organizations a single, consolidated repository for all data and data formats, so powerful business insights can be efficiently mined, understood and acted upon.
No longer do you have to move data from cloud data lake storage into proprietary data warehouses—or create cubes, aggregation tables or BI extracts—in order to perform BI or data science analytics upon it.
Now there’s a way to eliminate that data pipeline complexity, and enable both BI users and data scientists to easily search, curate, accelerate and share datasets on their own.
By doing so, you can empower any data consumer in your company to self-serve accurate answers to their most pressing business questions directly from data residing in cloud data lake storage.
IT budgets are under pressure due to economic uncertainty, forcing technology leaders to find ways to accomplish more with less. With data and technology at the heart of the business, it is not possible to simply shut down cloud migrations and data analytics projects. Furthermore, in some verticals such as financial and health services, higher volatility and volume is leading to increasing amounts of data that needs to be processed and analyzed.
In this paper we look at three popular architecture choices for cloud data analytics, then describe how Dremio can help you accelerate projects and productivity at a fraction of the cost of cloud data warehouses and simple query engines.
Sind Sie auf der Suche nach einer einfachen Lösung, mit der Sie Ihre SAP-Daten optimal nutzen können? Mit Qlik können Sie SAP-Daten zur Modernisierung Ihres Data Warehouses oder Data Lakes nutzen und in Ihre Cloud integrieren. SAP-Testdaten lassen sich einfacher managen und Ihre Analysemöglichkeiten werden erweitert.
Avez-vous besoin d'une solution simple pour mieux exploiter vos investissements dans les données SAP ? Avec Qlik, vous pouvez exploiter vos données SAP pour moderniser votre Data Warehouse ou votre Data Lake, réaliser l'intégration à votre Cloud, gérer les données de test et augmenter vos capacités d'analyse.
Reducing costs and boosting agility is a huge priority for organizations when considering upgrading, modernizing, re-homing, or re-platforming existing Oracle database workloads. Get started on the right track with Pythian’s Oracle Database Estate Planning Guide.
Do you need an easy solution to help you get more out of your SAP data investments? With Qlik, you can leverage your SAP data to modernize your data warehouse or lake, integrate with your cloud, manage test data, and boost your analytics capabilities.
Understanding your query performance is critical to discerning the performance of your application. To many outsiders, databases can seem like a black box of performance information. But knowing how the database engine stores performance information—and how to interpret this data—allows you to grasp where the bottlenecks are in your database systems. And by comprehending the metrics inside the database engine, you can better focus your tuning efforts.
MySQL continues to gain popularity as the data platform for many critical business applications. As the most widely used open-source database, MySQL provides database services for many on-premises and cloudbased applications. MySQL performance is critical. Performance tuning MySQL depends on a number of factors, and businesses are always looking for ways to tweak the database to gain added performance. Some of the tips here are basic and some are more technical, but all need to be considered and implemented for optimal MySQL database performance.
This whitepaper will explore ways to reduce the risk of code deployment from a database perspective. We’ll cover some of the main challenges to deploying new open-source database code and the issues your development team must understand to mitigate the risk of new code deployment. Paying attention to these database deployment issues can make your deployment process more efficient, allowing you to deploy code with confidence.
When deploying changes to your database schema or indexes, performance is always top of mind. Being able to quickly analyze changes in performance in near real time can let you comfortably build as frequently as you’d like.
Database Performance Monitor (DPM) provides deep database performance monitoring at scale, without overhead. Our SaaS-based platform helps increase system performance, team efficiency, and infrastructure cost savings by offering full visibility into major open-source databases including MySQL, PostgreSQL, MongoDB, Amazon Aurora, and Redis.
Exploding data volumes and performance requirements can lead to growing pains with open source databases such as MySQL and MongoDB. Download this white paper to learn why storage – often an afterthought in smaller-footprint, open source projects – needs to be addressed on an enterprise level and how a modern storage environment can maximize your performance and scalability.
Hadoop is a popular enabler for big data. But with data volumes growing exponentially, analytics have become restricted and painfully slow, requiring arduous data preparation. Often, querying weeks, months, or years of data is simply infeasible, and organizations succeed in analyzing only a fraction of their data.
Today’s enterprises rely on an assortment of platforms and environments, from on-premise systems to clouds, hybrid clouds and multi-clouds. This calls for modern data management practices that leverage emerging technologies, providing enterprise decision managers with the tools and insights they need to improve and transform their businesses. Download this special report for best practices in moving to modern data management standards to ensure the integration and governance of valuable data sources within today’s diverse environments.
Company survival depends on understanding the market better than the competition and acting more quickly. Being able to quickly process, understand, and make profitable decisions from all your data is key to being successful.
Are you under pressure to deploy high-quality code but lack the time, CI/CD tools and training to test it? According to our recent survey, the majority of your peers can relate! That’s why we added powerful PL/SQL unit testing to every new edition of Toad® for Oracle using the utPLSQL framework. In this on-demand webcast, you’ll learn how Toad makes it unbelievably fast and easy to proactively improve the quality of code entering your CI/CD pipeline.
Data visualization, dashboards and predictive data science are only as good as the data you start with.
CDWs (Cloud data warehouse) increase the value of data, while DSaaS (Data security as a service) reduces the attendant risks. Using both together enables organizations to improve privacy and compliance while taking full advantage of
the portability, scalability, innovation, and speed of the cloud. Whether you’re responsible for implementing a security solution or not, you still play a part in limiting the risk to your organization’s most valuable asset: its data. Because applications like cloud data warehouses are so easy to set up, convenience often overshadows security. With DSaaS, you finally get both.
A data and analytics platform strategy can balance the benefits of centralized management with distributed agility while enabling users with analytic capabilities to tackle business initiatives. This research paper identifies four specific challenges and provides recommendations for evolving the strategy, architecture, and enablement process for data and analytics in the enterprise.
Insurance industry success is driven by how fast and accurately a company turns large, complex data sets into profitable business decisions.
A hybrid cloud approach helps protect your insurance company’s unique analytics investments and existing infrastructure while moving toward a more flexible, cloud-based future.
Retail industry success is driven by volume and speed, yet most existing retail systems are not able to keep up. Learn how to modernize your data warehouse to get the scale and speed you need to keep your customers satisfied and your business profitable.
Banks and financial services firms are more dependent than ever on effective and efficient data analytics. Learn how to modernize your data warehouse and turn large, complex data sets into profitable business decisions.
Telecommunications companies face critical pressure points that require more data and data analytics than ever before. With competitive pricing pressures, subscriber growth issues and 5G network buildouts, it is more important than ever to ensure your telecom company can turn large, complex data sets into profitable business decisions.
The explosion in data, the vast array of new capabilities, and the dramatic increase in demands have changed how data needs to be moved, stored, processed and analyzed. But new architectures like data warehouses and lakes are creating additional bottlenecks within IT, because many existing processes are labor-intensive and insufficient.
Modern cloud architectures combine three essentials: the power of data warehousing, flexibility of Big Data platforms, and elasticity of cloud at a fraction of the cost to traditional solution users.
But which solution is the right one for you and your business? Download the eBook to see a side-by-side comparison.
As you make the decision to move your data warehouse from on-premise to the cloud or cloud to cloud, there are many things to take into consideration. Find out how a cloud data warehouse in Azure has advantages in cost, time to value, and the ability to work with real-time data across the organization for analytics.
Today’s organizations are starting to think of data as the "new water” – not just a valuable asset, but instead an essential ingredient for survival. In the IDC Infobrief, Data as the New Water: The Importance of Investing in Data and Analytics Pipelines, you can read the highlights.
Now more than ever, data is moving to the cloud, where data warehousing has been modernized and reinvented. The result is an explosion in adoption. And for Snowflake users, Qlik offers an end-to-end data integration solution that delivers rapid time-to-insight.
To adapt – and succeed – in today’s rapidly evolving digital landscape, enterprises have adopted new data architectures, including data lakes. But despite the investment, the insights still aren’t coming quickly enough – because traditional integration processes just can’t meet the demand.
Learn how one customer streamlined their Oracle licensing and saved $220k annually.
Fully unlocking the value of your data and streaming analytics on Azure to deliver meaningful insights means developing a plan for managing, optimizing, securing, and scaling data to meet the unique requirements of your business.
In this webinar, Jeremy Frye and Dan King, Navisite’s data analytics experts, will provide a roadmap to delivering Azure data analytics quickly and efficiently within your organization.
Join our webinar as we:
Outline the state of typical environments relative to data analytics capabilities
Review the underlying Azure tools and technologies that can support your strategy
Share a practical roadmap for leveraging Azure enhancements and advanced analytics
Explain how Azure Data Analytics Services can support your business
Emerging agile technologies and techniques are leading to new ways of accessing and employing data. At the same time, the increasing complexity of these environments is creating additional challenges around security and governance, and orchestration and monitoring, which is particularly evident with the rise of hybrid, multi-cloud enterprise environments. Welcome to the era of the digitally enriched platform. Download this special report today to dive into emerging technologies and best practices.
By now, the benefits of cloud computing are undeniable. There’s flexibility, scale and global reach. Another promise of the cloud is cost optimization, but many organizations haven’t been able to fully realize these benefits yet. In this video, Mike Gallo, Navisite’s senior director of cloud professional services, shares six areas to look at to start saving cloud costs.
Watch this video to learn:
The single biggest area for cost savings
Reigning in unchecked storage growth
Cloud resources not in use, automation and more
By now, the benefits of cloud computing are well known in terms of costs savings, scalability and flexibility. The question is not whether to move to the cloud, but how to determine which cloud provider is the best fit. To help make the right choice, learn about six key factors to consider in your decision-making process. Ultimately, this is not about eliminating providers that are wrong, but rather, identifying the right path to the cloud for your business.
Download this eBook to learn:
The importance of past experiences
How interoperability and refactoring play a role
Advice around security, compliance and pricing
In this webinar, Tom Camarro, Principle Consultant at Navisite addresses the main challenges Oracle users are facing, and he will highlight the financial and technical benefits of moving off Oracle.
Watch our webinar to learn about improving your environment with:
Initial cost savings - removing of the cost of licensing and support
Right-sized, not oversized – ensuring you don’t pay more than you need to
Speed to deploy - spinning up resources in the cloud in minutes
Enterprises of all sizes have been moving critical IT applications, databases and infrastructure to the cloud for years. However, there is still a great deal of confusion when it comes to running systems in the cloud. Learn the truth behind the seven most common myths of running databases in the cloud.
This eBook will explore:
Top cloud misunderstandings that can derail IT teams
Hidden fees and cost concerns
The reality around what’s involved with time, labor and tools
It’s easy to think of cloud adoption as a one-time event, but realistically, for most enterprises, this is an incremental and iterative process. So what happens on Day 2? Watch this 451 Research presentation today to understand the challenges that can arise and set realistic expectations.
Learn how to grow capacity without adding or provisioning servers by taking advantage of automation and performance optimization solutions to stop manual tuning and recapture wasted capacity and eliminating inefficiencies and bottlenecks . Watch today.
In this on-demand webinar, Matthew Lang, Customer Success Director – Americas, outlines some of the considerations to take when planning a move to Cloud, including the pros and cons of single cloud region vs multi-region cloud deployments. Matt further discusses hybrid and multi-cloud MySQL applications and how to best deploy them; and how Tungsten Clustering’s MySQL Primary/DR Geo-Cluster and MySQL Active/Active Geo-Cluster topologies are the ideal go-to solutions for any organisation looking for high availability MySQL.
During this webcast, you’ll hear directly from IBM executives and Netezza experts focusing on the improvements to your business that you can achieve by modernizing Netezza in a multicloud world, as well as the benefits of Cloud Pak for Data, a fully integrated data and AI platform.
Netezza Performance Server is an all-new cloud-native data analytics and warehousing system designed for deep analysis of complex data volumes scaling into petabytes. Designed for speed, simplicity and agility, Netezza Performance Server is 100% compatible with earlier Netezza appliances. Upgrade today with a single command - nz_migrate Anywhere: on-premise, on-cloud or hybrid.
Performance tuning can be complex. It's often hard to know which knob to turn or button to press to get the biggest performance boost.
Oracle 19c brings us a very powerful new feature for index management called 'Automatic Indexes'. Automatic indexing requires very little effort from a DBA as it automates the creation, tests the validity and performance of indexes, removes obsolete or redundant ones, and continuously monitors their usage.
You've been hearing about all these robots that are coming to take your job. They're going to automate all the SQL performance tuning and make your life way easier, right? Or harder, I guess...since you'll be looking for a job. Thing is, most of that is just plain old marketing hype that Microsoft is using, trying to sell your management on upgrading to newer versions or moving to the cloud. In this on demand session, Brent Ozar will blow the marketing smoke away, show you these features in action, and show you which ones are ready for prime time. You'll walk away better equipped to have conversations with management about why you should (or shouldn't) upgrade, and how you can use these features not just to stay employed, but have a better career.
For years, you've heard that you're supposed to reorganize your indexes to make SQL Server go faster. It sounds like it makes sense - keep things in order, right? But you keep doing it, and SQL Server isn't getting any faster. You've even heard that setting fill factor will help prevent fragmentation, and you're doing that too - but your indexes still keep getting fragmented every day, and users aren't happy with SQL performance. This advice made a lot of sense at the turn of the century, but today, things are different - and we're not just talking solid state drives. In just the first 15 minutes, you'll have a series of ah-ha moments when you realize that your daily index maintenance jobs might just be making the problem worse instead of better. Then, you'll learn what you need to do instead.
Query tuning is key to peak performance in SQL Server databases. However, lots of developers and DBAs constantly struggle to pinpoint the root cause of performance issues and spend way too much time trying to fix them. In this on demand session, I will share my tried and true best practices for tuning SQL statements and other issues by utilizing Wait Time Analysis, reviewing execution plans and using SQL diagramming techniques. In addition, I’ll go over several case studies to demonstrate these best practices.
Every new release of SQL Server brings a load of new features you can add to your database management arsenal to increase efficiency. SQL Server 2019 has introduced many new features and Pinal Dave of SQL Authority will show you how to maximize them in this special session.
Industry leaders have embraced DevOps as a guiding philosophy for ensuring a fast flow of features to the business. They’ve constructed toolchains that automate everything from continuous integration to configuration management, with teams provisioning infrastructure and code in just minutes. This level of speed and automation has propagated to every other key part of the software development lifecycle—except for data.
Enterprises are investing in a wide range of business and technology initiatives to accelerate their digital transformation that address ever-changing customer needs and market dynamics while staying ahead of their competition.
These investments lead to eliminating manual processes, acquiring new tools, and training teams that focus on accelerating innovation initiatives and minimizing data risk in non-production environments.
Speed is a critical business imperative for all organizations, regardless of industry. The pace at which enterprises can bring new products and services to market determines their ability to differentiate from competitors and retain market share. Applications are at the center of this race, and as enterprises look to accelerate innovation, they need to build out a more agile application infrastructure—and that includes a robust and comprehensive test data management (TDM) strategy. Once viewed as a back office function, TDM is now a critical business enabler for enterprise agility, security, and cost efficiency.
With the growing understanding that application release speed has a direct relationship with revenue, businesses across all industries are embracing DevOps as a guiding philosophy for fast application development. They’ve constructed toolchains that integrate everything from codebase versioning to configuration management, with software pipelines automating the provisioning, configuration, and deployment of infrastructure and code in mere minutes. The goal? A state of continuous integration and continuous delivery (CI/CD) in which build/test cycles can be shortened so that high-quality releases are quickly delivered to the business. Organizations see CI/CD as a means to achieving key objectives of a DevOps practice.
The value of operational governance in Office 365 and Microsoft Teams derives from allowing users to access the powerful features of Groups, while also having mechanisms for keeping risks in check.
Learn how to develop the most effective Microsoft 365 backup strategy for your organization.
Retails and consumer packaged goods (CPG) organizations around the world are being challenged with changing consumer demand, supply chain disruptions and more. Overcome these uncertainties by harnessing your business data, and using advanced analytics with the help of Cognizant and Microsoft Azure Synapse to modernize your data platform. Discover how you can adjust marketing outreach, enable more precise inventory management, and enhance customer satisfaction today.
With businesses facing economic uncertainty, the potential of AI at scale is no longer a goal. It is an essential business priority. This is why Avanade and Microsoft have teamed up to power advanced analytics with Azure Synapse. Learn the four questions you should ask yourself to uncover the value of your data at scale.
More and more companies are adopting cloud-native strategies to deliver the innovative real-time experiences that today’s online customers demand. Not surprisingly, this massive infrastructure shift to the cloud is also driving big changes at the application layer. Applications are increasingly moving from monolithic architectures to highly distributed microservices architectures to make software releases faster and make operations more nimble. These developments are putting a ton of pressure on the data layer, which must stretch to meet the new requirements of the modern cloud-native world. Download this white paper to learn how to unlock the cloud-native data layer.
AIOps market is set to be worth $11B by 2023 according to MarketsandMarkets. Originally started as automating the IT operations tasks, now AIOps has moved beyond the rudimentary RPA, event consolidation, noise reduction use cases into mainstream use cases such as root causes analysis, service ticket analytics, anomaly detection, demand forecasting, and capacity planning. Join this session with Andy Thurai, Chief Strategist at the Field CTO ( thefieldcto.com) to learn more about how AIOps solutions can help the digital business to run smoothly.
A challenge of ML is operationalizing the data volume, performance, and maintenance. In this session, Rashmi Gupta explains how to use tools for orchestration and version control to streamline datasets. She also discusses how to secure data to ensure that production control access is streamlined for testing.
As market conditions rapidly evolve, DataOps can help companies produce robust and accurate analytics to power the strategic decision-making needed to sustain a competitive advantage. Chris Bergh shares why, now more than ever, data teams need to focus on operations, not the next feature. He also provides practical tips on how to get your DataOps program up and running quickly today.
Traditional methodologies for handling data projects are too slow to handle the teams working with the technology. The DataOps Manifesto was created as a response, borrowing from the Agile Manifesto. This talk covers the principles of the DataOps Manifesto, the challenges that led to it, and how and where it's already being applied.
The ability to quickly act on information to solve problems or create value has long been the goal of many businesses. However, it was not until recently that new technologies emerged to address the speed and scalability requirements of real-time analytics, both technically and cost-effectively. Attend this session to learn about the latest technologies and real-world strategies for success.
Each week, 275 million people shop at Walmart, generating interaction and transaction data. Learn how the company's customer backbone team enables extraction, transformation, and storage of customer data to be served to other teams. At 5 billion events per day, the Kafka Streams cluster processes events from various channels and maintains a uniform identity of each customer.
To support ubiquitous AI, a Knowledge Graph system will have to fuse and integrate data, not just in representation, but in context (ontologies, metadata, domain knowledge, terminology systems), and time (temporal relationships between components of data). Building from ‘Entities’ (e.g. Customers, Patients, Bill of Materials) requires a new data model approach that unifies typical enterprise data with knowledge bases such as industry terms and other domain knowledge.
We are at the juncture of a major shift in how we represent and manage data in the enterprise. Conventional data management capabilities are ill equipped to handle the increasingly challenging data demands of the future. This is especially true when data elements are dispersed across multiple lines of business organizations or sourced from external sites containing unstructured content. Knowledge Graph Technology has emerged as a viable production ready capability to elevate the state of the art of data management. Knowledge Graph can remediate these challenges and open up new realms of opportunities not possible before with legacy technologies.
Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast amounts of interconnected data. The promise of graph technology has been there for a decade. However, the scale, performance, and analytics capabilities of AnzoGraph DB, a graph database, is a key catalyst in Knowledge Graph adoption.
Though MongoDB is capable of incredible performance, it requires mastery of design to achieve such optimization. This presentation covers the practical approaches to optimization and configuration for the best performance. Padmesh Kankipati presents a brief overview of the new features in MongoDB, such as ACID transaction compliance, and then move on to application design best practices for indexing, aggregation, schema design, data distribution, data balancing, and query and RAID optimization. Other areas of focus include tips to implement fault-tolerant applications while managing data growth, practical recommendations for architectural considerations to achieve high performance on large volumes of data, and the best deployment configurations for MongoDB clusters on cloud platforms.
Just as in real estate, hybrid cloud performance is all about location. Data needs to be accessible from both on-premise and cloud-based applications. Since cloud vendors charge for data movement, customers need to understand and control that movement. Also, there may be performance or security implications around moving data to or from the cloud. This presentation covers these and other reasons that make it critical to consider the location of your data when using a hybrid cloud approach.
What if your business could take advantage of the most advanced AI platform without the huge upfront time and investment inherent in building an internal data scientist team? Google’s Ning looks at end-to-end solutions from ingest, process, store, analytics, and prediction with innovative cloud services. Knowing the options and criteria can really accelerate the organization's AI journey in a quicker time frame and without significant investment.
After 140+ years of acquiring, processing and managing data across multiple business units and multiple technology platforms, Prudential wanted to establish an enterprise wide data fabric architecture to allow data to be available where and when its needed. Prudential chose data virtualization technology to create the logical data fabric that spans their entire enterprise.
The pace of technology change is continuing to accelerate and organizations have no shortage of tool and application options. But while many are modernizing tool infrastructure and ripping out legacy systems, the data that powers new tools still presents difficult and seemingly intractable problems. Seth Earley discusses approaches for bridging the gap between a modernized application infrastructure and ensuring that quality data is available for that infrastructure.
As business models become more software driven, the challenge of maintaining reliable digital services and delightful customer experiences, as well as keeping those services and customer data safe is a "continuous" practice. It’s particularly important now, when the COVID-19 global pandemic has created a discontinuity in digital transformation and many industries have been forced entirely into a digital business model due to social distancing requirements. Bruno Kurtic discusses the impact of the pandemic on industries and digital enterprises leverage continuous intelligence to transform how they build, run, and secure their digital services and use continuous intelligence to outmaneuver their competition.
In this session, Lee Rainie discusses public attitudes about data, machine learning, privacy, and the role of technology companies in society—including in the midst of COVID-19 outbreak. He covers how these issues will be factors shaping the next stages of the analytics revolution as politicians, regulators, and civic actors start to focus their sights on data and its use.
Is data helping you react to change and drive actionable insights or is locked away in silos? TCS and Microsoft solve this with Azure Synapse. Discover how you can industrialize your data in Azure and gain instant business clarity.
As long as databases continue to evolve, so too will our role as a DBA. There’s nothing wrong with plugging away at the same DBA duties you’ve known all these years. But eventually trends like DevOps, multi-platform databases and the cloud will cause those duties to change. The sooner you can identify and pursue the opportunities each trend brings, the sooner you can move past the zombie stage of database administration and on to the high-value tasks that turn DBAs into true partners in the business.
Many organizations choose open source databases to support a great customer experience. However, most open source databases are built on off-the-shelf CPU-based systems and are highly inefficient at scaling data volumes and performance on their own. Today’s latency-sensitive applications require consistent and predictable high-throughput processing in an architecture that supports the real-time operationalization of data. Download this special white paper to learn how the use of an FPGA-accelerated database engine can supercharge the performance of your open sources databases to meet increasing scalability and performance demands.
From the rise of hybrid and multi-cloud architectures, to the impact of machine learning and automation, database professionals today are flush with new challenges and opportunities. Now, more than ever, enterprises need speed, scalability and flexibility to compete in today’s business landscape. At the same time, database environments continue to increase in size and complexity; crossing over relational and non-relational, transactional and analytical, and on-premises and cloud sites. Download this report to dive into key enabling technologies and evolving best practices today.
This GigaOm Radar report examines and evaluates the most important data warehouse platforms in the market today. It looks at each vendor’s approach, capabilities and, crucially, its ongoing development, and explores how each is poised to evolve over the next twelve months.
This report is designed to help you evaluate both the current and future position of solutions within the market. The aim is to help your organization make the best possible decision about the vendor it selects for its data warehouse.
According to Forrester, insights-driven businesses are on track to collectively earn $1.8 trillion by 2021. Enabling insights that drive exponential revenue growth requires the shift from being “data aware” to being “data driven.”
But being data-driven requires having insights available throughout the organization. It’s no secret that scaling analytics is difficult — there are any number of ways to go about it, but perhaps none are quite as effective as DevOps, a mindset shift that pairs, rather than separates, the work of developers and implementers. Their combined skills enable faster integration, fewer siloes, and a handy element called automation.
Everyone in business is talking about data science, but what does it really mean? Get past the talk and understand what data science is and how it can impact your business — and get your analysts back home in time for dinner. If you secretly wonder if data science is really a science or some sort of obscure black magic, this is the whitepaper for you. We'll debunk the myths and show you how data science can be used to drive true business decisions and make a positive impact.
Data prep is where time goes to die. If you’re like most analysts, you spend 80% of your day preparing data for basic analysis and reports, and just 20% delivering results that propel your career and the business forward.
Become a data disruptor and differentiate yourself as the innovative analyst you were meant to be.
Now more than ever, data is crucial to informing critical decisions and business growth. However, without analytics, data is just noise. But with analytics, data becomes insight. Learn how a modernized data system powered by AI, machine learning, and cloud-enabled architecture from Insight and Microsoft Azure Synapse can amplify the power of data as strategic currency by providing analytics at blazing speeds and significantly lower costs.
Globally, retailers and consumer packaged goods (CPG) firms are facing unprecedented uncertainty due to changing consumer demand and supply chain disruptions. For these enterprises, the key to navigating large-scale challenges is data modernization: implementing an AI and machine learning-driven, cloud-enabled architecture harnessing the power of advanced analytics. With Cognizant and advanced analytics using Microsoft Azure Synapse, retailers and CPG organizations can adjust marketing outreach, enable more precise inventory management, and enhance customer satisfaction.
In uncertain times, data provides the clarity enterprises need to confidently navigate the rapidly changing economic and business landscapes. An effective data-driven strategy powered by AI and advanced, machine-learning analytics will give you the tools to make informed decisions for your business’s innovation and growth. Learn how a modern data system with the combined power of Insight and Microsoft Azure Synapse can accelerate business outcomes by leveraging best-in-breed data technology solutions backed by expert support.
There's a saying: "Garbage in, garbage out." It’s common knowledge that every machine learning solution needs a good algorithm powering it, but what gets far less press is what actually goes into these algorithms -- the training data itself. Your model is only as good as the data it's trained on. The Essential Guide to Training Data covers everything you need to know about creating the training data necessary to drive successful machine learning projects.
The world has changed dramatically since March 2020. The rapid spread of the novel coronavirus and its resulting illness, COVID-19, have completely altered business, personal, and social life for the immediate future.
To better understand the immediate impact of all these unprecedented changes on businesses and their IT infrastructures, Yellowbrick Data recently surveyed 1000 enterprise IT managers and executives to uncover their infrastructure priorities during this era of economic uncertainty and disruption.
Today, hybrid cloud is viewed not only as the logical and inevitable consequence of an abundance of choice in relation to computing and data storage location options, but also as a strategic imperative that enables enterprises to make the most efficient use of the variety of infrastructure location options.
In this white paper from 451 Research, you’ll learn how hybrid cloud is viewed as a strategic imperative to enable enterprises to make the most efficient use of their data warehouse infrastructure.
In this white paper, you’ll learn how a modern data strategy based on hybrid cloud only supports all of today’s requirements, including superior price/performance regardless of data scale, but also provides a path to the future, with flexible deployment options and expand-as-you-grow architecture.
The status quo approach to data warehousing is out of step with the times: Many enterprises can’t take full advantage of powerful analytic and BI tools and skill sets because their legacy data warehouse is too slow, too expensive to scale, and too difficult to manage. And when real-time decisions are needed—those are insights you can’t afford to lose.
For most organizations, data warehouses are more critical than ever. But all too often, they’re also no longer able to keep up to the task. They are simply too inflexible. They’re too hard to scale. They’re too expensive to scale. They require too many technical resources to manage and update. And they’re too hard to manage in the face of modern requirements such as huge data volumes, growing numbers of users, increasingly complex queries, and real-time data.
Hai bisogno di una soluzione semplice che ti aiuti a ottenere di più dai tuoi investimenti nei dati SAP? Con Qlik, puoi sfruttare i dati SAP per modernizzare il tuo data warehouse o data lake, eseguire l'integrazione con il cloud, gestire i dati dei test e migliorare le tue funzionalità di analytics.
As businesses adapt to change, data is relied upon to provide actionable insights and drive decisions. However, analysis becomes extremely difficult when data is siloed, of low quality, or too disorganized to use. TCS and Microsoft have partnered to solve these issues using Azure solutions, including AI with Azure Synapse. Navigate industry storms with data insights and cloud analytics from TCS and Microsoft.
How do you organize all your data tools and teams into one smooth and efficient process that delivers fast, error-free data? DataOps is the answer. DataOps draws on lessons learned from the software and manufacturing industries to transform your data processes. Read The DataOps Cookbook for practical tips on how to get your DataOps program off the ground, reclaim control of your pipelines, and get back to doing what you love - creating innovative analytics that deliver business value.
Every business today depends on data, making smart data management a required core competency inside every organization. Businesses need to manage data assets with the same discipline and rigor as financial assets, and they need tools that do not require deep technical knowledge.
Bridging boundaries between operations and analytics isn't a new goal for organizations, but the latest whitepaper from Ventana Research explores how to do so with master data management and the role of an Intelligent Data Hub.
Organizations today need to realize the importance of MDM, as complete and accurate data is the lifeblood of successful business operations.
MDM solutions have been instrumental in solving core data quality issues in a traditional way, focusing primarily on simple master data entities such as customer or product. Organizations now face new challenges with broader and deeper data requirements to succeed in their digital transformation.
The growth of IoT adoption has been exponential across all industries, but organizations within each industry face a unique set of challenges along this journey. Enterprises leveraging all the big data generated from IoT devices in their machine learning models are able to use prescriptive and predictive analytics to make well-informed decisions.
Read this ebook to learn about the challenges of implementing data-driven IoT, and solutions for addressing the challenges across multiple industries.
With constantly evolving threats and an ever-increasing array of data privacy laws, understanding where your data is across the enterprise and properly safeguarding it is more important today than ever before. Download this year’s Cybersecurity Sourcebook to learn about the pitfalls to avoid and the key approaches and best practices to embrace when addressing data security, governance, and regulatory compliance.
Migrieren Sie Ihre lokalen Oracle-Datenbanken mit Tools von Quest® zu fu¨hrenden Cloud-Anbietern, um Ausfallzeiten sicher zu minimieren, die Datenintegrita¨t zu garantieren, Kosten zu kontrollieren, die Leistung zu u¨berwachen und zu optimieren und kontinuierliche Replikationen durchzufu¨hren.
Unabha¨ngig davon, ob Sie gerade als DBA in einem neuen Unternehmen begonnen haben oder bereits seit mehreren Jahren im gleichen Unternehmen ta¨tig sind, stehen wir vor der gleichen Herausforderung: Wir verfu¨gen nicht u¨ber optimale Hardware, Software, Workflows oder die entsprechende Organisationskultur, um den reibungslosen Betrieb der Umgebung zu gewa¨hrleisten. Tatsa¨chlich ist sogar ha¨ufig das Gegenteil der Fall: In der Abteilung sind schlechte Sicherheitsprozesse im Einsatz, es fehlen Notfall-Wiederherstellungspla¨ne und die Hardware- und Netzwerkkonfiguration entspricht nicht den Standards. Zudem bestehen kulturelle und organisatorische Herausforderungen durch schlechte Verwaltung, unzureichend ausgestattete Abteilungen oder schlecht durchdachte Prozesse.
Die Zeiten, in denen Unternehmen ganz auf zentrale lokal installierte Oracle oder SQL Server Systeme setzten, sind vorbei. Wenn Sie gerade einen Datenbankverwaltungs- und -u¨berwachungsansatz richtig beherrschen, entscheidet sich das Unternehmen fu¨r Kosteneinsparungen durch die Nutzung von Cloud- und Open-Source-Datenbanken. Als wa¨ren diese massiven Vera¨nderungen noch nicht genug, kommt es außerdem immer mehr zu einer Verschiebung hin zu einer DevOps- Kultur, in der Unternehmen durch die Verku¨rzung von Freigabezyklen ihre Wettbewerbsfa¨higkeit sichern ko¨nnen.
Migrez vos bases de donne´es Oracle sur site vers des fournisseurs de Cloud leaders a` l’aide d’outils Quest® et re´duisez les temps d’arre^t, assurez l’inte´grite´ des donne´es, mai^trisez les cou^ts, surveillez et optimisez les performances, et proce´dez a` la re´plication en continu en toute se´curite´.
L’e´poque de la boutique Oracle ou SQL Server est belle est bien re´volue. Pre´cise´ment au moment ou` vous mai^trisez enfin une approche de gestion et de surveillance des bases de donne´es, votre entreprise de´cide de re´duire les de´penses et de migrer vers les bases de donne´es open source etCloud. Et comme si ces importants changements ne suffisaient pas, l’adoption de la culture DevOps, qui permet aux entreprises de rester concurrentielles tout en acce´le´rant les cycles de livraison se ge´ne´ralise de plus en plus.
Que vous preniez vos fonctions en tant qu’administrateur de bases de donne´es dans une nouvelle entreprise ou que vous soyez en poste dans votre entreprise actuelle depuis plusieurs anne´es, vous e^tes probablement confronte´ au me^me de´fi : vous ne disposez pas force´ment du mate´riel, des logiciels, du workflow ou de la culture ne´cessaires pour maintenir efficacement votre environnement. En ge´ne´ral, c’est tout le contraire : le de´partement peut avoir des pratiques de se´curite´ de´ficientes, des plans de reprise d’activite´ inexistants, une documentation incomple`te et une configuration mate´rielle et re´seau non adapte´e. Viennent ensuite les de´fis culturels ou organisationnels : gestion inefficace, manque de ressources ou processus mal conc¸us.
Para adaptarse y tener éxito en el cambiante contexto digital actual, las empresas han adoptado nuevas arquitecturas de datos, incluidos los data lakes. Sin embargo, a pesar de la inversión, los conocimientos siguen sin llegar con la celeridad necesaria, ya que los procesos de integración tradicionales no pueden satisfacer la demanda.
L'explosion des données, l'immense choix de nouvelles fonctionnalités et l'augmentation spectaculaire des demandes ont modifié la manière dont nous devons déplacer, stocker, traiter et analyser ces données. Cependant, de nouvelles architectures, telles que les Data Warehouses et Data Lakes, donnent naissance à de nouveaux goulets d'étranglement au sein des services informatiques, car de nombreux processus existants s'avèrent insuffisants et nécessitent une main-d'œuvre importante.
Les architectures modernes dans le cloud allient trois éléments essentiels : la puissance du data warehouse, la flexibilité des plateformes de Big Data et l'élasticité du cloud, le tout à une fraction du prix des solutions traditionnelles.
Pour s’adapter - et réussir - à l’évolution rapide du paysage numérique actuel, les entreprises ont adopté de nouvelles architectures de données, y compris les data lakes. Mais malgré l’investissement, les perspectives ne sont toujours pas assez rapides - parce que les processus d’intégration traditionnels ne peuvent tout simplement pas répondre à la demande.
Plus que jamais, les données sont migrées vers le cloud, où le Data Warehouse s'est modernisé et réinventé. C'est pourquoi son adoption est en forte hausse. Pour les utilisateurs de Snowflake, Qlik propose une intégration de données de bout en bout qui accélère le temps d'analyse.
Immer mehr Daten, immer mehr Möglichkeiten und immer mehr Bedarf haben die Art und Weise, wie Daten bewegt, gespeichert, verarbeitet und analysiert werden von Grund auf verändert. Es gibt zwar viele neue Architekturen, doch Data Warehouses und Data Lakes führen oft zu zusätzlichen Engpässen in der IT, weil viele der bestehenden Prozesse arbeitsintensiv und unzureichend sind.
Moderne Cloud-Architekturen zeichnen sich durch drei Merkmale aus: die Leistungsfähigkeit eines Data Warehouse, die Flexibilität von Big-Data-Plattformen und die Elastizität der Cloud, zu einem Bruchteil der Kosten herkömmlicher Lösungen.
Um auf die rasanten Veränderungen der digitalen Welt zu reagieren und erfolgreich in ihr zu arbeiten, setzen Unternehmen auf neue Datenarchitekturen wie Data Lakes. Allerdings führen die Investitionen oft nicht zu den erwünschten schnelleren Erkenntnissen. Herkömmliche Integrationsprozesse sind den damit verbundenen Anforderungen einfach nicht gewachsen.
Immer mehr Daten werden in die Cloud ausgelagert, gleichzeitig wurden die Konzepte des Data Warehousing in den letzten Jahren modernisiert und praktisch neu erfunden. In der Folge haben sich Data Warehouses explosionsartig ausgebreitet. Snowflake-Anwendern bietet Qlik eine End-to-End-Plattform für die Datenintegration, die es ermöglicht, schneller Erkenntnisse zu gewinnen.
Las arquitecturas modernas en la nube combinan tres elementos básicos: el poder del data warehousing, la flexibilidad de las plataformas de Big Data y la elasticidad de la nube por una fracción del precio de las soluciones tradicionales.
La explosión de datos, el amplio abanico de nuevas capacidades y el espectacular aumento de la demanda han cambiado la forma en que los datos deben moverse, almacenarse, procesarse y analizarse. Sin embargo, nuevas arquitecturas como los data warehouses y los data lakes están creando nuevos cuellos de botella en los departamentos de TI, porque muchos procesos requieren mucho trabajo y son insuficientes.
Le moderne architetture cloud combinano tre elementi essenziali: la potenza del data warehousing, la flessibilità delle piattaforme di Big Data e l'elasticità del cloud a una frazione del costo per gli utenti delle soluzioni tradizionali.
L'esplosione dei dati, la vasta gamma di nuove funzionalità e il drastico aumento delle richieste hanno cambiato il modo in cui i dati devono essere spostati, memorizzati, elaborati e analizzati. Tuttavia, nuove architetture come i data warehouse e i data lake stanno creando ulteriori colli di bottiglia all'interno dell'IT, poiché molti processi esistenti sono molto laboriosi e insufficienti.
Per adattarsi e avere successo nell'odierno panorama digitale in rapida evoluzione, le aziende hanno adottato nuove architetture di dati, compresi i data lake. Tuttavia, nonostante l'investimento, ancora le intuizioni non vengono rivelate abbastanza velocemente, perché i processi di integrazione tradizionali non riescono a soddisfare la domanda.
¿Necesita una solución sencilla que le ayude a sacar más provecho de su inversión en datos de SAP? Con Qlik, puede utilizar los datos de SAP para modernizar su data warehouse o data lake, integrarlo todo en la nube, gestionar datos de pruebas y mejorar sus capacidades de analítica.
Is your application easy to monitor in production? Many are, but sadly, some applications are designed with observability as an afterthought.
Today’s organizations want advanced data analytics, AI, and machine learning capabilities that extend well beyond the power of existing infrastructures, so it’s no surprise that data warehouse modernization has become a top priority at many companies. Download this special report to under how to prepare for the future of data warehousing, from increasing impact of cloud and virtualization, to the rise of multi-tier data architectures and streaming data.
Improving data quality is one of the top 50 ways businesses can save money and remain successful during economic downturns. With a bumpy road ahead, now is the perfect time for developers, data architects and data stewards to review the 7 Cs of Data Quality and build a game plan to eliminate poor quality or inconsistent customer data and improve data accessibility and usability.
According to MIT Technology Review, less than 1% of all data is analyzed; however, to be successful in today’s competitive business environment, organizations must aim to make data-driven decisions that drive high value return. Modernizing an organization's information architecture for artificial intelligence (AI) and multicloud has become a business imperative. Cloud-based data management infused with AI capabilities enables businesses to predict and shape future outcomes by simplifying complex queries and discovering previously hidden insights.
63% of companies are already using more than one cloud provider according to ISG’s new report which explores why multicloud IT is rapidly becoming the norm.
SaaS adoption is also rising with 90% of IT leaders suspecting most of their app portfolio will be delivered via a SaaS model by 2021.
Using a sub-optimal solution for your data management workloads can result in slow and missed insights. Offerings that can handle several workloads exist, but as IDC’s recent report notes, often lack the performance of workload-specific or data-type-specific options like streaming data capture systems and data warehouses.
Machine Learning (ML) and Artificial Intelligence are key parts of fueling digital transformation with data. The journey to achieve these capabilities is like a ladder where each component like data collection, organization, and analysis is a rung that strengthens it.
85% of enterprises view Artificial Intelligence as a strategic opportunity, recognizing its ability to turn data into real business value. Yet many don’t have a data foundation capable of rapidly analyzing data in a simple, efficient manner. With the recent explosion of data there needs to be a paradigm shift in how that data is managed and accessed to achieve competitive advantages. Databases must be both powered by AI for greater optimization and built to support AI application development.
Advanced Analytics and Artificial Intelligence (AI) are poised to rapidly transform the economy and society. Applications of these fast-growing technologies enable organizations to predict and shape future outcomes, empower people to do higher value work, automate decisions, processes and experiences, and reimagine new business models.
However, most organizations are stuck in experimentation in silos. Industrializing AI throughout the enterprise is not easy. There are many deployment challenges associated with data, talent and trust especially as data volume, velocity and variety continue to explode.
To amplify the value of AI and make it pervasive, it is imperative that clients consider best practices and solutions that address these challenges holistically across several dimensions: Business, Process, Applications, Data and Infrastructure. Doing so provides clients extensive choice and flexibility to maximize the Total Value (Benefits –Costs) of Ownership (TVO) from their inv
Today’s businesses run on data and the leaders that drive them must embrace forward-looking data science and artificial intelligence (AI) technologies to retain competitive differentiation. They must also reliably support increasingly complex business operations without downtime. Supporting these disparate needs once required a myriad of data platforms, but that is no longer the case.
With version 11.5, IBM Db2® is extending its legacy of dependability by adding AI functionality designed to help optimize performance and support data scientists’ mission to find deeper insights. It is both powered by and built for AI.
As organizations are more likely than ever to be audited by their software vendor, one of the top questions we are asked is, “How at risk is my organization in the event of an Oracle audit?” In this eBook, you will be able to quantify your organization’s Oracle audit risk through traditional risk calculating practices in a risk matrix.
For modern data teams, the question is no longer ‘should we use the cloud’ but instead ‘should we take advantage of multi-cloud?’. In this white paper, we explore the benefits of multi-cloud analytics, from the flexibility to manage data across multiple cloud environments to the freedom from being stuck with a single cloud provider.
Data modeling is an essential discipline for data-informed organizations. Modeled data is much easier for cross-functional teams to consume and use to inform their decisions. In this white paper, we explore the basics of data modeling, explain why data modeling is important and offer some example models you can use to model your data in line with your business logic.
The quality of our data has never been more important. Advanced use cases such as personalization or AI demand complete, accurate data we can rely on. This white paper examines what we mean by ‘high-quality’ data and outlines the steps we can take to collect rich, well-structured data that drives business success.
Tungsten Clustering by Continuent is the only complete, fully-integrated, fully-tested MySQL High Availability, Disaster Recovery and Geo-clustering solution running on-premises and in the cloud combined with industry-best and fastest, 24/7 support for business-critical MySQL, MariaDB, & Percona Server applications. Learn more about it in this product guide.
Vertica maximizes cloud economics for mission-critical big data analytical initiatives. Vertica delivers advanced SQL analytics on massive amounts of data with blazing performance, and elastic scalability for just-in time deployments on major public clouds–AWS, Azure, and Google Cloud Platform.
Vertica is transforming the way organizations build, train and operationalize machine learning models. Are you ready to embrace the power of Big Data and accelerate business outcomes with no limits and no compromises?
Nucleus Research analyzed the results of the return on investment (ROI) case studies from six deployments of Vertica to identify the value returned for each dollar invested in the solution. Many decisions about the purchase of technology are being made by the office of the chief financial officer (CFO) as opposed to the information technology (IT) department due to the direct impact a solution can have on a company’s bottom line. For this reason, it is critical that software offers a significant return on investment. The results of the analysis showed that Vertica customers realized $4.07 for each dollar spent. Nucleus also found that Vertica delivers value quickly with customers’ payback period averaging under one year.
The digital revolution has spawned a rapid expansion of cloud data warehousing. In the cloud, there are two viable options for organizations moving toward analytic dominance: data warehouse as a service (DWaaS) and bring your own license (BYOL) to the cloud.
Business executives today are well aware of the power of data, especially for gaining actionable insight into products and services. How do you jump into the big data analytics game without spending millions on data warehouse solutions you don’t need?
Rapid data collection is creating a tsunami of information inside organizations, leaving data managers searching for the right tools to uncover insights. Knowledge graphs have emerged as a solution that can connect relevant data for specific business purposes. Download this special report to learn how knowledge graphs can act as the foundation of machine learning and AI analytics.
It’s no surprise then that adoption of data lakes continues to rise as data managers seek to develop ways to rapidly capture and store data from a multitude of sources in various formats. However, as the interest in data lakes continues to grow, so will the management challenges. Download this special report for guidelines to building data lakes that deliver the most value to enterprises.
A discussion of how Onsystex's OASYS OMNI and OASYS Bridge components enable the rapid development and integration of new applications and systems by surfacing legacy MultiValue functionality in cloud frameworks such as Microsoft AZURE.
Enterprise customers report that queries are a significant portion of their analytics workloads, and the performance of these workloads is critical to their big data success. Inefficient queries can mean missed SLAs, negative impact on other users, and slow database resources.
Please join Pepperdata CEO, Ash Munshi; Peter Cnudde, former VP of Engineering of Yahoo's Big Data and Machine Learning platforms; Pepperdata Field Engineer, Alex Pierce, for a roundtable Q and A discussion on how to take the guesswork out of migrating to the cloud, and reduce the runaway management costs of a hybrid data center.
Apache Spark is a full-fledged, data engineering toolkit that enables you to operate on large data sets without worrying about the underlying infrastructure. Spark is known for its speed, which is a result of improved implementation of MapReduce that focuses on keeping data in memory instead of persisting data on disk. However, in addition to its great benefits, Spark has its issues including complex deployment and scaling. How best to deal with these and other challenges and maximize the value you are getting from Spark?
While cloud is seen as the go-to environment for modernizing IT strategies and managing ever-increasing volumes of data, it also presents a bewildering array of options. Download this special report for the nine points to consider in preparing for the hybrid and multi-cloud world.
The process of migrating and upgrading hardware, operating systems, databases and software applications has become inextricably linked to risk, downtime and weekends at the office for most DBAs and system administrators who perform them. Want to simplify the migration and upgrade process so you can avoid the risk, downtime and long hours normally associated with it? Read this e-book!
Agile & DevOps are key transformation practices to delivering better software faster, but they do not go far enough. How do you ensure your software delivers value to customers?
Value Stream Management (VSM) helps align and scale Agile & DevOps with the business to deliver customer results. It provides complete visibility into the flow of value across the entire software delivery lifecycle and connected toolchain. You can identify bottlenecks, minimize waste, and uncover areas of improvement to optimize your value stream.
In this eBook, we explore key insights and statistics from Forrester’s research of VSM enterprise adoption. It is based on the study, Holistic Solutions Drive the ‘Value’ in Value Stream Management, which includes survey findings on the challenges, results, and learnings of IT and Business Professionals involved in executing VSM initiatives.
Agile & DevOps are key transformation practices to delivering better software faster, but they do not go far enough. How do you ensure your software delivers value to customers?
Value Stream Management (VSM) helps align and scale Agile & DevOps with the business to deliver customer results. It provides complete visibility into the flow of value across the entire software delivery lifecycle and connected toolchain. You can identify bottlenecks, minimize waste, and uncover areas of improvement to optimize your value stream.
CollabNet VersionOne commissioned Forrester Consulting to research how VSM adoption has helped IT and Business Professionals move the needle on delivering key software objectives.
Sure, there’s some doom and gloom out there about the future of the DBA. But in this session, you’ll learn how to adapt, evolve, survive and even thrive in a changing database world. You’ll get to see real-world statistics on the evolving role of the DBA, common DBA concerns and valuable insights for career success. You’ll learn how the trends of DevOps, cloud, NoSQL, big data and more will shape the future.
Over the last 10 years, major trends in technology have shaped and reshaped the ongoing role of the DBA in many organizations. New data types coupled with emerging applications have led to the growth of non-relational data management systems. Cloud technology has enabled enterprises to move some data off-premises, complicating the overall data infrastructure. And, with the growth of DevOps, many DBAs are more deeply involved with application and database development.
To gain insight into the evolving challenges for DBAs, Quest commissioned Unisphere Research, a division of Information Today, Inc., to survey DBAs and those responsible for the management of the corporate data management infrastructure. Download this special report for insights on the current state of database administration, including new technologies in use and planned for adoption, changing responsibilities and priorities today, and what’s in store for the future.
Foglight SQL PI enables DBAs to address these challenges with visibility into database resources, proactive alerts, advanced workload analytics, change tracking and more. Armed with these tools, DBAs can get a complete picture of their environment to find and fix performance issues before they put the database at risk.
Even though it’s still common practice today, the harsh reality is that using an external cache layer as the fundamental component of a System of Engagement (SoE), where huge scale, ultrafast response, and rock-solid reliability are critical success factors, is a last decade approach for solving next decade problems. Learn the five signs to watch for that show your cache-first data architecture needs an update.
Learn how to modernize data and analytics environments with scalable, efficient and real-time data replication.
Read this whitepaper to learn about the drivers for database streaming for Apache Kafka and how Qlik Replicate™ provides several differentiated advantages for Kafka and streaming architectures, including one to many publication capabilities, automated data type mapping, metadata integration, transactional consistency safeguards and configuration flexibility.
In this 15min presentation, Gilles Rayrat, VP of Engineering at Continuent and database proxies guru, shares some of his knowledge on the world of database proxies, how they work, why they’re important and what to use them for. Starting with a simple database connectivity scenario, Gilles builds up the content by discussing clustered databases and what happens in the case of a failure, all the way through to explaining the important role database proxies play for high availability MySQL; including a more in-depth look into some advanced database connectivity setups and proxies functionalities.
The amount of data available to mid- and enterprise-sized companies continues to grow year over year. Most companies are pulling data from several data sources, making big data intake much more complex. This whitepaper examines the five factors you need to consider when moving from legacy data ingestion platforms to a more modern solution.
Since the 1980s, capacity planners have used Merrill’s Expanded Guide (MXG) by Merrill Consultants to turn raw mainframe System Measurement Facility (SMF) records into detailed reports, insightful graphics and clarifying charts to better understand and manage their mainframe environment. Over the years, MXG has continued to shift much of the burden of day-to-day data management off of capacity planners. As a result, and as SMF record creation evolved and became more detailed, the process of collecting, sorting and storing the huge number of records generated by SMF has, in many cases, become one of the top ten CPU workloads on the mainframe.
Being tied-in to proprietary database software brings its own restrictions, you often have no choice but to payup in order to keep your database running. You might have to settle for sub-standard features, availability, or performance, which would be improved with a different set-up. If you actively choose vendor lock-in, you should be aware of your choices and the trade-offs that occur as a result. It is still desirable to have an exit plan in place, just in case.
DataOps is poised to revolutionize data analytics with its eye on the entire data lifecycle, from data preparation to reporting. Download this special report to understand the key principles of a DataOps strategy, important technology, process and people considerations, and how DataOps is helping organizations improve the continuous movement of data across the enterprise to better leverage it for business outcomes.
Today’s enterprises are looking to data managers to be able to respond to business challenges with scalable and responsive systems that deliver both structured and unstructured data – and accompanying insights – at a moment’s notice, with the ability to respond to any and all queries. What’s needed is a modern data architecture that is built on flexible, modular technology, either from open source frameworks and software or through cloud services. Download this special report for the eight key ways to prepare for and manage a modern data architecture.
From modern data architecture and hybrid clouds, to data science and machine learning, the Data Sourcebook is your guide to the latest technologies and strategies in managing, governing, securing, integrating, governing and analyzing data today. Download your copy today to learn about the latest trends, innovative solutions and real-world insights from industry experts on pressing challenges and opportunities for IT leaders and practitioners.
As enterprise data warehouses evolve to become modern data warehouses in the cloud, they still hold a significant role for enterprise analytics as a vital component of an enterprise data analytics platform. The reality is that this evolution will be a hybrid-cloud architecture that requires shared and unified capabilities to represent both cloud and on-premises environments as a single data analytics platform for the business. A multi-cloud architecture will be likely for many companies as data gravity from more data sources, users, and applications shifts data processing among clouds, requiring open data architecture principles and furthering the need for enterprise data unification and governance.
Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility and the ability to innovate. However, most database teams are struggling just to keep the lights on. While database environments continue to grow in size and complexity in conjunction with new business demands, the challenge of maintaining the performance and availability of business-critical systems and applications is growing in step. Download this report for key strategies and technologies to survive and thrive in today’s world of speed and scalability today.
Data science and machine learning are on the rise at insights-driven enterprises. However, surviving and thriving means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from infrastructure management, to data preparation and exploration, model training and deployment. In response, new solutions have emerged, along with the rise of DataOps, to address key needs in areas including self-service, real-time and visualization.
This book will discuss the ins and outs of Oracle’s licensing web, clarifying the murky points. We’ll also go in-depth on the petrifying and dreaded “Oracle Audit,” providing clear advice on how to prepare for it; advice that includes calling in the cavalry when needed, to protect you from Oracle’s clutches.
Until recently, clunkiness ruled the data systems and integration game. Expensive and complicated middleware was required to bring applications and information together, consisting of connectors, adapters, brokers, and other solutions to put all the pieces together. Now, cloud and containers – and Kubernetes orchestration technology – have made everyone’s jobs easier, and raised the possibility that both applications and data can be smoothly transferred to whatever location, platform, or environment best suits the needs of the enterprise. Download this special reports to learn the ins and outs of Containers, emerging best practices, and key solutions to common challenges.
From the rise of cloud computing, machine learning and automation, to the impact of growing real-time and self-service demands, the world of database management continues to evolve. Download this special report to stay on top of new technologies and best practices.
This white paper explores how GPU databases enable the rapid analysis of massive volumes of data, resulting in fast, accurate, up-to-the-minute dashboards from which valuable new insights can be extracted. It also explains how you can accelerate your existing BI pipeline to gain fast, unrestricted, ad-hoc access to your organization’s full scope of data, even when data grows exponentially.
Download this whitepaper to learn:
• Why raw data is much more powerful than pre-aggregated and cubed data
• Why distributed databases struggle with complex JOINs - and the secret to performing them with ease
• How GPU-acceleration offers value-added SQL capabilities that scale with your business
Data analytics is no longer the luxury of organizations with large budgets that can accommodate roving teams of analysts and data scientists. Every organization, no matter the size or industry, deserves a data analytics capability. Thanks to a convergence of technology and market forces, that’s exactly what’s happening. Download this special report to dive into the top technology trends in analytics today and why 2019 is becoming a year of transformation.
The pressure on companies to protect data continues to rise. In this year’s Cyber Security Sourcebook, industry experts shed light on the ways the data risk landscape is being reshaped by new threats and identify the proactive measures that organizations should take to safeguard their data. Download your copy today.
In a world where customers "crave self-service," having the technology in place to allow them to do this—and do it swiftly, efficiently, and correctly—is critical to satisfying customers.
Data warehouses are poised to play a leading role in next-generation initiatives, from AI and machine learning, to the Internet of Things. Alongside new architectural approaches, a variety of technologies have emerged as key ingredients of modern data warehousing, from data virtualization and cloud services, to JSON data and automation. Download this special report for the top trends, emerging best practices and real-world success factors.
"Digital transformation can only bring value if it supports what the business is trying to achieve. Viewing information as a single entity, connected through technology, is crucial to positioning modern organizations to cope with the challenges they face is a rapidly changing business environment."
The ability for knowledge graphs to gather information, relationships, and insights – and connect those facts – allows organizations to discern context in data, which is important for extracting value as well as complying with increasingly stringent data privacy regulations. Download this special report to understand how knowledge graphs work and are becoming a key technology for enterprise AI initiatives.
Data lakes help address the greatest challenge for many enterprises today, which is overcoming disparate and siloed data sources, along with the bottlenecks and inertia they create within enterprises. This not only requires a change in architectural approach, but a change in thinking. Download this special best practices report for the top five steps to creating an effective data lake foundation.
With the advent of big data and the proliferation of multiple information channels, organizations must store, discover, access, and share massive volumes of traditional and new data sources. Data virtualization transcends the limitations of traditional data integration techniques such as ETL by delivering a simplified, unified, and integrated view of trusted business data.
Managing data environments that cross over from on-premises to public cloud sites requires different approaches and technologies than either traditional on-premises data environments or fully cloud-based services. Following the eight rules outlined in this special report will help data managers stay on track. Download today.
Getting to a modern data architecture is a long-term journey that involves many moving parts. Most organizations have vintage relational database management systems that perform as required, with regular tweaking and upgrades. However, to meet the needs of a fast-changing business environment, data executives, DBAs, and analysts need to either build upon that, or re-evaluate whether their data architecture is structured to support and grow with their executive leaderships’ ambitions for the digital economy. Download this special report for the key steps to moving to a modern data architecture.
The world of data management has changed drastically – from even just a few years ago. Data lake adoption is on the rise, Spark is moving towards mainstream, and machine learning is starting to catch on at organizations seeking digital transformation across industries. All the while, the use of cloud services continues to grow across use cases and deployment models. Download the sixth edition of the Big Data Sourcebook today to stay on top of the latest technologies and strategies in data management and analytics today.
The adoption of new databases, both relational and NoSQL, as well as the migration of databases to the cloud, will continue to spread as organizations identify use cases that deliver lower costs, improved flexibility and increased speed and scalability. As can be expected, as database environments change, so do the roles of database professionals, including tools and techniques. Download this special report today for the latest best practices and solutions in database performance.
A lot has happened since the term “big data” swept the business world off its feet as the next frontier for innovation, competition and productivity. Hadoop and NoSQL are now household names, Spark is moving towards the mainstream, machine learning is gaining traction and the use of cloud services is exploding everywhere. However, plenty of challenges remain for organizations embarking upon digital transformation, from the demand for real-time data and analysis, to need for smarter data governance and security approaches. Download this new report today for the latest technologies and strategies to become an insights-driven enterprise.
Building cognitive applications that can perform specific, humanlike tasks in an intelligent way is far from easy. From complex connections to multiple data sources and types, to processing power and storage networks that can cost-effectively support the high-speed exploration of huge volumes of data, and the incorporation of various analytics and machine learning techniques to deliver insights that can be acted upon, there are many challenges. Download this special report for the latest in enabling technologies and best practices when it comes to cognitive computing, machine learning, AI and IoT.
Containers and microservices are the environments of choice for most of today’s new applications. However, there are challenges. Bringing today’s enterprise data environments into the container-microservices-Kubernetes orbit, with its stateless architecture and persistent storage, requires new tools and expertise. Download this report for the most important steps to getting the most out of containerization within big data environments.
The world of data management in 2018 is diverse, complex and challenging. The industry is changing, the way that we work is changing, and the underlying technologies that we rely upon are changing. From systems of record, to systems of engagement, the desire to compete on analytics is leading more and more enterprises to invest in expanding their capabilities to collect, store and act upon data. At the same time, the challenge of maintaining the performance and availability of these systems is also growing. Download this special report to understand the impact of cloud and big data trends, emerging best practices, and the latest technologies paving the road ahead in the world of databases.
From automated fraud detection to intelligent chatbots, the use of knowledge graphs is on the rise as enterprises hunt for more effective ways to connect the dots between the data world and the business world. Download this special report to learn why knowledge graphs are becoming a foundational technology for empowering real-time insights, machine learning and the new generation of AI solutions.
Fast Data Solutions are essential to today’s businesses. From the ongoing need to respond to events in real time, to managing data from the Internet of Things and deploying machine learning and artificial intelligence capabilities, speed is the common factor that determines success or failure in meeting the opportunities and challenges of digital transformation. Download this special report to learn about the new generation of fast data technologies, emerging best practices, key use cases and real-world success stories.
Cognitive computing is such a tantalizing technology. It holds the promise of revolutionizing many aspects of both our professional and personal lives. From predicting movies we'd like to watch to delivering excellent customer service, cognitive computing combines artificial intelligence, machine learning, text analytics, and natural language processing to boost relevance and productivity.
GDPR is coming, and with it, a host of requirements that place additional demands on companies that collect customer data. Right now, organizations across the globe are scrambling to examine polices and processes, identify issues, and make the necessary adjustments to ensure compliance by May 25th. However, this looming deadline is just the beginning. GDPR will require an ongoing effort to change how data is collected, stored, and governed to ensure companies stay in compliance. Get your copy of the GDPR Playbook to learn about winning strategies and enabling technologies.
Today, more than ever, data analysis is viewed as the next frontier for innovation, competition and productivity. From data discovery and visualization, to data science and machine learning, the world of analytics has changed drastically from even a few years ago. The demand for real-time and self-service capabilities has skyrocketed, especially alongside the adoption of cloud and IoT applications that require serious speed, scalability and flexibility. At the same time, to deliver business value, analytics must deliver information that people can trust to act on, so balancing governance and security with agility has become a critical task at enterprises. Download this report to learn about the latest technology developments and best practices for succeeding with analytics today.
Data lake adoption is on the rise at enterprises supporting data discovery, data science and real-time operational analytics initiatives. Download this special report to learn about the current challenges and opportunities, latest technology developments, and emerging best practices. You’ll get the full scoop, from data integration, governance and security approaches, to the importance of native BI, data architecture and semantics. Get your copy today!
As data sources, workloads, and applications continue to grow in complexity, so does the challenge of supporting them. To be successful, businesses need faster, more flexible, and more scalable data management processes. Answering this call is a new generation of hybrid databases, data architectures and infrastructure strategies. Download today to learn about the latest technologies and strategies to succeed.
The adoption of new database types, in-memory architectures and flash storage, as well as migration to the cloud, will continue to spread as organizations look for ways to lower costs and increase their agility. Download this brand new report for the latest developments in database and cloud technology and best practices for database performance today.
Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility, and the ability to innovate through better collaboration, visibility, and performance. However, as data sources, workloads, and applications continue to grow in complexity, so does the challenge of supporting them. To be successful, businesses need faster, more flexible, and more scalable data management processes.
Download this special report to gain a deeper understanding of the key technologies and strategies.
The Internet of Things represents not only tremendous volumes of data, but new data sources and types, as well as new applications and use cases. To harness its value, businesses need efficient ways to store, process, and ana¬lyze that data, delivering it where and when it is needed to inform decision-making and business automation. Download this special report to understand the current state of the marketplace and the key data management technologies and practices paving the way.
Underpinning the movement to compete on analytics, a major shift is taking place on the architectural level where data is captured, stored, and processed. This transformation is being driven by the need for more agile data management practices in the face of increasing volumes and varieties of data and the growing challenge of delivering that data where and when it is needed. Download this special report to get a deeper understanding of the key technologies and best practices shaping the modern data architecture.
Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility and the ability to innovate. However, the reality is most IT departments are struggling just to keep the lights on. A recent Unisphere Research study found that the amount of resources spent on ongoing database management activities is impacting productivity at two-thirds of organizations across North America. The number one culprit is database performance.
Since its early beginnings as a project aimed at building a better web search engine for Yahoo — inspired by Google’s now-well-known MapReduce paper — Hadoop has grown to occupy the center of the big data marketplace. Right now, 20% of Database Trends and Applications subscribers are currently using or deploying Hadoop, and another 22% plan to do so within the next 2 years. Alongside this momentum is a growing ecosystem of Hadoop-related solutions, from open source projects such as Spark, Hive, and Drill, to commercial products offered on-premises and in the cloud. These next-generation technologies are solving real-world big data challenges today, including real-time data processing, interactive analysis, information integration, data governance and data security. Download this special report to learn more about the current technologies, use cases and best practices that are ushering in the next era of data management and analysis.
The value of big data comes from its variety, but so, too, does its complexity. The proliferation of data sources, types, and stores is increasing the challenge of combining data into meaningful, valuable information. While companies are investing in initiatives to increase the amount of data at their disposal, most are spending more time finding the data they need than putting it to work. Download this special report to learn about the key developments and emerging strategies in data integration today.
When asked recently about their top reasons for adopting new technologies, the readers of Database Trends and Applications all agreed: supporting new analytical use cases, improving flexibility, and improving performance are on the short list. To compete in our global economy, businesses need to empower their users with faster access to actionable information and a better overall picture of their operations and opportunities. At the forefront of this journey to create value from data is in-memory processing. Download this special report to learn about the latest developments surrounding in-memory data management and analysis.
Download this special report to guide you through the current landscape of databases to understand the right solution for your needs.
From fraud detection to ad targeting, supply-chain optimization to campaign forecasting, the key use cases for big data require a successful analytics program. Businesses are investing heavily in initiatives that will increase the amount of data at their fingertips. In fact, the percentage of organizations with big data projects in production is expected to triple within the next 18 months according to a recent study from Unisphere Research. However, many businesses are spending more time finding needed data rather than analyzing it. To compete on analytics, the right mix of people, processes and technology needs to be in place to generate value. Download this special report to learn about the key technology solutions and strategies for succeeding with big data analytics today.
Today, the world of decision-making, along with the data sources and technologies that support it, is evolving rapidly. The future of data warehousing is not only being shaped by the need for businesses to deliver faster data access to more users, but the need for a richer picture of their operations afforded by a greater variety of data for analysis. A new data warehousing architecture is emerging, along with a new generation of technologies and best practices, to support the requirements of big data and the need for faster decision-making. To learn about the new technologies and strategies paving the way, download this special report today.
The “pie-in-the-sky” days of big data may be over, but the urgency for businesses to compete on analytics is stronger than ever. In fact, the percentage of organizations with big data projects in production is expected to triple within the next 18 months based on a recent study from Unisphere Research. The conversation around big data is shifting, from why to how. How can businesses harness the bits and bytes of data being captured inside and outside their enterprise to improve, empower and innovate? To learn about the key big data success stories today, download this special report.
The hottest term today—the “Data Lake”—is currently coming off the hype cycle and into the scrutiny of pragmatic IT and business stakeholders. As with all big concepts that have transformed the industry, from the early days of data warehousing and business intelligence, to the growth of cloud computing and big data, best practices are ultimately proven to deliver the benefits promised. To clarify the ambiguities surrounding the concept of the Data Lake, Unisphere Research and Database Trends and Applications combined forces with Radiant Advisors to publish a comprehensive report, “The Definitive Guide to the Data Lake.” By combining an analysis of fundamental information management principles with existing customer implementations of big data and analytics, this report explains how current data architectures will transform into modern data platforms. Download your copy today.
Sponsored by industry-leaders Hortonworks, MapR, Teradata and Voltage Security
From hybrid databases that can process structured and unstructured data - and run transactions and analytics - in the same location, to hybrid data architectures that bring together both established and new database approaches to address the requirements of different data sources, workloads and applications, the reality that most organizations are facing today is that the world of big data is a multifaceted one. To be successful, organizations need speed, scale, flexibility and agility. At the same time, they need ways to keep down costs and complexity. To learn about the key technologies and approaches to hybrid databases and data environments, download this special report from Database Trends and Applications.
Today, there are more things connected to the Internet than people on the planet. From home appliances and cars, to light bulbs and livestock, if you can attach a sensor to it, it can be become part of a universe of physical objects able to communicate and interact digitally. According to estimates, this universe is on track to exceed over 25 billion devices by 2020, not including PCs, tablets and smartphones.
Underpinning the movement to compete on analytics, a major shift is taking place on the architectural level, where data is captured, stored, and processed. This transformation is being driven by the need for more agile and flexible data management processes in the face of increasing volumes and varieties of data.
Whether Hadoop becomes the de facto data management platform of the future or simply a key component in a hybrid architecture comprised of numerous technologies, one thing is for sure: Hadoop adoption is growing. In fact, a recent survey conducted using subscribers of Database Trends and Applications found that 30% have deployed Hadoop at their organization while 26% are currently considering or planning for its adoption within the next 12 months.
Ask the average DBA how they spend the majority of their time and the answer is almost always going to be “performance tuning.” Optimal performance is a constantly moving target. Database transactions and volumes are constantly growing. Business applications are increasing in sophistication with greater user requirements. To stay competitive, companies want speed, scalability, high-availability and cost-efficiency. The challenge, of course, is getting there.
Many IT departments are researching new technologies to address these issues, from database monitoring tools, to new types of databases, to virtualization and cloud solutions. In a recent study conducted over 285 organizations across North America, database performance monitoring was ranked the top area ripe for automation. This same study found that migrating or upgrading databases was the top area for investment, followed closely by virtualization and cloud.
Data integration is a crucial part of the equation for any business interested in fully harnessing its information resources. However, data integration challenges are multiplying in step with the growing complexity of data environments. Most organizations today are dealing with an ever-expanding array of data sources and users with varying requirements. Therefore, it is no surprise that integration projects are topping the priority list. In fact, a brand new study conducted over the readers of Database Trends and Applications found that 38% of companies polled had integration projects in production while 30% were planning or piloting projects. Download this special report to learn about the key developments in the marketplace and new solutions helping companies overcome challenges.
In-memory computing is currently racing toward the mainstream and revolutionizing the way enterprises leverage data to support their business requirements along the way. How big is this revolution? Nearly 75% of IT stakeholders at organizations across North America surveyed by Unisphere Research believe that in-memory technology is important to enabling their organization to be competitive. To succeed in today’s economy, businesses need faster data processing, fresher data, and more cost-effective data systems. Download this special report to learn the ins and outs, as well as the key products available in the marketplace.
When it comes to databases, businesses have more choices than ever today. From the longstanding RDBMS, to the growing camps of NoSQL and NewSQL databases, the landscape is becoming increasingly specialized and best-of-breed. This transformation mirrors the plethora of challenges IT departments across industries face today: the need to handle larger data volumes, the need to handle new data types, the need to deliver data faster, the need to support more application users, and the need to operate more cost-effectively, to name a few. Download this special report to read about the current state of the marketplace and learn about the new technologies that are helping businesses address these challenges.
Real-time information processing, a concept that has been around for a long time, has been in vogue lately. One reason for its popularity is the fact that real-time capable technology and online services have become very affordable, even for small businesses. Another factor is that real time has the attention and interest of the boardroom and executive suite. The idea of being able to instantaneously sense and respond to threats and opportunities has a lot of appeal for business leaders vying for an edge in a fiercely competitive global economy. With technology chipping away at the time it takes to gather relevant and accurate data, there’s less need for bureaucratic, hierarchical decision-making structures. Emerging technologies are now becoming part of the enterprise scene—such as in memory technology, cloud, mobile, and NoSQL databases—are bringing more real-time capabilities to the fore.
Business intelligence and analytics has undergone a revolutionary shift over the past few years, a transition that is still working its way through enterprises and their processes. Nowhere is this more evident than in the rapidly changing roles and expectations of information workers—those managing the data, as well as those consuming it.
Cloud databases are on the rise as more and more businesses look to capitalize on the advantages of cloud computing to power their business applications. In fact, a recent Unisphere Research study revealed that 37% of organizations are now using or considering adopting a cloud database.
Elastic scalability, high availability, flexible capacity planning, and self-service provisioning are among the key, sought-after benefits. While traditional concerns about data security and compliance still have some enterprises watching from the sideline, for many enterprises, the advantages of cloud databases are becoming harder and harder to ignore.
Since the 1980S, companies have invested millions of dollars in designing, implementing, and updating enterprise data warehouses as the foundation of their business intelligence systems. The founding principle of the data warehouse was simple: a single version of the truth to support corporate decision making. Today, the world of decision making, along with the data sources and technologies that support it, is evolving rapidly.
The future of data warehousing is not only being shaped by the need for businesses to deliver faster data access to more users, but the need for a richer picture of their operations afforded by a greater variety of data for analysis. The unstructured and semistructured data that companies are collecting from social media, remote sensors, web traffic, and other sources needs to be integrated and combined for analysis to produce valuable insights for better decision making.
Listening to the pundits, you can be forgiven for thinking that the unstructured, “cloudified,” out-of-network data tsunami is poised to sweep through and shake enterprises out of their comfortable, relational worlds. But there’s more to the story than that. Enterprises still, and will likely continue to, rely on relational database systems as their transactional workhorses. These systems continue to evolve and adapt to today’s new data realities. Many relational database and data warehouse environments are opening to unstructured data, running in clouds, and supporting caches that enable real-time— or near real-time—decision making.
The next generation of databases and data platforms is coming into full fruition to help enterprises more effectively store, process, analyze and deliver value from Big Data. This report hones in on the key challenges and opportunities ahead, and provides in-depth information on leading-edge technologies and solutions. Download your copy today to stay ahead of the latest developments in NoSQL, NewSQL and Hadoop.
This DBTA Thought Leadership Series discusses new approaches to planning and laying out tracks and infrastructure, moving to a real-time analytics requires new thinking and strategies to upgrade database performance. What are needed are new tools, new methodologies, new architectures, and a new philosophy toward managing data performance.
Today’s 24/7 enterprises require a well-designed, next-generation data integration architecture. Why is data integration so difficult? For many organizations, data integration has been handled as a dark art over the years, implemented behind the scenes with ad hoc scripts, extract, transform, and load (ETL) operations, connectors, manual coding, and patching. Often, front-end applications to get at needed data are built and deployed one at a time, requiring considerable IT staff time, as well as creating a waiting period for business decision makers. This one-off, manual approach to data integration will not work in today’s competitive global economy. Decision makers need information, at a moment’s notice, that is timely and consistent. However, they are challenged by their organizations’ outdated data integration systems and methods. Often, information may be delayed for weeks, if not months, by the time it takes to develop handcoded scripts to deliver requested reports. The process
Hadoop is marching steadily into the enterprise, but key challenges remain, from manual coding demands to a lack of real-time capabilities and the time it takes to bring a Hadoop project into production. At the same time, brand-new startups and veteran software companies alike are delivering new offerings to the marketplace to make it easier to deploy, manage, and analyze Big Data on Hadoop. From data integration and business intelligence tools to integrated analytical platforms and a new wave of SQL-on-Hadoop solutions, the common goal is to help companies unleash the power of Hadoop for Big Data analytics. Download this special report to learn about the key solutions. Sponsored by MarkLogic, RainStor, Tableau, Qubole, Karmasphere, Appfluent, and Hadapt.
UNSTRUCTURED DATA: Managing, integrating, and Extracting Value While unstructured data may represent one of the greatest opportunities of the big data revolution, it is one of its most perplexing challenges. In many ways, the very core of big data is that it is unstructured, and many enterprises are not yet equipped to handle this kind of information in an organized, systematic way. Effectively capturing and capitalizing on unstructured data isn’t just a technical challenge, it represents an organizational challenge. A flexible and agile enterprise environment—supported and embraced by all business units—will elevate unstructured data processing and analysis to a position in which it can help drive the business. This Thought Leadership Series is sponsored by Objectivity and Database Plugins
THE IDEA OF THE REAL-TIME ENTERPRISE is straightforward: Increase your organizational responsiveness through automated processes and raise organizational effectiveness and competiveness. If your organization can fulfill orders, manage inventory, resolve customer issues, and implement strategies to address changing circumstances faster and more efficiently, your organization is going to be more successful. However, for most enterprises, this is still an unrealized objective. Increasing data volumes, data varieties, and business demands are now stretching the limitations of traditional data management technologies and intensifying the challenge of integrating and analyzing data in real-time. Consequently, many organizations are looking beyond their current IT infrastructures. Download this report to learn about the leading technologies enabling organizations to deliver data across the enterprise in real-time. Sponsored by Oracle, SAP, Objectivity, JackBe and BackOffice Associates.
Cloud databases are on the rise as more and more businesses look to capitalize on the advantages of cloud computing to power their business applications. In fact, a recent Unisphere Research study found that nearly one-third of organizations are currently using or plan to use a cloud database system within the next 12 months. Download this complimentary report, sponsored by NuoDB, GenieDB, 10gen, Cloudant, Progress DataDirect, Clustrix, Objectivity and TransLattice, to gain a deeper understanding of the different types of cloud databases, their unique benefits and how they are revolutionizing the IT landscape.
BIG DATA, a well-used term defining the growing volume, variety, velocity, and value of information surging through organizations-has become more than a buzz phrase thrown about at conferences and in the trade press. Big Data is now seen as the core of enterprise growth strategies. Business leaders recognize the rewards of effectively capturing and building insights from Big Data, and see the greatest opportunities for Big Data in competing more effectively and growing business revenue streams. As the amount and variety of data grows, so do the skills required to capture, manage and analyze this data. This specialized issue of Best Practices from Oracle, Attunity, Couchbase, HiT Software Inc, Progress DataDirect, LexisNexis, Confio and Objectivity focus on a more formidable challenge: making Big Data valuable to the business. Complimentary from DBTA.
The appeal of in-memory technology is growing as organizations face the challenge of Big Data, in which decision-makers seek to harvest insights from terabytes and petabytes worth of structured, semi-structured and unstructured data that is flowing into their enterprises. This special thought leadership series provides context and insight on the use of in-memory technology, and detailed explanations of new solutions from SAP, Tableau Software, Tibco Spotfire, JackBe, Terracotta and MemSQL. Complimentary from DBTA.
Is your organization’s systems and data environments ready for the Big Data surge? If not, you are not alone. A recent study conducted among Independent Oracle User Group members by DBTA’s Unisphere Research finds that fewer than one-in-five data managers are confident their IT infrastructure will be capable of handling the survey of Big Data. This special Best Practices section from DBTA provides context and insight on the need to address this issue now, and detailed explanations of new technologies for dealing with Big Data from Aster/Teradata, MarkLogic, Akiban, Progress/Data Direct, Infinitegraph, HP-Vertica and Denodo. Complimentary from DBTA.
To compete in today’s economy, organizations need the right information, at the right time, at the push of a keystroke. But the challenge of providing end users access to actionable information when they need it has also never been greater than today. Enterprise data environments are not only growing in size, but in complexity - with a dizzying array of different data sources, types and formats. The September 2012 Best Practices in Data Integration, Master Data Management, and Data Virtualization report examines the data integration challenges and opportunities that Big Data is currently presenting data-driven organizations.
With the rise of big data, the database and data management tools market is in a state of flux, the likes of which have not been seen in this sector before. Companies are now awash in big data, and end users are demanding greater capability and integration to mine and analyze new sources of information. As a result, organizations are supplementing their relational database environments with new platforms and approaches that address the variety and volume of information being handled. In this special section in Database Trends and Applications analyst Joseph McKendrick brings you up on current thinking and strategies users and vendors are pursuing to extract value from large, often unwieldy data stores. This is followed by nine separate sponsored content pieces focusing on in-memory, real-time data integration, data virtualization, BI, columnar databases, NoSQL and Hadoop.
The rise of Big Datais challenging many long-held assumptions about the way data is organized, managed, ingested, and digested. However, for many organizations, Big Data is still a new frontier that they have only begun to explore. "Many organizations leave their data to pile up; they are aware of it as a resource but haven't analyzed it. They don't know what's useful and what's worthless." This fourteen-page section from the March edition of Database Trends and Applications is an invaluable resource that provides multiple perspectives on the chief challenges our readers face and the solutions that will enable organizations to begin tapping into the power of Big Data assets.
Key extracts from Database Trends and Applications from the December print edition focus on "Data Security and Compliance".