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.
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.
Data visualization, dashboards and predictive data science are only as good as the data you start with.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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?
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.
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.
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.
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.
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.
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. You need to take into account the differences that exist between an on- premise data warehouse and a cloud data warehouse.
In the eBook - Data Warehouse Automation in Azure for Dummies, you can 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.
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?
This book isn’t just about companies adopting DevOps as a general concept, but specifically about applying it to their databases. Even among DevOps adopters, the database tends to be a stronghold of pre-DevOps culture and processes.
Thus, it becomes what is often the biggest bottleneck and the biggest opportunity in many software teams. So, this book is about DevOps for the database, and why it’s different, hard, valuable… and doable!
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.
It’s an all-too familiar situation: at first, Redis users find the system easy to deploy and use. But then their workloads grow and their data volumes increase, and things start to change quickly. That’s when many organizations discover that the ownership costs, scalability, and operational complexity of Redis to be much worse than they’d ever imagined. Learn how to compare Aerospike to Redis side-by-side and determine if you've outgrown the speed and scale that Redis can handle.
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.
The "care and feeding" of open source databases is a must, because though the tool itself is free, there is a level of manpower and maintenance required to adjust these tools to specific data needs. Server sprawl is one of the first signs that an open source data architecture is no longer functional, along with unpredictable latency responses and uptime (or downtime). Signal, a leading identity resolution platform, was looking to replace its existing data store which was becoming increasingly expensive and unreliable and had grown to an incredible 550 servers to support growing data needs. This paper details Signal's data requirements and why they chose to move away from the popular open source database, Cassandra, in order to optimize for 25% data growth YoY.
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™.
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.
Learn how the AnzoGraph DB, a highly scalable and fast graph analytics database, can empower boundless applications in the graph, AI, and machine learning revolution. AnzoGraph DB partners are creating the next wave of on-premises SaaS, cloud, and Kubernetes-based applications with the powerful AnzoGraph DB graph database.
Large organizations do not need to be told that their extensive data universe is fragmented, poorly integrated and drenched in complexity. Neither do they need to be told about the value that can be derived if they could assemble, explore and analyze organized subsets of that data regardless of format or location in the organization. Cambridge Semantics’ Anzo allows enterprises the ability to do just that, in a scalable and practical way. This white paper by Eric Kavanagh and Robin Bloor, PhD, of the Bloor Group explains how your organization can successfully navigate the journey to a fully automated Enterprise Data Fabric with Anzo.
Within every business, the data landscape varies widely in maturity and quality. Some parts of the company’s data are like the shiny, new living room of a house, where everything is clean and spic and span. However, establishing a fully operationalized, powerful enterprise data fabric is possible for a business whether they have the majority of their data in well polished data warehouses, or if their data lacks any unifying organization. This white paper by Dan Woods at Early Adopter Research will explain the process of building an enterprise data fabric from four common starting points using the Anzo data discovery and integration platform.
Enterprises can’t afford to have critical applications go down for any amount of time – no matter where they reside. However, teams trying to monitor both cloud and traditional environments are faced with exceptional challenges. Being able to efficiently and effectively automate incident management can determine how quickly an enterprise embraces digital transformation and ensures its future success. Learn how AIOps can help your IT Ops team confidently support your growing environments today.
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.
Analytics are crucial to business development but are constantly evolving, which is why analytics software developers are always looking for ways to enhance user experience, improve data management, and make data more accessible to users.
As companies look to improve decision-making processes, self-service business intelligence (SSBI) has become a necessity. SSBI software stands as the missing link between managing raw data and turning that data into digestible, actionable information. It gives businesses the tools needed to analyze trends and streamline company operations.
Working with data generally entails carefully examining it, interpreting it, and creating reports. But the single most important part of the job is effectively communicating the meaning of that data. People need to understand it—and why it matters—so they can use it to make decisions. You probably know the data better than anyone, so you’re in the best position to communicate its importance. It’s up to you to make sure that you’re getting your point across.
How Looker helped a German startup become the leading pan-European marketplace for deposit and investment products.
To drive growth and innovation, leading organizations creatively leverage the data they collect to maximize its value and fuel growth. Embedding analytics in your product offering is an effective, proven way to monetize data. Let’s review some best practices.
Although mobile gaming has been around for twenty years, this now billion-dollar industry continues to experience explosive growth, with about 800,000 games currently offered through app stores. Candy Crush, produced by King, a leading interactive entertainment company, is one of the leading game franchises in this crowded field.
The California Consumer Privacy Act (CCPA) applies to a state that represents the world’s fifth-largest economy. In this webinar hear real-world insights and strategies to help organizations as you prepare for the CCPA. The session discusses planning and scope, understanding your data landscape, assessing risk, prioritizing and operationalizing remediation efforts and planning for the emerging privacy landscape.
What you’ll take away:
Lessons learned from GDPR and CCPA remediation projects
Tips to understand the privacy journey and get started with understanding your obligations
Best practice recommendations to help build an effective data governance program
Preparing your data privacy strategy and approach to support additional regulation.
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.
How do organizations protect their data from persistent, sophisticated and well-funded attackers? Ponemon Institute's latest survey of current IBM Security Guardium clients shows how the Guardium platform has improved organizations' ability to protect their data.
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.
In less than one year's time, regulators in California will start enforcing the requirements of the California Consumer Privacy Act (CCPA). Security and privacy professionals must repurpose their GDPR programs to comply with CCPA and address privacy globally. This report outlines the main steps companies must take today to kick off their preparation for CCPA.
Data security is on everyone’s mind these days, and for good reason. The number of successful data breaches is growing thanks to the increased attack surfaces created by more complex IT environments, widespread adoption of cloud services and the increasingly sophisticated nature of cyber criminals. This paper looks at five of the most prevalent – and avoidable – data security missteps organizations are making today, and how these “epic fails” open them up to potentially disastrous attacks.
This white paper provides a high-level overview of key data privacy trends and regulations along with conceptual frameworks to begin addressing these changes.
It also shows how data security solutions from IBM Security can help support and accelerate specific privacy needs through the provision of robust security controls that enable smarter data protection.
According to IBM, 9 billion records have been breached since 2013, but only 4% of them were encrypted. More than ever, the security of your firm's bottom line depends on the technologies that secure your data — the fundamental currency of digital businesses. Most security and risk (S&R) leaders can't be completely risk averse; they must instead secure their data with the right tools for the right circumstances. Today, that means strategically deploying data-encrypting solutions. This report details which encryption solutions are available to secure data in its various states and looks at the viability of emerging encryption technologies.
From the perspectives of both data protection and regulatory compliance, it is just as critical to protect sensitive cloud-based data as it is on-premises data. One way to do this is through data encryption, yet many business’s encryption efforts are mired in fragmented approaches, siloed strategies for policy management and compliance reporting, and decentralized key management. These situations have all contributed to making encryption complicated and difficult to implement and manage. This paper looks at 5 best practices for securing data in multi-cloud environments using the latest data encryption technologies.
When it comes to cloud environments, whether in the public cloud or a privately hosted or hybrid environment, data security and protection controls must protect sensitive data—and support constantly growing government and industry compliance requirements. Read this ebook to learn how data security and protection technologies should operate in multiple environments (physical, cloud and hybrid) at the same time.
Because of this, the Internet of Things creates an opportunity to measure, collect and analyze an ever-increasing variety of behavioral statistics. That being said, data is central to any IoT implementation, and depending on the application, there could be high data acquisition requirements which in turn lead to high storage and heavy data processing requirements.
This paper reviews how an IoT Data platform fits in with any IoT Architecture to manage the data requirements of every IoT implementation. It is based on the learnings from existing IoT practitioners that have adopted an IoT Data platform using InfluxData.
Stream processing unifies applications and analytics by processing data as it arrives, in real-time, and detects conditions within a short period of time from when data is received. The key strength of stream processing is that it can provide insights faster, often within milliseconds to seconds.
With that being said, stream processing naturally fits with time series data, as most continuous data series are time series data. And time series data needs a purpose-built database to ingest, store and process it. This is exactly what InfluxDB is. And this is why, given its high-write throughput and the scalability it allows, InfluxDB suits stream processing.
The first step in getting control and visibility into your DevOps environment is to collect and instrument everything. But how do you get started? And how do you perform DevOps Monitoring efficiently and quickly in a way that’s measurable and scalable? To answer that question, InfluxData distilled our learning from hundreds of our customers into a simple 5-step process; and also highlighted some of our representative DevOps Monitoring customer use cases. Our perspective stems from hands-on experience and from our differentiation as a company—built by developers for developers.
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.
There is one commonality amongst all these professionals: no one has time to spare. Everyone wants to streamline processes and solve problems as quickly as possible and move on to the next issue of the day. A simple, repeatable process for performance tuning is a valuable time saver because you’ll get it right the first time.
This paper is for the Accidental DBA and contains 12 essential tips for working with SQL Server. These simple, yet powerful tips are filled with lessons learned on the front lines and from years of database administration experience. Following these tips can help transform the Accidental DBA into an organized DBA with clear direction for ensuring the best possible database performance.
This has been a banner year for cybersecurity crime, with hackers targeting consumers, government agencies, and private corporations alike. At the same time, new data privacy mandates such as the EU’s GDPR, as well as the California Consumer Privacy Act (CCPA), which goes into effect in 2020, have increased the burden of safeguarding data. Download this white paper to better understand the challenges and how you can maintain security, compliance AND functionality.
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.
Almost everyone today operates multi-cloud and cloud-hybrid systems. These organizations have data in flat files, tagged files, relational databases, document stores, graph databases, and more. Furthermore, processing that data spans technologies from batch ETL to changed data capture, stream processing, and complex event processing. The variety of tools, technologies, platforms, and data types makes it difficult to manage processing, access, security, and integration. Enter the Data Fabric. A Data Fabric is a combination of architecture and technology that is designed to streamline the complexities of managing all those different kinds of data. It uses multiple database management systems and is deployed across a variety of platforms. This report by Eckerson Group summarizes the results of their extensive research into Data Fabrics through interviews with numerous industry experts and a dozen or more demonstrations of implemented data fabric technologies.
With any development project it is important to track and measure the value being delivered for the business investment made over time to the end-users or stakeholders against plan. Without this, continued investment in the project is difficult to justify. In traditional project management, Earned Value Management (EVM) is a proxy to measure this. EVM is based on a well-defined baselined project plan at the onset of the large-scale project. EVM is a sufficient tool when building items that have been built time and time again, as in manufacturing. Unfortunately, for emergent development, “we don’t know what we don’t know” at the onset of a project in order to establish a full project basis of estimate to measure against. This is where Agile approaches are well suited. Enter Agile Earned Value Management (AEVM).
Over the past decade, teams and departments within organizations have adopted the Agile model—including principles, processes and tools. Data shows that Agile software development teams are bringing products to market 30-75% faster—certainly qualifying as game changing speed.
The good news is that if your organization has adopted agility principles for any teams and departments, you already have an important foundation to move forward with Enterprise Business Agility. Each of these Agile teams is like a gear, creating momentum within its function. Synchronizing these gears is necessary to propel the whole organization forward. This is the goal of Enterprise Business Agility.
The software industry has been talking about applying Value Stream Management to improve transparency, efficiency and quality for a few years now. Yet, you won’t find many clear and concise definitions of what it is or how to “do” it.
Value Stream Management offers a unique view of the software delivery lifecycle through the customer experience lens, to better align with business objectives and scale Agile & DevOps transformations.
Agile software development promises to reduce or eliminate common challenges encountered in aligning software design and delivery with business objectives. The result: greater value for customers and the business alike. How are organizations adopting, scaling, and measuring the effectiveness of their Agile initiatives? Gatepoint Research surveyed IT executives across multiple industries to find out.
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.
One of the key components of an insurance company’s success is its ability to analyze risks, which leads to the development of policy rates that enable them to maintain a profit while keeping insurance affordable. Like many enterprises, this insurance company’s data was generated, collected and processed separately on multiple platforms. This created data silos between mainframe and open systems, making the complex task of risk analysis even more challenging. In order to increase their competitive edge and provide better claim experiences, they required a solution that allowed them to integrate data from all computing platforms to better understand customer behavior and meet customer expectations.
Like most enterprises, you've likely invested in best-in-class APM and NPM tools.
But now, do you find that your NOC and IT Ops teams are
• Sifting through thousands of APM and NPM alerts to determine business impact in real-time?
• Endlessly tweaking thresholds and suppressing alerts to catch disruptions, problems and outages in time?
• Constantly living with the fear of missing important alerts?
Hear Jason Trunk and Iain Armstrong from BigPanda as they explain how to use AIOps to help you unlock the full value of your APM and NPM investments. Help your NOC and IT Ops teams finally take full advantage of the deep monitoring data generated by these tools.
Your IT Operations execs and your service owners want reporting that shows easy-to-understand reports on:
• Application and service uptime and performance
• IT Ops and Network Operations Center team performance
• Incidents by source, severity and other parameters.
To do this, your IT Ops team is probably wasting precious hours every week, wrangling with spreadsheets and general-purpose reporting tools that are hard to use and update. Sound familiar?
Utilizing a unified analytics approach can change all of that and give back hours that your IT Ops team doesn’t have!
View this on-demand webinar to learn how BigPanda Unified Analytics, purpose-built for IT Ops reporting and analysis, can help you quickly and easily analyze, visualize and improve your key IT Ops KPIs and metrics.
If you’re part of a NOC or an IT Ops team, or if you manage one, you know that overwhelming IT noise is your #1 enemy.
It floods your teams with false-positives, it buries critical root-cause events and it makes it hard to proactively detect expensive P1 and P0 outages.
But can AIOps tools be the answer?
View a 30-minute on-demand webinar to learn if AIOps (IT Ops tools powered by AI and ML) can help you:
• Eliminate - not just reduce - IT noise
• Create correlated incidents that point to the probable root cause
• Help you catch P2s and P3s before they become customer-impacting P1 and P0 outages.
If you use ServiceNow, JIRA or another service desk system, your helpdesk and ITSM teams have probably run into some of these issues:
A single outage or disruption creating hundreds of tickets
Critical contextual information missing from these tickets
Duplicate and wasted efforts because your service desk and your monitoring tools are not connected through a bi-directional integration
What's the cure? AIOps.
When you adopt AIOps with BigPanda, in about 8-12 weeks, you can reduce your ticket volume by up to 99%, use information from your CMDB, inventory databases, spreadsheets and excel files to add critical context to your tickets, and unify workflows between your IT Ops, helpdesk and ITSM teams.
Successful businesses are those that can take these data points and convert them into actionable information to optimize operations or to innovate, even as their records and data sources grow exponentially. This is why Change Data Capture (CDC) has been deployed in thousands of organizations worldwide and remains a critical component of data architectures. This white paper discusses the benefits of modernizing and upgrading from legacy CDC platforms.
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 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.
Delegating data security to IT teams does not absolve the responsibility business leaders have to protect data. Forrester Consulting surveyed 150 IT, security, and risk decision makers and examined their approach to protecting their company’s critical data and information and communicating data risk to senior business executives.
There is nothing easy about securing sensitive data to combat today’s threat landscape, but companies can take steps to ensure that they are devoting the right resources to their data protection strategy. Get a quick overview of the five most common data security failures that, if left unchecked, could lead to unforced errors and contribute to the next major data breach.
Security and risk (S&R) pros can't expect to adequately protect customer, employee, and sensitive corporate data and IP if they don't know what data exists, where it resides, how valuable it is to the firm, and who can use it. In this report, we examine common pitfalls and help S&R pros rethink overly complex and haphazard legacy approaches to data discovery and classification. This is an update of a previously published report. Forrester reviews and revises it periodically for continued relevance and accuracy, most recently doing so to factor in new ideas, tools, and data.
Public sentiment is changing around data privacy. In this video, see how IBM Security Guardium Analyzer can help onrganizations efficiently address regulated data risk through data discovery, data classification, vulnerability scanning and database risk scoring for on-premises and cloud databases.
A holistic data protection strategy that includes encryption can reduce data breaches and privacy concerns. Stephanie Balaouras, Research Director at Forrester Research discusses the importance of a data encryption, how to get started on your data encryption strategy; why the cloud is a major use case for encryption; and why the savviest companies prioritize data privacy not only for compliance, but with customers' best interests in mind.
Your data is moving to the cloud – that’s a given – but will it be safe once it gets there? The new reality of a hybrid, multi-cloud world complicates data protection efforts for organizations everywhere, as do new privacy and compliance mandates.
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.
You know that you need to protect personal and sensitive data to comply with privacy regulations. But are you sure you know all of the places where your organization is storing that data?
Breaches and stolen data result in fines, low productivity, tarnished reputation, burned customers and lost revenue. With the dust still settling on how companies can best assign responsibility for data privacy, more database administrators (DBAs) like you are becoming the go-to data controller. Your experience with databases, storage and the network make your desk one of the first stops when someone asks, “What is our data privacy exposure?”
Watch this webinar to learn how you can secure and govern your data on Cloud Pak for Data System and improve your insights and accelerate regulatory readiness with advanced data governance.
Watch the webinar to learn more about the IBM Cloud Pak for Data platform. Join Philip Howard, an analyst with Bloor Research, and IBM’s Janine Sneed, Chief Digital Officer, Hybrid Cloud, for the discussion.
If you want to deploy machine learning – and almost everybody does – then you need an environment that facilitates that. IBM refers to this by saying that you can’t have artificial intelligence without an information architecture (“AI requires IA”). And the problem with building an information architecture is that it involves many moving parts, many software requirements and many personas. To make this work requires that companies adopt AnalyticOps as a principle, and this requires not just a broad range of base functionality but collaborative support across all of the personas involved. Even though ICP for Data is still developing you can see that this is the direction in which the product is headed. It would be infinitely harder to achieve with a set of disparate products from multiple vendors.
Join this webinar to learn about the new deployment option for IBM Cloud Private for Data software – IBM Cloud Pak for Data System. This new release is a hyper-converged system delivered with IBM Cloud Private for Data software pre-installed inside OEM hardware.
Makes queries across multiple data sources fast and easy without moving your data. IBM Cloud Pak for Data provides all the benefits of data virtualization and helps you manage your data better.
In our 21-criterion evaluation of enterprise insight platforms (EIPs), which combine data management, analytics, and insight application development tooling, we identified the nine most significant ones — EdgeVerve, GoodData, Google, IBM, Microsoft, Reltio, SAP, SAS, and TIBCO Software — and researched, analyzed, and scored them. This report shows how each provider measures up and helps CIO professionals make the right choice when selecting an enterprise insight platform.
Enterprises today are sitting on an untapped goldmine — their data — and they are looking to use it to transform their businesses by improving decision making across the organization, accelerating innovation, improving the customer experience, and driving operational efficiency. Extracting this value has been fraught with challenges ranging from siloed data, outdated tools, a lack of skills, misaligned teams, and shadow IT. However, firms are tackling these challenges head-on, with a broad range of initiatives and new, integrated tools that democratize data and analytics, streamline collaboration, accelerate time-to-insight, and drive impact.
Artificial intelligence uses algorithms to make sense of and act upon diverse, complex and fast-moving data — its entire reason for being. CIOs responsible for enabling AI initiatives need to foster a culture of data literacy to drive success with AI-based systems.
As companies invest more and more in data access and organization, business leaders seek ways to extract more business value from their organization’s data.
Today, an overwhelmingly large portion of information in the world exists in textual form, from business records and government documents, to social media streams, emails and blogs. The information these sources contain is only as good as our ability and tools to extract and interpret it. Download this white paper for a deep dive into how text analytics works, including linked data techniques and semantic annotation.
Experience the Cloudera Data Warehouse on Cloudera Data Platform (CDP), the industry's first enterprise data cloud. Learn how data-driven businesses enable thousands of new users and hundreds of new use cases with the Cloudera Data Warehouse on CDP.
With 95% of C-level executives saying data is integral to their business strategy, now is the time to align your data and hybrid cloud strategy. This paper outlines the five steps for aligning your data and hybrid cloud strategies. You’ll also learn the importance of data context that is shared across multifunctional systems to deliver self-service business analytics and help turn insights into actions.
Join us for a demonstration of the Cloudera Data Platform (CDP) experience. The industry’s first enterprise data cloud brings together the best of Cloudera and Hortonworks. CDP delivers powerful self-service analytics across hybrid and multi-cloud environments along with sophisticated and granular security and governance. In this webinar, we will highlight key use cases and demonstrate unique CDP capabilities including intelligent migration, adaptive scaling, and cloud bursting.
Read 12 Requirements for a Modern Data Architecture in a Hybrid Cloud World to learn the key characteristics to look for in a modern data platform:
-Spanning on-premises and cloud infrastructures
-Handling data at rest and in motion
-Managing the complete data life cycle
-Delivering consistent data security, governance and control
-Providing data-driven insight and value
The flow of data through, around, and between enterprises keeps getting faster and deeper, but many organizations seem be drowning - rather than reveling - in it. Managing, storing, and classifying the information is one challenge. Figuring out what’s of material importance to the business is another. Then, there’s the fact that most companies still only leverage a relatively small portion of their data to better serve customers, understand markets, and improve operations. Download this special report to learn ways to better embrace data integration and governance to align data to pressing business needs.
The growing emphasis on digital transformation is encouraging more organizations to adopt initiatives driven by the Internet of Things (IoT).
While such initiatives enable enterprises to enhance customer experiences, create new business channels, or acquire new partner ecosystems, gaining the insights to realize these benefits can prove to be challenging. The sheer volume of data that these devices generate, the variety of data that comes in, and the velocity in which data is collected creates its own set of challenges in terms of storage, processing power, and analytics for such enterprises.
This technical brief outlines the top five complications faced by DBAs amid the rush of new database technologies in recent years. For each challenge it provides background, context and the benefits Foglight for Databases brings in addressing the challenge.
Read this report to dive into how DataOps applies to DevOps, agile and lean manufacturing principles for data management. Also, learn how the Qlik Data Integration Platform can enable agile cloud migration, automate data transformation for analytics and accelerate the cataloging, management, preparation and delivery of data assets.
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.
Take the first steps toward Database Release Automation (DRA). By following four simple steps, your team will see many immediate benefits, including:
• Faster application releases
• Huge reduction in human error
• Increased productivity for developers and DBAs
• Happier teams
Read this Frost & Sullivan whitepaper to hear how Financial Services Institutions (FSIs) are grappling with new challenges surrounding data growth, regulatory compliance, query complexity, and business demands. You’ll learn how Vertica can address the highest levels of performance and scalability, while meeting the key criteria outlined for an analytical database solution.
Vertica is transforming the way organizations build, train and operationalize machine learning models. Read this white paper to find out how you can bring predictive analytics projects to market faster than ever before with end-to-end machine learning management functions, massively parallel processing, simple SQL execution, and C++, Java, R and Python extensibility.
Read this case study to see how China PnR moved to Vertica when the company’s legacy system reached a tipping point in its business growth. Learn how China PnR saved millions in reduced hardware and software costs, improved employee productivity, and increased platform stability after switching to Vertica.
Read this case study to see why Finansbank selected Vertica to support the company’s more robust security and fraud-detection processes. The bank needed to enhance its cybersecurity capabilities and after implementing Vertica was able to perform queries on 2-4 billion data rows, improve report generation, and empower its security team to quickly detect anomalies.
Are you ready to align the strategy of your organization or agency to execution so that you can maximize investment value? Is your Portfolio/Program Management Office (PMO) ready to join development and delivery in realizing the benefits of Lean and Agile?
Tungsten Clustering by Continuent allows enterprises running business-critical MySQL database applications to cost-effectively achieve continuous operations with commercial-grade high availability (HA), geographically redundant disaster recovery (DR) and global scaling. This white paper provides a technical overview of Tungsten Clustering as well as its benefits.
We are excited to announce the release of the 13th annual State of Agile report. The widely-cited report includes responses from software professionals around the world who shared the latest trends, practices and values in Agile.
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.
The now expensive nodes you need to support are strained, and the complex data architecture built around Hadoop struggles to bring business insights.
The Museum of London struggled with increasing complexity, particularly due to the management overhead and prolonged recoveries with tape and offsite storage. When they shifted to a virtualized environment and set up a SAN to SAN replication, it was too expensive. Learn how Rubrik helped them enable a faster and more cost-effective DR solution.
This technical reference is intended to help backup admins and DBAs understand the benefits and implementation of Rubrik's Microsoft SQL Server backup solution. Download this white paper to understand common challenges that Rubrik helps SQL Server users overcome, its key capabilities and features, recovery options, advanced SQL functionality support and real-world examples.
To deliver successful business outcomes, enterprises need a powerful data management solution that protects their Oracle database data while delivering business uptime, on-demand access, and self-service automation for their large-scale Oracle environments. Download this white paper for the top three data management challenges for Oracle databases and how to overcome them.
Mainstream enterprise applications built on NoSQL databases need a reliable backup solution to prevent downtime or data corruption as a threat to the business. Modern application, database, and IT teams at well-known Fortune 500 and Global 2000 organizations use Rubrik Datos IO, both to protect their NoSQL applications and achieve their larger strategic priorities for data center modernization and digital transformation. Download this white paper to learn why and how.
This paper examines eight main questions at the intersection of database administration and data privacy. Data controllers — and database administrators (DBAs) working with data controllers — can use the following guidelines to evaluate the tools they’ll use to comply with data protection regulations such as GDPR.
It’s time to make proactive database management and productivity a reality with Toad. Read the tech brief.
Read this white paper to learn:
- Why you need a data strategy first and a hybrid cloud one second
- Why you must see cloud as infrastructure, not data architecture
- Why open source is crucial to success
- How to balance business and IT needs
- How to keep sensitive data secure and compliant
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.
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.
Entrusted with delivering programs and services to millions of citizens dispersed across wide geographies, federal governments are inherently complex and highscale operations. Their agencies have huge workforces to manage, a multitude of facilities and assets to maintain, and massive budgets to monitor and regulate. They have massive budgets to monitor and regulate. With all these moving parts, government agencies face the continual challenge of making well-informed decisions, maintaining operational efficiency, and avoiding waste and fraud.
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".