Cloud native application architectures let you make highly available, massively scalable, globally distributed applications. This O’Reilly technical guide for architects and developers details the most commonly used cloud native design patterns. You will learn how to build cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development.
Kroger, America’s largest supermarket chain with over 2800 retail stores, has experienced tremendous growth in digital sales and e-commerce. In this video, Sriram Samu, VP of Engineering for Customer Technology at Kroger, describes the company’s journey and lessons learned from using YugabyteDB—the open source, high-performance distributed SQL database for transactional applications—to power business-critical microservices used by millions of customers across the United States.
Wells Fargo, one of America’s largest financial services companies with $1.92 trillion in assets, is delivering next generation services to savvy customers. In this video from Yugabyte’s latest Distributed SQL Summit, Chintan Mehta, CIO of Digital Technology and Innovation at Wells Fargo, shares his experiences building an effective and efficient digital ecosystem of capabilities that are safe, secure, resilient, and highly-responsive to change.
GM, one of the world’s largest automobile manufacturers, processes over 30 billion transactions per day from the company’s connected vehicles. In this video from Yugabyte’s latest Distributed SQL Summit, Logan McLeod, Director of Strategic Incubation at GM, explores cutting-edge innovation of the company’s data architecture. He reveals how a distributed SQL database allows GM to achieve continuous availability and linear scalability while enabling developers to build high-value data products quickly.
Data-driven decision-making and the ability to drive meaningful insights from increasing volumes of data is no longer just a competitive advantage: it’s a requirement for business leaders.
However, as the volume and complexity of data grows, data teams still struggle to manage data migration and maintenance, causing new and growing information gaps as well as burnout across the teams. In our ebook, find out what we heard from 450 data professionals just like you.
In this ebook, you’ll learn:
Key trends and pain points facing enterprise data teams
How to spot blind spots in your data as volumes continue to increase and how to turn those gaps into knowledge
The top three challenges of working with different data sources
Why introducing the right technology that can integrate and manage data at scale is the key to winning market, mindshare, and talent
The race to compete for online ad bidding, placement and delivery is being waged on display, video, mobile and in-app. As a result, both speed and accuracy are at a premium, lest your customer go to your competitor for better results. Plus, with online cookie technology on its way out, and regional regulations on their way in, ad tech players must now leverage multiple additional sources of data just to rebuild online profiles, globally, for precise marketing and targeting, while controlling for new compliance needs. In short, the ad tech industry’s insatiable appetite for more data, faster has only grown in recent years, with the need for a real-time data platform.
This whitepaper provides a simplified reference architecture for Demand Side Platform datastores for real-time bidding and campaign reporting using Aerospike as the datastore technology.
If you are considering or planning to deploy data virtualization technology but are indecisive because you are not able to substantiate the tangible benefits of data virtualization, this report is a must read!
Users today need frictionless access to all the data, wherever it is stored, in a transactional database, a data warehouse, or a data lake. A popular new architecture that supports this approach is data fabric. This whitepaper describes how to develop data fabrics using data virtualization. It describes the benefits of this approach. A data fabric developed in this way is called a logical data fabric.
5G is significantly accelerating IoT and real-time services resulting in a huge influx of information that needs to be captured. With it brings a need for a higher performance data layer and a cutting edge data platform to take advantage of this data to provide new digital services and personalized experiences. The risk of churn for Communication Service Providers that don't rise to the challenge are huge.
While compliance requirements increase the number of strict limitations placed on data, the need for quick and easy access remains integral to the productive use of said data.
With data-driven businesses trapped in the middle, can this problem be solved while still meeting each side’s needs? With automated data access control, it can.
In this white paper, you’ll learn:
The distinction between data governance and data access control
Why passive access control models are no longer efficient or effective as cloud platform adoption accelerates
The five pillars of a modern automated data access control model
How organizations across industries are successfully meeting their data governance and analysis needs with automated data access control
The 2020 coronavirus pandemic has driven disruption and turbocharged digital transformation in banking. Disruptors are gaining ground, innovating around both customers' and businesses' needs. While a handful of leading banks are pushing ahead with their digital transformation, others are still struggling to create and execute a coherent transformation strategy. This report explores how digital technologies are changing the industry's customers, competitors, and business and tech priorities globally.
Digital banking is growing rapidly as going digital provides tremendous convenience with 24/7 services and an opportunity to reach more of the world’s population and demographics (i.e. the previously unbanked).
With 42 billion Internet of Things (IoT) devices expected to generate 80 zettabytes of data by 2025 and 5 billion mobile phone users currently generating 2.5 exabytes of data daily, it is no surprise that 95% of businesses cite the need to manage unstructured data as a serious problem for their businesses.
ROI of up to 320% and total benefits of over $9.86 million over three years for the Stardog Enterprise Knowledge Graph Platform.
Stardog commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying its Enterprise Knowledge Graph Platform.
Read this study to learn how several customers turned their data into knowledge, completed their data analytics projects faster, saved on infrastructure costs, and unlocked new business opportunities with Stardog including:
3x faster development of data analytics applications
$2.6 million in avoided infrastructure costs
$3.8 million in time savings for data scientists
$2.4 million in profit from incremental successful data analytics projects
Now, more than ever, businesses want scalable, agile data management processes that can enable faster time to market, greater self-service capabilities, and more streamlined internal processes. Download this report for seven key steps to designing and promoting a modern data architecture that meet today’s business requirements.
Secure your Redshift data warehouse in minutes! Learn more about Auditing, Monitoring, Network Access Control, Encryption and other topics for Amazon Redshift, in our AWS Redshift Security Guide.
Ransomware is getting smarter and the attackers shrewder. How well could you recover your critical data and processes if you became a target?
Learn how to secure your Snowflake data cloud in minutes. This guide will provide you an overview of Snowflake Security and its features as well as a practical guide for using them to their full potential.
An at-a-glance guide highlighting 37 trends and statistics impacting businesses today.
Does your ransomware protection plan contain the 5 essential components needed to be effective against attackers?
With all that’s happened in the past 2 years, it is often observed that there may be more risk in staying with the status quo than moving forward and trying something new. Today, agility, enabled by modern methodologies, such as DataOps and DevOps, and the use of new technologies, like AI and machine learning, is critical for addressing new challenges and flexibly pivoting to embrace new opportunities. As we come to the end of 2021 and look ahead to 2022, the annual Data Sourcebook issue has been designed to put the current data scene in perspective and look under the covers of the key trends in data management and analytics. Download your copy today.
Protecting your data against ransomware involves putting together a multi-layer defensive plan all the way from thwarting such attacks to recovering quickly in the event of a breach. Download this checklist to guide you in building your own comprehensive data protection plan.
Learn to build and deploy production-ready serverless apps and services with Java using AWS Lambda
This hands-on guide includes hands-on tutorials and exercises for building, packaging, testing, and deploying Java-based Lambda code. Ultimately, developers will learn how serverless development can dramatically simplify how they build and scale their applications.
In this complete, 10-chapter book you will learn:
The fundamentals of serverless and functions as a service, using the AWS Lambda platform
How to build and package Java-based Lambda code and dependencies
How to create serverless applications by building a serverless API and data pipeline
How to automate testing for your serverless applications
Advanced techniques for building production-ready applications
In this whitepaper you’ll discover 3 of the coolest ways you can use Talend and AWS to maximize your data’s value in the fastest, most cost-effective manner possible. Plus, learn from AstraZeneca and how they built a data lake using AWS and Talend successfully.
With volumes of data continuing to grow rapidly, organizations feel more and more pressure to scale their log management systems to support business operations.
In 2021, ChaosSearch conducted a research project to better understand how customers are managing their ever-growing volumes of log data, and how they are harnessing it to drive their daily operations.
Download this report to learn key insights and findings, including:
Best practices for log data management, and how organizations assess their performance for each
Real-life customer use cases
Top challenges of log data management
Key investment areas this year
Surprising uses of log data management
Can CloudOps Be Both Stable and Agile?
Smart log analytics holds the key
Each day, the average enterprise’s cloud applications, containers, compute nodes, and other components throw off thousands or even millions of tiny logs. Each log is a file whose data describes an event such as a user action, service request, application task, or compute error.
Cloud operations (CloudOps) teams that study those logs can maintain stability by optimizing performance, controlling costs, and governing data usage. They can stay agile by responding to events that require speed, scale, or innovation. But this requires new approaches to log analytics pipelines. This whitepaper explains:
What CloudOps and log analytics mean
Why traditional pipelines for log analytics break down
How to streamline or re-architect these pipelines
Are open-source data solutions, like the ELK Stack, really as simple and cost-effective as they are purported to be?
Download this white paper to understand how costs are generated and can quickly mount, including: deploying your infrastructure, managing ongoing operations, scaling the stack as data grows, and building support plans.
The Future of Data
A special Raconteur report published in The Times
They say that data is the “new oil,” but companies are going to have to make sure they are drilling for it responsibly or risk damaging repercussions.
This Future of Data report explores the opportunity for companies to craft their own data strategies, the importance of trust and transparency, the power of decision intelligence, and whether companies really need a chief data officer.
Get your free report.
The modern data engineering technology market is dynamic, driven by the tectonic shift from on-premise databases and BI tools to modern, cloud-based data platforms built on lakehouse architectures.
More than the on-premises market that preceded it, the cloud data technology market is evolving rapidly, and spans a vast set of open source and commercial data technologies, tools, and products. At the same time, organizations are adopting multiple technologies to keep up with the scale, speed, and use cases that today’s
data environment demands.
To remain competitive and maximize the value of their data – including sensitive data – organizations are developing DataOps functions and frameworks to varying degrees. DataOps tools and processes enable continuous and automated delivery of data to power BI, analytics, data science, and data-powered products.
The 2022 Data Engineering Survey examined the changing landscape of data engineering and operations challenges, tools, and opportu
Per Gartner, ”Innovations continue to enter the field of data management every day. Their potential benefits are numerous, but challenging to understand and track.” The Gartner Hype Cycle for Data Management, 2021, assesses more than 30 different categories of data management technologies -- including data lakes, multi-model DBMS, and logical data warehouses -- based on their business benefit and years to mainstream adoption and maturity.
The report helps data and analytics leaders plan ahead and make informed decisions relative to emerging and maturing data management technologies.
This Deep Dive report by Eckerson Group takes a look at four cloud data platforms, and defines the index-driven data lake platform in the context of evolving cloud data platforms.
Understanding Cloud Data Platforms
An index-driven data lake platform helps enterprises increase the scale of their log analytics and BI workloads without incurring too many expensive compute cycles. It transforms, queries, and searches data objects to drive effective log analytics. With this approach, ITOps, DevOps, or CloudOps engineers can analyze more IT logs faster in order to manage the performance and reliability of their IT infrastructure.
Going beyond fundamentals, this report will compare the capabilities of ChaosSearch and three other cloud data platforms in the following categories:
Performance and scale
Ease of use
The cost of downtime is higher than ever, amplifying pressure on IT Ops, NOCs, and DevOps to minimize outages.
In our just-released collection of customer case studies, you will see how BigPanda helped each company turn their "what-if" into a reality.
This report will be of value to Infrastructure and Operations teams evaluating how AIOps can improve monitoring, service management and automation tasks with AI-powered anomaly detection, diagnostic information, event correlation, and root cause analysis (RCA).
The need for speed drives enterprises to adopt clouds, containers, micro-services and continuous delivery. The rise of DevOps has created a culture of optionality within organizations. With speed and optionality comes tremendous operational challenges. IT Ops, Site Reliability, DevOps teams have to deal with overwhelming alert volumes, continuous production changes, and dynamic service topologies.
Without enrichment, your AIOps tools will struggle to make sense of incoming data. The AI/ML technology powering your AIOps tools will struggle to create high-quality incidents. Incidents that lack valuable, actionable context result in frequent, long and painful incidents and outages. You will be no better off than you were before your AIOps investment.
Thanks to an explosion of data, exponential increases in computing power and storage capacity, and better algorithms, artificial intelligence (AI) and machine learning (ML) capabilities are poised to revolutionize business processes. These intelligent capabilities will not only underpin increased automation and process optimization but also improve business results with better and faster planning, decision making, and risk forecasting.
Download a free copy of this hands-on guide, developers will learn to build production-ready serverless apps and services on Google Cloud Run
Today’s digital businesses and those moving towards digitization are rapidly embracing event-driven, in addition to historic batch-driven IT architectures. Event-enabling digital enterprises brings new capabilities, but also brings exponential growth in the streaming data volumes to be handled. This new survey offers insights on how enterprises use event-driven architectures, the volumes of streaming data they face, and the approach they are taking with respect to designing streaming applications and deploying streaming analytics.
The cloud sits at the heart of countless innovations and has transformed the way many organizations do business. It has more than proven itself for over a decade through market fluctuations, business model changes and even a pandemic by delivering the flexibility, performance, scalability, and robustness needed to keep companies running with increasingly greater efficiency. Remote work has rapidly become widespread, and the cloud — with its anywhere, from any device, delivery approach — is a critical component of supporting this new normal.
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.
By properly using indexing, you'll make your databases run efficiently, allowing for fast data retrieval. But identifying and building the correct indexes isn't always a straightforward process. This MSSQLTips white paper will help by showing you how to overcome common indexing challenges.
Are you under pressure to release changes faster? Without the right continuous integration tools, it’s hard to keep up. But what if you could easily integrate database development and change management into your DevOps pipeline? The result would be continuous database operations. With the expert advice in this tech brief, you'll learn how to use end-to-end solutions to bring database into DevOps. You’ll see how continuous integration tools can help you reduce risk and accelerate release cycles.
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.
The five SQL query optimization tips in this e-book comprise a method for tuning your SQL Server queries for higher speed and better performance. By monitoring wait time, reviewing the execution plan, gathering object information, finding the driving table and identifying performance inhibitors, database professionals like you can improve performance in your database environment.
Database professionals agree – SQL Server performance tuning is hard. And on top of that, it never stops because complex database environments are always changing with upgrades, application updates and queries. It often feels like as soon as you get one query optimized, there’s another one right behind it that’s eating CPU time or clogging
memory or otherwise slowing down the entire database. Then, add to that, the instances when the latest SQL Server version itself has made performance worse instead of making it better as promised.
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.
It finally happened: Your CIO has told you to prepare for the cloud migration of your organization’s databases. The digital transformation process is lengthy and riddled with the risks of moving your on-premises databases to the Microsoft Azure SQL database. Where do you start?
Quest offers a variety of information and systems management tools, explained in our white paper that will guide you along the migration path.
In this paper, we’ll explore these technical challenges in more detail, and then we’ll present reasons why Foglight® for Databases by Quest is the best choice for a monitoring solution that can help organizations overcome their challenges and move forward with transformation initiatives.
CIOs and senior IT leaders are business enablers tasked with delivering products and services to customers and employees. Many of today’s products and experiences are built on data, but little attention is often paid to the databases that make them possible. Quest Software offers tools that maximize database performance and identify issues before they impact users.
How Dataware is leading the data revolution and solving data complexity by decoupling data from apps.
Discover how Dataware makes it possible for apps and users to collaboratively manage data without creating new silos, copies, and integrations. In this white paper, learn how Dataware blends the benefits of all the other data strategies into a unified approach for building data-centric apps.
Why other data and application integration solutions and strategies just don’t cut it
What makes Dataware different from Data Warehouses, Data Lakes, MDM, Knowledge Graphs, etc.
How Dataware solves all the pain points that make app-centricity a problem
4 quick ways to begin leveraging Dataware to solve real-world problems
o User-managed data collaboration platform
o Application augmentation (CRM, ERP, HRMS)
o Digital Integration Hub / Data Fabric
o Cross application persistence and controls as a service
How this new category simplifies data collaboration for maximized business agility.
Collaboratively manage data without creating new silos, copies, or integrations.
Data is one of the most valuable resources for an organization. Yet, it's difficult to extract intelligence from it because data management and app development are traditionally centered around applications. The result is data silos, lack of control, and numerous integrations.
Discover why Dataware is different from other data management solutions and how moving to data centricity can improve your IT capacity -- bringing agility to your business.
In this eBook, learn how Cinchy's Dataware Platform can help you:
Eliminate the need for data integration every time you buy or build applications
Reduce the time and money wasted on data integration
Manage data as a linked network allowing for real-time data collaboration
In keeping up with the demands of a digital economy, organizations struggle with availability, scalability, and security. For users of the world’s most popular enterprise database, Microsoft SQL Server, this means evolving to deliver information in a hybrid, multi-platform world. While the data platform has long been associated with the Windows Server operating system, many instances can now be found running within Linux environments. The increasing movement toward cloud, which supports greater containerization and virtualization, is opening platform independence in ways not previously seen, and enterprises are benefitting with greater flexibility and lower costs. Download this special white paper today to learn about the era of the amplified SQL Server environment supported by the capabilities of Linux and the cloud.
From the rise of hybrid and multi-cloud architectures to the impact of machine learning, automation, and containerization, database management today is rife with new opportunities and challenges. Download this special report today for the top 9 strategies to overcome performance issues.
New cloud offerings are coming to market every day, and there are a number of fundamental principles that you should use to evaluate their appropriateness for your particular enterprise and applications. However, not all services are built to the same standards, nor will they necessarily meet your needs.
This eBook will help you understand the top ten principles of a cloud backup service, so you can make an informed decision by applying the following cloud-first principles:
Reduce the cost and complexity of data protection, lower TCO by up to 50%
Maximize storage efficiencies with best-in-class deduplication
Accelerate and protect cloud projects, increase agility with on-demand scale
Defend against ransomware attacks and meet data compliance requirements
Discover the primary principles of a cloud backup service that any organization should consider when seeking out a cloud data protection solution.
PostgreSQL is an incredibly reliable open-source database technology that continues to grow in popularity with its users whether its supporting enterprise-grade workloads and commercial databases. It’s flexible, you can use it for SQL and NoSQL workloads, and has high availability. On-premises PostgreSQL deployments make it difficult to harness the true potential of these databases.
Migrating PostgreSQL to a cloud platform like Amazon Aurora can deliver a host of benefits including increased flexibility, greater capacity, improved security and automation to start. It also requires a thorough understanding of your current on-premises databases, your application requirements, your database migration goals, and your technical resources.
Download Datavail’s white paper to discover why your PostgreSQL database should live on Amazon Aurora and learn:A brief overview of AWS & Amazon Aurora
A brief overview of AWS & Amazon Aurora
15 benefits of an Amazon Aurora migration
In a hybrid, multi-cloud world, data management must evolve from traditional, singular approaches to more adaptable approaches. This applies to the tools and platforms that are being employed to support data management initiatives – particularly the infrastructure-as-a-service, platform-as-a-service, and SaaS offerings that now dominate the data management landscape. Download this special report today for new solutions and strategies to survive and thrive in the evolving hybrid, multi-cloud world.
Object storage, often referred to as object-based storage, is a data storage architecture for handling large amounts of unstructured data. Unstructured data is data that does not conform to, or cannot be organized easily into a traditional relational database with rows and columns. Today’s Internet communications data—email, videos, photos, web pages, audio files, sensor data, and other types of media and web content (textual or non-textual)—is largely unstructured.
Microsoft Office 365 is used widely and gaining new customers every day. While Microsoft Office 365 does offer a variety of system availability and data protection capabilities, it doesn’t really satisfy all of today’s stringent data protection, disaster recovery, legal and compliance requirements. Data can be damaged or lost based on several unforeseen situations, including accidental changes and deletions, as well as malicious activities like viruses and ransomware. And there are rare situations where Office 365 applications and data are just not available, impacting business performance, customer service and staff productivity.
Exponential data growth is something every IT team faces. It’s natural to worry about exceeding capacity, wondering how a solution will scale or perform over time. The best approach is to use a software solution that’s not limited by hardware, allowing you to turn generic hardware into a robust, infinitely-scalable storage pool.
To minimize cloud complexities and costs, there are three best practices organizations can follow when storing their backup data in the cloud.
The decision to adopt the cloud has been made. Your company exercised caution by electing to first adopt the foundational compute and storage services that AWS and Azure offer to provide the fastest journey to the cloud. It also took the appropriate next steps of managing user access to mitigate cost overruns in the cloud. Now it’s time to start moving your company’s applications and backup data to the cloud.
Ransomware has become a ‘cyber-pandemic’ that shows no signs of diminishing anytime soon. Your ransomware recovery capabilities must be up to the challenge, as these attacks have gone well beyond just another cybercrime to becoming a threat to our society as well.
Just recently, we have seen some high-profile ransomware attacks lead to serious shortages in oil, gas and meat supplies as well as interruptions to transportation systems and countrywide health systems. And those are only a small part of the ransomware iceberg that is visible as many organizations avoid publicly disclosing that they have been compromised.
Multiple backup solutions, clouds, and cloud storage tiers can all lead to escalating cloud storage costs and complexity. These, in turn, hamper an organization’s ability to perform a DR in the cloud. Three new best practices exist that account for these new variables. By adopting them, organizations better position themselves to more quickly perform DR in the cloud while incurring lower costs.
Protecting enterprise data in a complex IT environment is challenging, time-consuming, cumbersome and often incomplete. First of all, it’s not uncommon for backup and recovery technologies to be complicated to install, often requiring help from the vendor’s
professional services staff, which adds to your total cost of ownership (TCO). Then learning to manage and maintain them for your
ongoing backup and recovery needs can take a lot of time, effort and patience — especially if your environment has multiple operating systems and applications and is heavily virtualized.
Inside the report, organizations share their:
Number and types of Kubernetes workloads deployed
Concerns and challenges around adopting Kubernetes
Multi-cloud adoption timelines
Serverless adoption strategies
Analytic vs. transactional database needs & usage
Production environment preferences
This new book from O’Reilly will teach developers, architects, and devops teams how to build, optimize, and manage applications that run on CockroachDB. It includes specific guidance for anyone transitioning from a monolithic database (e.g., MySQL or PostgreSQL) to a distributed architecture, as well as practical examples for anyone more familiar with NoSQL systems.
HPE commissioned Forrester Consulting to evaluate the current state of HPC and AI infrastructure. Forrester conducted an online survey with 464 AI and/or HPC, global decision-makers and practitioners.
The study revealed that combining AI and HPC enhances the value of both, leading to increased business agility, innovation, and competitive differentiation, with shared infrastructure being a critical component of unifying both disciplines.
Mark III Systems is a leading digital and IT transformation solutions provider with a long, rich history of providing “full stack” technology solutions to enterprises and service providers across North America.
Since 2016, the State of AI and Machine Learning Report has given insight into the adoption, implementation and scale of AI across industries. This large-organization study of senior business leaders and technologists details where organizations are within their AI journey — from the types of data they leverage to the tools they use and budgets they have.
Overall, this report will help you understand the broader context of your AI work, what your peers are experiencing, and what dials to turn for AI success.
Download the report to learn:
How AI budgets have increased YoY
Why many are accelerating their AI strategy as a result of COVID-19 in 2020 and into 2021
Why companies are reporting a higher commitment to data security and privacy
Many companies ingest more than a petabyte of data but struggle to turn it into high-quality training data for machine learning algorithms that fuel core business. When coupling AIOps with professional services to guide the entire AI-creation journey, AI and ML will increase efficiencies and innovation around volume, quality, and speed requirements for training data.
Download the AIOps for Business Leaders white paper to learn about:
How AIOps with training data can power your tech stack, which translates to scalable AI
Why machine learning and the big data flywheel will create momentum that takes programs from experiment, to program, to core product
Key data-labeling resources for AIOps
There's a saying of 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.
Pulling from over 20 years of experience, we break down how to approach training data, starting with raw data and annotating and labeling it so that it can be used to power the most ambitious projects. It's how we help some of the most innovative companies in the world. This guide will give you a few of the lessons we’ve learned along the way.
In the Essential Guide to Training Data we’ll cover everything you need to know about creating the training data necessary to drive successful machine learning projects, including:
Why having a lot of big data isn’t the same as having labeled data
How to determine which labels to use to evaluate your success
Where to find some great open datas
Your business relies on data. And your users want it fast. IT and data teams are seeing a dramatic increase in data demands, on top of a massive explosion in data volume and extensive array of new capabilities.
In an increasingly competitive and noisy business landscape, Managed Service Providers (MSPs) need to stand out – and not just for the expertise of their IT consultants. Security, compliance, and time-to-productivity issues are very much on every customer’s mind, and the MSPs that come out on top in today’s marketplace will be those that deliver on those issues.
Even as employees return to the office, most organizations say they’ll continue to facilitate remote work through a hybrid workforce. This is an unprecedented period – one ripe with opportunities to harness global talent, but also serious IT risks. That’s why CISOs everywhere need a clear, effective plan for scaling and securing a blended workforce of both in-office and remote employees.
Cinchy CEO and Co-Founder, Dan DeMers, and Concentra Bank COO and SVP, Neal Oswald, share thought-provoking insights on why Dataware and Data-Centricity are key components of successfully unlocking a business and technical advantage.
Many of today’s popular data strategies provide limited ROI because they don’t change how applications manage data. Trends such as data marts, data warehouses, data lakes, master data management, etc. all have limitations and require costly integration with applications.
There is a major shift away from app-centric approaches toward modern data-centric architectures. Why?
Data management is changing. It’s no longer about standing up databases and populating data warehouses; it’s about making data the constant fuel of the enterprise, accessible to all who need it. As a result, organizations need to be able to ensure their data is viable and available. Download this special report for the key approaches to developing and supporting modern data governance approaches to align data to today’s business requirements.
In a head-to-head comparison by GigaOm, Immuta’s attribute-based access control (ABAC) required 75X fewer policy changes than Ranger’s role-based access control (RBAC) to accomplish the same security objectives. The downstream impact of this is enormous, with the attribute-based approach increasing cloud ROI by more than $300,000.
In this report, you’ll learn:
How Immuta compares to Apache Ranger
What’s so special about ABAC, in both simple and advanced access control scenarios
How data access policy changes translate to time and opportunity costs
Ready to deliver trusted data to your analytics platform in real time – and save millions of dollars in the process? Qlik commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Qlik’s Data Integration platform. In this study, you can read the key findings, drill into the details, and get a framework for evaluating the potential for savings in your own organization.
AI and machine learning adoption is growing faster than ever. However, many ML initiatives and use cases fail despite the high level of investment. Read this ultimate guide to understand the requirements for success, deployment strategies, and selection criteria for scaling ML use cases for the enterprise.
Today’s emerging architecture is built to change, not to last. Flexible, swappable systems, designed to be deployed everywhere and anywhere, and quickly dispensed at the end of their tenure, are shifting the dynamics of application and data infrastructures. The combination of containers and microservices is delivering a powerful one-two punch for IT productivity. At the same time, the increasing complexity of these environments brings a new set of challenges, including security and governance, and orchestration and monitoring. Download this special report for guidance on how to successfully leverage the flexibility and scalability that containers and microservices offer while addressing potential challenges such as complexity and cultural roadblocks.
The ChaosSearch Data Platform is the first viable alternative to ELK, delivering the same level of advanced log management and analytics while solving the underlying cost, complexity, scalability, and reliability issues inherent in ELK stack environments today.
Ready to embrace a better way?
This paper provides the details you need to build a rock-solid business case for switching from your existing ELK stack to an environment built for maximum efficiency.
Calculating the TCO of your existing ELK environment
Projecting the cost benefits of a shift to an innovative, cloud-based Data Platform
Constructing the business case to switch to ChaosSearch
Finding the factors or combinations of factors driving repeatable business is critical for building a successful new business. But with only one analyst monitoring and analyzing a company’s data, how can any business surface those insights in time to act? Fractory, the one-stop-shop for on-demand manufacturing services, faced this exact issue. Learn why Fractory turned to Sisu to get a complete view of the drivers of their e-commerce platform and identify where to act to drive repeatable business.
Global power generation companies are facing major disruptions as the means by which power is created, maintained, and distributed evolve. In the face of these changes, power generation companies must be willing to adapt to new opportunities and trends to remain operable and avoid real existential risk. The organizations that act now to supercharge their operations with data won’t just keep up, but stay ahead of the pack.
Verdantix and Cognite explain how to solve DataOps challenges in heavy-asset industries with technology.
Utilities are gathering more data than ever—from equipment, sensors, business processes, customer interactions and third-party sources. This data can drive down risk from operational change by helping utilities understand patterns and trends, and also by increasing flexibility and nimbleness. For utilities to become driven by data, rather than drowning in it, they must effectively and efficiently deliver the right, meaningful data to the personnel and applications that need it.
Learn from BCG and Cognite how two symbiotic perspectives on data and organizational change together enable effective enterprise transformation.
We believe data, algorithms, and software should power industry, freeing human creativity to shape a profitable, safe, and sustainable future. Today, heavy-asset industries like oil and gas, manufacturing, shipping, and power have reached a digitalization tipping point. Increasing access to data has made data handling a key differentiator.
Databases are vital to any organization. So with an increase in data growth, and a complex mix of both relational and non-relational database engines, protecting and managing applications has become a challenge for DBAs. Many organizations are now running Amazon RDS engines on AWS, along with Oracle, or SQL either on-premises or in the cloud. So how can you effectively protect these critical DB resources?
In this session, learn how DBAs can get complete data protection for databases across hybrid and cloud environments while providing visibility, control, and automation to meet SLAs, lower costs, and increase agility, all from a single platform.
Database architecture is an important consideration in any MarTech and AdTech strategy. If you’re an application developer or technical executive, learn why Aerospike’s innovative Hybrid Memory Architecture is the defacto standard among the world’s leading advertising and marketing technology organizations.
Adform replaces Cassandra with the Aerospike database to achieve predictable low latency at scale for its multi-screen marketing platform.
The Advertising industry historically has been built upon cookie technology whereby advertisers can glean very detailed user profile information in order to segmentation and target advertisements, profitably. However, with Google’s proposal to eliminate 3rd party cookies and compliance mandates notably in the US and EMEA, the need to create a new, better alternative to cookie technology is upon us.
The Trade Desk, the world's largest independent programmatic advertising DSP, needed to migrate from cloud back to on-prem for one of its largest Aerospike clusters. There were multiple catalysts for the change including a new business requirement, a new, tailor-built site, as well as risk and capacity challenges. This session will uncover the findings and methods used to gain confidence in the move.
Leading Ad Tech companies use the Aerospike non-relational NoSQL database to improve customer engagement, campaign effectiveness and top-line results.
Enterprise data fabrics offer the new way forward. The data fabric weaves together data from internal silos and external sources and creates a network of information to power your business’ applications, AI, and analytics. Quite simply, they support the full breadth of today’s complex, connected enterprise.
This is the first demonstration of a massive Knowledge Graph that consists of materialized and virtual graphs that span multiple cloud platforms. We show that it is possible to have a one trillion-edge Knowledge Graph with sub-second query times without storing all the data in a central location. This capability has the ability to usher in a new era where the Knowledge Graph is a powerful component of company profitability and competitive advantage.
For years, TDWI research has tracked the evolution of data warehouse architectures as well as the emergence of the data lake. The two have recently converged to form a new and richer data architecture.
Within this environment, data warehouses and data lakes can incorporate distinct but integrated, overlapping, and interoperable architectures that incorporate data storage, mixed workload management, data virtualization, content ETL, and data governance and protection.
This TDWI Best Practices Report examines the convergence of the data warehouse and data lake, including drivers, challenges, and opportunities for the unified DW/DL and best practices for moving forward.
While cloud data warehouses like the Snowflake Data Cloud make it easier and less expensive to manage large, rich data, most companies still face an analytics bottleneck. To effectively turn all that data into better decisions, we need better analytics tools purpose-built for Snowflake-scale data. Download this ebook to discover three quick wins to unlock the value of your Snowflake data for a faster, more comprehensive analysis.
This paper will guide DBAs, infrastructure admins, IT managers, IT security admins and IT directors along the path of migrating databases to Microsoft Azure SQL Database. It breaks the process into three phases—planning, migration and maintenance—and explores the logical steps in each phase. IT professionals will see how to smoothly navigate each phase and use Quest products to reduce the risks to system performance
and service levels, from start to finish.
Building a business case for consolidating taxonomies to create a unified marketing data governance practice is essential for marketing to deliver personalization at scale and accurately measure marketing campaign ROI.
Data teams struggle to architect, build, and operate data systems to meet rapidly expanding business requirements. Data observability seeks to address this new world of unprecedented data complexity with a systematic approach that builds on its predecessor technology, application performance monitoring (APM). It monitors and correlates data events across the data pipeline, data, and infrastructure layers, enabling business owners, DevOps engineers, data architects, data engineers and site reliability engineers to detect, predict, prevent, and resolve issues—in an automated fashion—that would otherwise break production analytics and AI. To succeed with data observability, data analytics leaders must assemble and prioritize requirements, then select a comprehensive data observability product that minimizes custom integration work. They should tackle small, achievable observability projects first, enlisting a cross-functional team of contributors to focus on key pain points, such as perfo
The database is no longer just a database. It has evolved into the vital core of all business activity; the key to market advancement; the essence of superior customer experience. In short, the database has become the business. What role does the new data environment—let’s call it “the era of data races”—play in moving enterprises forward? Download this special report to learn about the ways emerging technologies and best practices can support enterprise initiatives today.
Companies embarking on significant Hadoop migrations have the opportunity to advance their data management capabilities while modernizing their data architectures with cloud platforms and services. Consequently, having a comprehensive approach reduces the risk of business disruption and/or the potential for data loss and corruption. Download this special eGuide today to learn the do’s and don’ts for migrating Hadoop data to the cloud safely, securely, and without surprises, and key architecture strategies to follow.
As data lakes increasingly make their move to the cloud, it’s easier than ever to set up, maintain, and scale storage to meet your all your analytic needs. But, with all the platforms out there, it can be hard to know exactly which is right for you.
This study guide goes in-depth on the topics you need to pass the CKAD exam from the Cloud Native Computing Foundation. Learn core principles of services and networking, and gain a thorough understanding of state persistence and volumes. Practice with real sample exercises.
Source-to-target time reduced from days to seconds. 60+ million rows replicated hourly. And improved data delivery by 400%. These are just a few of the outcomes that leading organizations have seen after solving their data integration challenges with Qlik. Around the world, Qlik is helping enterprises in every industry streamline, accelerate, and automate their data pipelines to deliver in-the-moment data for immediate action.
It’s not every day a company launches a billion-dollar product. Samsung’s Mobile team does so at least twice a year. And with mounting pressure from lower-quality competitors and a rapidly changing global marketplace, it’s critical to understand the complex galaxy of variables that can impact success.
The marketing and analytics teams at Samsung had access to a wealth of dashboards and market reports, but digging even one level deeper into the data could take weeks to answer a single question. When the team needed to understand upgrade preference across demographics, device profiles, carrier loyalty, and more, they needed answers fast.
Read the case study to learn how Samsung is able to successfully address critical business decisions with an augmented intelligence solution.
The future of analytics in all its fast, proactive, comprehensive glory is in the cloud. But, to successfully unlock the speed and agility of your team, there are key data analytics platforms, data structures, and processes you’ll need to invest in first to get truly proactive in your use of data. Learn how to build the analytics stack of your dream with this blueprint for a better, faster, and more efficient cloud-native data architecture.
Sisu is a 2021 Gartner Cool Vendor for Analytics and Data Science. Gartner recognized Sisu based on evaluation in the areas of Augmentation, Contextualization, Composability and Automation.
Gartner Cool Vendors in Analytics and Data Science, Julian Sun, David Pidsley, Shubhangi Vashisth, James Richardson, May 10, 2021
The GARTNER COOL VENDOR badge is a trademark and service mark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular
Businesses need better quality data and analytics to drive decisions and respond to change. Read Gartner’s Top Trends in Data and Analytics 2021 to learn the critical investments companies can’t afford to ignore to build a disruption-ready and resilient organization.
Gartner, Inc. [Top 10 Trends in Data and Analytics, 2021], [Rita Sallam, Donald Feinberg, Pieter den Hamer, Shubhangi Vashisth, Farhan Choudhary, Jim Hare, Lydia Clougherty Jones, Julian Sun, Yefim Natis, Carlie Idoine, Joseph Antelmi, Mark Beyer, Ehtisham Zaidi, Henry Cook, Jacob Orup Lund, Erick Brethenoux, Svetlana Sicular, Sumit Agarwal, Melissa Davis, Alan D. Duncan, Afraz Jaffri, Ankush Jain, Soyeb Barot, Saul Judah, Anthony Mullen, James Richardson, Kurt Schlegel, Austin Kronz, Ted Friedman, W. Roy Schulte, Paul DeBeasi, Robert Thanaraj], [February 2021]
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission.
Download KgBase’s Vision Paper to learn how to build the ultimate enterprise knowledge system: a federated mesh of independently maintained no-code knowledge graphs.
To successfully make the journey to a data-driven enterprise, businesses are under pressure to extract more value from their data in order to be more competitive, own more market share and drive growth. This means they have to make their data work harder by getting insights faster while improving data integrity and resiliency, leverage automaton to short cycle times and reduce human error, and adhere to data privacy regulations. DataOps opens the path to delivering data through the enterprise as its needed, while maintaining its quality and viability. In this thought leadership paper, we will provide perspectives on the advantages DataOps gives to stakeholders across the enterprise, including database administrators, data analysts, data scientists, and c-level executives.
If you run Oracle, database upgrades and migrations are inevitable. While there are real benefits to performing these upgrades and migrations, changes of this scale introduce equally real risks of unforeseen issues and downtime. Native Oracle solutions provide some protection, but all have trade-offs or leave remaining risks.
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.
This technical brief examines those options in detail. You’ll see the differences — especially in recovery time — and discover how you can use SharePlex® by Quest® to stick with Oracle SE2 without putting your high availability and disaster recovery strategies at risk.
Even with the emergence of new technologies such as Apache Kafka and Spark, the ability to effectively support the speed and scalability requirements of real-time data can be difficult for many enterprises. In a recent survey of DBTA subscribers, nearly half indicated that streaming data is a top priority. However, less than half were confident that their infrastructure is capable of handling the demands. Download this special report for best practices to ensure confidence and performance in developing real-time analytical capabilities.
So what makes a good API? The same thing that makes products outstanding – design.
This ebook takes an in-depth look at the entire ML lifecycle and reveals how your organization can power more trusted and explainable AI use cases. Download it now to learn how to take control of the ML lifecycle—so you can build and scale practical AI use cases to solve your actual business problems.
Learn how each embedded use case increases the value of analytics for that company, and find out how you can do the same for your organization. In this paper, you'll learn how 5 companies overcame the challenge and offered their customers an analytic solution that added value to the original solution.
The challenge that each company faced
How each embedded solution was implemented
How long it took to implement the solution and achieve ROI
The main benefit of the embedded solution for the company and their customers
With more data than ever flowing into organizations and stored in multiple cloud and hybrid scenarios, there is greater awareness of the need to take a proactive approach to data security. Download this special report for the top considerations for improving data security and governance from IT and security leaders today.
There are many types of disruption affecting the data management space, but nowhere will the impact be more substantial than at the edge. Leading operations moving to the edge include smart sensors, document and data management, cloud data processing, system backup and recovery, and data warehouses. Download this special report for the key transformational efforts IT leaders need to focus on to unlock the power of IoT and the edge.
How to integrate cloud backup with Oracle RMAN to reduce costs and complexity
Oracle’s RDBMS has been the gold standard for managing structured data for decades, and today most major businesses rely on it for their mission-critical applications. Yet maintaining relational database integrity during a backup can be complex. It takes keeping physical parameters secure and database processes consistent as well as auditing data trails and performing risk-based validation. To reduce this complexity, Oracle introduced Recovery Manager (RMAN) as its standard tool to handle basic backup and restore functionality.
Read this white paper to learn about the fundamental backup concepts applicable to RMAN and how Druva works with an Oracle image copy and incremental merge features to securely protect an Oracle database in the cloud. As an Oracle Backup Solutions Program (BSP) partner, Druva also gives additional control of data protection to your backup admins and teams while it provides:
SSIS is a popular and mature tool for performing data movement and cleanup operations. In the Microsoft ecosystem, SSIS is one of the most common extract, load, and transform (ETL) tools in use today. SSIS is powerful and configurable, yet surprisingly easy to use.
Execution plans provide a rich source of information that can help us identify ways to improve the performance of important queries. Sometimes, performance will still not be good enough, even after multiple performance tuning techniques are applied.
We've curated several articles to help you understand the plans themselves and the optimizer's strategy behind them. Learn how to effectively optimize your queries from industry experts.
Is your application easy to monitor in production? Many are, but sadly, some applications are designed with observability as an afterthought.
Quarkus can save as much as 64% of cloud resources as compared to Framework A when running in native mode and 37% when running on a JVM. Read how.
Learn four reasons developers should try Quarkus, a modern, Kubernetes-native Java framework.
Cloud-native application development is a key part of open transformation. By focusing on technology, processes, and people, you can deploy innovative, open approaches that support business agility, transformation, and success. Align your cloud technology with your business needs.
Learn how to build application environments for reliability, productivity, and change.
O’Reilly provides reusable Kubernetes patterns so containers can improve rapid app development. Learn how to use Kubernetes to support cloud-native app development.
This O’Reilly e-book explains how to build Kubernetes Operators using SDK and the Operator Framework. Learn how Operators are used to automate the app life-cycle.
In addition to data being more diverse, distributed, and dynamic, a growing number of organizational roles work with data daily to complete tasks, make decisions and affect business outcomes. This tide of users is increasing the demand for data consumption throughout organizations. The data must be accessible from anywhere people are working but controlled to ensure the data is being used by the right resource and for the right reason.
Enter the cloud-native data warehouse to meet these demands, take advantage of cloud scale and elasticity, and reclaim control of data in the cloud. Cloud-native data, data lakes, and data warehouses require cloud-native data integration solutions that can also take advantage of cloud scalability and elasticity to help calm the storm.
Download this Technology Spotlight by Stewart Bond, Research Director of Data Integration and Data Intelligence Software at IDC, to learn about the benefits of cloud-native data integration, the trends surrounding it
The only constant is change, especially when it comes to technology. And 2020 was a year of rapid and sometimes tumultuous change. Some changes will be permanent and will affect the trends we’ll see in the coming year. Our need to work 100 percent remotely, and our need to have data accessible to us no matter where we worked, accelerated the move to the cloud for many organizations. IDC predicts that 80 percent of enterprises will speed up their shift to the cloud.
As we leave behind a year that generated— and required—so many changes in the way we work with data and each other, let’s take a look at the data integration trends you can expect to see in 2021.
This eBook highlights how four leading financial services organizations are accelerating their speed-to-insight with fast analytics to drive some of the most compelling and mission-critical use cases today.
-Real-Time Fraud Detection
Enabling real-time fraud detection in under 50 milliseconds with a modern real-time data infrastructure.
-Modernizing the Wealth Management Experience
Delivering premium data experiences for 40,000 users requires reliable ingest and query performance under extreme market conditions.
-Smart Portfolio Management for Reduced Risk
Dramatically improve the performance of analytical engines to continuously assess risk and optimize portfolio performance, to recommended actions in real-time.
-Operational Analytics for Digital Transformation
Deliver near real-time visibility into business performance and enterprise operations across finance, support, sales, marketing, and other business functions.
When it comes to thwarting cyberattacks, every millisecond matters.
Nucleus Security needed an underlying database that was truly fast and scalable to power their vulnerability management platform.
With SingleStore, they were able to dramatically improve performance by 50x, at ? the costs of the alternatives.
Join us for a 45-minute interactive session, with Nucleus Security & SingleStore, to learn more about:
• How SingleStore reduced Nucleus Security’s vulnerability scan from hours to minutes
• Why Nucleus Security chose SingleStore over MariaDB and other alternatives
• How they were able to achieve this without any architectural changes
Scott Kuffer, Co-Founder & COO, Nucleus Security
Domenic Ravita, Field CTO, SingleStore
Speed & Scale
As an application developer speed and performance at scale are key to delivering an optimal customer experience. It's even more critical in Cybersecurity where your application or platform needs to ingest and process millions of events every second.
At Nucleus Security, Scott Kuffer, the COO and his development team were constrained by performance and scalability bottlenecks with MariaDB to power their vulnerability management platform. They needed a relational database that was truly fast and scalable that enabled them to do ultra-fast data ingestion at scale while running thousands of low-latency queries in parallel.
Learn more about how Nucleus Security, with SingleStore, was able to increase in the number of scans processed in one hour by 60x, with 20x improvement in performance of the slowest queries.
For organizations with growing data warehouses and lakes, the cloud offers almost unlimited capacity and processing power. However, transitioning existing data environments from on-premises systems to cloud platforms can be challenging. Download this special report for key considerations, evolving success factors and new solutions for enabling a modern analytics ecosystem.
Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language models are able to represent the relationships and accurate meaning of data instead of simply generating words based on patterns. Download this special report to dive into key uses cases, best practices for getting started, and technology solutions every organization should know about.
For a variety of reasons, organizations are moving their workloads to the cloud. Our research shows that one-third of organizations have their primary data lake platforms in the cloud and most organizations (86%) expect the majority of their data to be in the cloud at some point in the future. Those organizations that already have the majority of their data in the cloud report they gained a competitive advantage, decreased time to value, and improved communication and knowledge sharing in their organizations.
Paul Scott-Murphy, VP of Product Management at WANdisco, demonstrates using LiveData Migrator to migrate actively changing data from an on-premises Hadoop environment to AWS S3, and leveraging the WANdisco UI to manage and monitor the migration.
AWS and WANdisco show how GoDaddy easily automated their big data migration with zero business disruption—and how you can too.
In this LiveData Unplugged session Tony Velcich, Sr. Director of Product Marketing at WANdisco, speaks with Steve Kilgore, Vice President, Field Technical Operations at WANdisco. Steve discusses the top 10 list of cloud data migration mistakes he and his team of global solution architects have witnessed while working with customers on their cloud data migration initiatives.
This is the inaugural session of the LiveData Unplugged series. In this first episode Tony Velcich, Sr. Director of Product Marketing at WANdisco, talks to Daud Khan, VP of Corporate Development at WANdisco, about WANdisco’s LiveData strategy. In this session they cover what is a LiveData strategy, and the benefits it provides to organizations moving their on-premises data lakes to the cloud and want to ensure data consistency across multiple distributed environments.
The adoption of hybrid and multicloud environments is accelerating; boosted by a mounting urgency for enterprises to digitally transform into more efficient and agile operators. At the same time, the challenges of managing, governing, security and integrating data are growing in step. Download this special DBTA report to navigate the key data management solutions and strategies for surviving and thriving in the growing hybrid, multi-cloud world.
Microsoft SQL Server sites are challenged with delivering greater capabilities at the same level of budget and staff. Backup and recovery processes have been in place for decades and so have many of the solutions and approaches offered. These solutions are no longer a match for the pace and scope of today’s digital enterprises. To create an environment that can effectively support digital transformation, enterprises need to move toward providing backup and recovery of databases that spans across on-premises and the cloud.
Today’s Oracle DBAs have a lot on their plate: a growing number of databases, expanding data volumes, not enough time and resources, pressure to do more with less, and the need to manage database protection across cloud and on-premises environments. These are all reasons why you need modern backup and recovery strategies that provide automation and access to data to drive other business needs.
The popularity of the Oracle Unlimited Licensing Agreement (ULA) has grown over the last decade and doesn’t seem likely to decrease. Whether your organization simply needs more licenses (more seats) or you’ve been audited and a ULA is being recommended to avoid this happening again in the future, it’s absolutely critical to understand when a ULA is a smart investment, when it’s a terrible idea, and how to best navigate the pitfalls of the complex world of licensing.
Read this Cloudera Special Edition of Production Machine Learning for Dummies to learn what’s needed to succeed with production ML and how to successfully apply a production ML approach at scale in your enterprise.
Read this whitepaper from Blue Badge Insights to better understand how Cloudera Machine Learning MLOps capabilities and features were built to address industry and customer needs.
From the rise of hybrid and multi-cloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges, and opportunities. The demand for fast, wide-ranging access to information is growing. At the same time, the need to effectively integrate, govern, protect, and analyze data is also intensifying. Download this special report for the top trends in data management to keep on your radar for 2021.
Datavail recently conducted a cloud adoption industry benchmark survey where hundreds of companies responded to questions and offered a wealth of insight into the cloud landscape. Download the white paper to learn more about the results and the 2021 cloud trends in order to refine your cloud strategy with lessons learned and best practices.
DataOps is now considered to be one of the best ways to work toward a data-driven culture and is gaining ground at enterprises hungry for fast, dependable insights. Download this special report to learn about the key technologies and practices of a successful DataOps strategy.
The move to modern data architecture is fueled by a number of converging trends – the rise of advanced data analytics and AI, the Internet of Things, edge computing, and cloud. Both IT and business managers need to constantly ask whether their data environments are robust enough to support the increasing digitization of their organizations. Over the past year, requirements for data environments have been driven largely by cost considerations, efficiency requirements, and movement to the cloud. Download this special report for emerging best practices and key considerations today.
Now, more than ever, the ability to pivot and adapt is a key characteristic of modern companies striving to position themselves strongly for the future. Download this year’s Data Sourcebook to dive into the key issues impact enterprise data management today and gain insights from leaders in cloud, data architecture, machine learning, data science and analytics.
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.
Melissa has a variety of tools available to clean, validate and enhance the Contact dimension in your SQL Server data warehouse. Specifically, Melissa’s suite of SSIS Data Quality Components can be leveraged for this task. The Melissa SSIS components are plug and play; you simply drag and drop the components onto the Data Flow, configure the component properties, and you are ready to go. There is no coding required.
The critical role of data as fuel for the growing digital economy is elevating data managers, DBAs, and data analysts into key roles within their organizations. In addition, this rapid change calls for a three-pronged approach that consists of expanding the use of more flexible cloud computing strategies, growing the automation of data environments, and increasing the flow of data and collaboration through strategies such as DevOps and DataOps. Download this special report today to better understand the emerging best practices and technologies driving speed and scalability in modern database management.
There’s a wide range of reasons why many organizations are deciding to modernize their data architectures. But they all agree on one thing: by using data more effectively, more widely, and more deeply, they can improve and optimize business and decision-making processes that will help them stay competitive in the emerging digital economy.
A strong data management foundation is essential for effectively scaling AI and machine learning programs to evolve into a core competence of the business. Download this special report for the key steps to success.
Today’s enterprises rely on an assortment of platforms and environments, from on-premise systems to clouds, hybrid clouds and multi-clouds. This calls for modern data management practices that leverage emerging technologies, providing enterprise decision managers with the tools and insights they need to improve and transform their businesses. Download this special report for best practices in moving to modern data management standards to ensure the integration and governance of valuable data sources within today’s diverse environments.
Emerging agile technologies and techniques are leading to new ways of accessing and employing data. At the same time, the increasing complexity of these environments is creating additional challenges around security and governance, and orchestration and monitoring, which is particularly evident with the rise of hybrid, multi-cloud enterprise environments. Welcome to the era of the digitally enriched platform. Download this special report today to dive into emerging technologies and best practices.
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.
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.
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.
Today’s organizations want advanced data analytics, AI, and machine learning capabilities that extend well beyond the power of existing infrastructures, so it’s no surprise that data warehouse modernization has become a top priority at many companies. Download this special report to under how to prepare for the future of data warehousing, from increasing impact of cloud and virtualization, to the rise of multi-tier data architectures and streaming data.
Improving data quality is one of the top 50 ways businesses can save money and remain successful during economic downturns. With a bumpy road ahead, now is the perfect time for developers, data architects and data stewards to review the 7 Cs of Data Quality and build a game plan to eliminate poor quality or inconsistent customer data and improve data accessibility and usability.
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.
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.
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.
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.
DataOps is poised to revolutionize data analytics with its eye on the entire data lifecycle, from data preparation to reporting. Download this special report to understand the key principles of a DataOps strategy, important technology, process and people considerations, and how DataOps is helping organizations improve the continuous movement of data across the enterprise to better leverage it for business outcomes.
Today’s enterprises are looking to data managers to be able to respond to business challenges with scalable and responsive systems that deliver both structured and unstructured data – and accompanying insights – at a moment’s notice, with the ability to respond to any and all queries. What’s needed is a modern data architecture that is built on flexible, modular technology, either from open source frameworks and software or through cloud services. Download this special report for the eight key ways to prepare for and manage a modern data architecture.
From modern data architecture and hybrid clouds, to data science and machine learning, the Data Sourcebook is your guide to the latest technologies and strategies in managing, governing, securing, integrating, governing and analyzing data today. Download your copy today to learn about the latest trends, innovative solutions and real-world insights from industry experts on pressing challenges and opportunities for IT leaders and practitioners.
As enterprise data warehouses evolve to become modern data warehouses in the cloud, they still hold a significant role for enterprise analytics as a vital component of an enterprise data analytics platform. The reality is that this evolution will be a hybrid-cloud architecture that requires shared and unified capabilities to represent both cloud and on-premises environments as a single data analytics platform for the business. A multi-cloud architecture will be likely for many companies as data gravity from more data sources, users, and applications shifts data processing among clouds, requiring open data architecture principles and furthering the need for enterprise data unification and governance.
Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility and the ability to innovate. However, most database teams are struggling just to keep the lights on. While database environments continue to grow in size and complexity in conjunction with new business demands, the challenge of maintaining the performance and availability of business-critical systems and applications is growing in step. Download this report for key strategies and technologies to survive and thrive in today’s world of speed and scalability today.
Data science and machine learning are on the rise at insights-driven enterprises. However, surviving and thriving means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from infrastructure management, to data preparation and exploration, model training and deployment. In response, new solutions have emerged, along with the rise of DataOps, to address key needs in areas including self-service, real-time and visualization.
Until recently, clunkiness ruled the data systems and integration game. Expensive and complicated middleware was required to bring applications and information together, consisting of connectors, adapters, brokers, and other solutions to put all the pieces together. Now, cloud and containers – and Kubernetes orchestration technology – have made everyone’s jobs easier, and raised the possibility that both applications and data can be smoothly transferred to whatever location, platform, or environment best suits the needs of the enterprise. Download this special reports to learn the ins and outs of Containers, emerging best practices, and key solutions to common challenges.
From the rise of cloud computing, machine learning and automation, to the impact of growing real-time and self-service demands, the world of database management continues to evolve. Download this special report to stay on top of new technologies and best practices.
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.
You’re already a data expert, so why do you need to become a data governance expert too? Because the business of data is changing. It’s no longer about building a better data warehouse. It’s about making sure your data can deliver value to the business. Learn how to be the expert your organization needs to turn your data into a strategic asset.
The adoption of new database types, in-memory architectures and flash storage, as well as migration to the cloud, will continue to spread as organizations look for ways to lower costs and increase their agility. Download this brand new report for the latest developments in database and cloud technology and best practices for database performance today.
Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility, and the ability to innovate through better collaboration, visibility, and performance. However, as data sources, workloads, and applications continue to grow in complexity, so does the challenge of supporting them. To be successful, businesses need faster, more flexible, and more scalable data management processes.
Download this special report to gain a deeper understanding of the key technologies and strategies.
The Internet of Things represents not only tremendous volumes of data, but new data sources and types, as well as new applications and use cases. To harness its value, businesses need efficient ways to store, process, and ana¬lyze that data, delivering it where and when it is needed to inform decision-making and business automation. Download this special report to understand the current state of the marketplace and the key data management technologies and practices paving the way.
Underpinning the movement to compete on analytics, a major shift is taking place on the architectural level where data is captured, stored, and processed. This transformation is being driven by the need for more agile data management practices in the face of increasing volumes and varieties of data and the growing challenge of delivering that data where and when it is needed. Download this special report to get a deeper understanding of the key technologies and best practices shaping the modern data architecture.
Today, more than ever, businesses rely on IT to deliver a competitive edge. They want efficiency, agility and the ability to innovate. However, the reality is most IT departments are struggling just to keep the lights on. A recent Unisphere Research study found that the amount of resources spent on ongoing database management activities is impacting productivity at two-thirds of organizations across North America. The number one culprit is database performance.
Since its early beginnings as a project aimed at building a better web search engine for Yahoo — inspired by Google’s now-well-known MapReduce paper — Hadoop has grown to occupy the center of the big data marketplace. Right now, 20% of Database Trends and Applications subscribers are currently using or deploying Hadoop, and another 22% plan to do so within the next 2 years. Alongside this momentum is a growing ecosystem of Hadoop-related solutions, from open source projects such as Spark, Hive, and Drill, to commercial products offered on-premises and in the cloud. These next-generation technologies are solving real-world big data challenges today, including real-time data processing, interactive analysis, information integration, data governance and data security. Download this special report to learn more about the current technologies, use cases and best practices that are ushering in the next era of data management and analysis.
The value of big data comes from its variety, but so, too, does its complexity. The proliferation of data sources, types, and stores is increasing the challenge of combining data into meaningful, valuable information. While companies are investing in initiatives to increase the amount of data at their disposal, most are spending more time finding the data they need than putting it to work. Download this special report to learn about the key developments and emerging strategies in data integration today.
When asked recently about their top reasons for adopting new technologies, the readers of Database Trends and Applications all agreed: supporting new analytical use cases, improving flexibility, and improving performance are on the short list. To compete in our global economy, businesses need to empower their users with faster access to actionable information and a better overall picture of their operations and opportunities. At the forefront of this journey to create value from data is in-memory processing. Download this special report to learn about the latest developments surrounding in-memory data management and analysis.
Download this special report to guide you through the current landscape of databases to understand the right solution for your needs.
From fraud detection to ad targeting, supply-chain optimization to campaign forecasting, the key use cases for big data require a successful analytics program. Businesses are investing heavily in initiatives that will increase the amount of data at their fingertips. In fact, the percentage of organizations with big data projects in production is expected to triple within the next 18 months according to a recent study from Unisphere Research. However, many businesses are spending more time finding needed data rather than analyzing it. To compete on analytics, the right mix of people, processes and technology needs to be in place to generate value. Download this special report to learn about the key technology solutions and strategies for succeeding with big data analytics today.
Today, the world of decision-making, along with the data sources and technologies that support it, is evolving rapidly. The future of data warehousing is not only being shaped by the need for businesses to deliver faster data access to more users, but the need for a richer picture of their operations afforded by a greater variety of data for analysis. A new data warehousing architecture is emerging, along with a new generation of technologies and best practices, to support the requirements of big data and the need for faster decision-making. To learn about the new technologies and strategies paving the way, download this special report today.
The “pie-in-the-sky” days of big data may be over, but the urgency for businesses to compete on analytics is stronger than ever. In fact, the percentage of organizations with big data projects in production is expected to triple within the next 18 months based on a recent study from Unisphere Research. The conversation around big data is shifting, from why to how. How can businesses harness the bits and bytes of data being captured inside and outside their enterprise to improve, empower and innovate? To learn about the key big data success stories today, download this special report.
The hottest term today—the “Data Lake”—is currently coming off the hype cycle and into the scrutiny of pragmatic IT and business stakeholders. As with all big concepts that have transformed the industry, from the early days of data warehousing and business intelligence, to the growth of cloud computing and big data, best practices are ultimately proven to deliver the benefits promised. To clarify the ambiguities surrounding the concept of the Data Lake, Unisphere Research and Database Trends and Applications combined forces with Radiant Advisors to publish a comprehensive report, “The Definitive Guide to the Data Lake.” By combining an analysis of fundamental information management principles with existing customer implementations of big data and analytics, this report explains how current data architectures will transform into modern data platforms. Download your copy today.
Sponsored by industry-leaders Hortonworks, MapR, Teradata and Voltage Security
From hybrid databases that can process structured and unstructured data - and run transactions and analytics - in the same location, to hybrid data architectures that bring together both established and new database approaches to address the requirements of different data sources, workloads and applications, the reality that most organizations are facing today is that the world of big data is a multifaceted one. To be successful, organizations need speed, scale, flexibility and agility. At the same time, they need ways to keep down costs and complexity. To learn about the key technologies and approaches to hybrid databases and data environments, download this special report from Database Trends and Applications.
Today, there are more things connected to the Internet than people on the planet. From home appliances and cars, to light bulbs and livestock, if you can attach a sensor to it, it can be become part of a universe of physical objects able to communicate and interact digitally. According to estimates, this universe is on track to exceed over 25 billion devices by 2020, not including PCs, tablets and smartphones.
Underpinning the movement to compete on analytics, a major shift is taking place on the architectural level, where data is captured, stored, and processed. This transformation is being driven by the need for more agile and flexible data management processes in the face of increasing volumes and varieties of data.
Whether Hadoop becomes the de facto data management platform of the future or simply a key component in a hybrid architecture comprised of numerous technologies, one thing is for sure: Hadoop adoption is growing. In fact, a recent survey conducted using subscribers of Database Trends and Applications found that 30% have deployed Hadoop at their organization while 26% are currently considering or planning for its adoption within the next 12 months.
Ask the average DBA how they spend the majority of their time and the answer is almost always going to be “performance tuning.” Optimal performance is a constantly moving target. Database transactions and volumes are constantly growing. Business applications are increasing in sophistication with greater user requirements. To stay competitive, companies want speed, scalability, high-availability and cost-efficiency. The challenge, of course, is getting there.
Many IT departments are researching new technologies to address these issues, from database monitoring tools, to new types of databases, to virtualization and cloud solutions. In a recent study conducted over 285 organizations across North America, database performance monitoring was ranked the top area ripe for automation. This same study found that migrating or upgrading databases was the top area for investment, followed closely by virtualization and cloud.
Data integration is a crucial part of the equation for any business interested in fully harnessing its information resources. However, data integration challenges are multiplying in step with the growing complexity of data environments. Most organizations today are dealing with an ever-expanding array of data sources and users with varying requirements. Therefore, it is no surprise that integration projects are topping the priority list. In fact, a brand new study conducted over the readers of Database Trends and Applications found that 38% of companies polled had integration projects in production while 30% were planning or piloting projects. Download this special report to learn about the key developments in the marketplace and new solutions helping companies overcome challenges.
In-memory computing is currently racing toward the mainstream and revolutionizing the way enterprises leverage data to support their business requirements along the way. How big is this revolution? Nearly 75% of IT stakeholders at organizations across North America surveyed by Unisphere Research believe that in-memory technology is important to enabling their organization to be competitive. To succeed in today’s economy, businesses need faster data processing, fresher data, and more cost-effective data systems. Download this special report to learn the ins and outs, as well as the key products available in the marketplace.
When it comes to databases, businesses have more choices than ever today. From the longstanding RDBMS, to the growing camps of NoSQL and NewSQL databases, the landscape is becoming increasingly specialized and best-of-breed. This transformation mirrors the plethora of challenges IT departments across industries face today: the need to handle larger data volumes, the need to handle new data types, the need to deliver data faster, the need to support more application users, and the need to operate more cost-effectively, to name a few. Download this special report to read about the current state of the marketplace and learn about the new technologies that are helping businesses address these challenges.
Real-time information processing, a concept that has been around for a long time, has been in vogue lately. One reason for its popularity is the fact that real-time capable technology and online services have become very affordable, even for small businesses. Another factor is that real time has the attention and interest of the boardroom and executive suite. The idea of being able to instantaneously sense and respond to threats and opportunities has a lot of appeal for business leaders vying for an edge in a fiercely competitive global economy. With technology chipping away at the time it takes to gather relevant and accurate data, there’s less need for bureaucratic, hierarchical decision-making structures. Emerging technologies are now becoming part of the enterprise scene—such as in memory technology, cloud, mobile, and NoSQL databases—are bringing more real-time capabilities to the fore.
Business intelligence and analytics has undergone a revolutionary shift over the past few years, a transition that is still working its way through enterprises and their processes. Nowhere is this more evident than in the rapidly changing roles and expectations of information workers—those managing the data, as well as those consuming it.
Cloud databases are on the rise as more and more businesses look to capitalize on the advantages of cloud computing to power their business applications. In fact, a recent Unisphere Research study revealed that 37% of organizations are now using or considering adopting a cloud database.
Elastic scalability, high availability, flexible capacity planning, and self-service provisioning are among the key, sought-after benefits. While traditional concerns about data security and compliance still have some enterprises watching from the sideline, for many enterprises, the advantages of cloud databases are becoming harder and harder to ignore.
Since the 1980S, companies have invested millions of dollars in designing, implementing, and updating enterprise data warehouses as the foundation of their business intelligence systems. The founding principle of the data warehouse was simple: a single version of the truth to support corporate decision making. Today, the world of decision making, along with the data sources and technologies that support it, is evolving rapidly.
The future of data warehousing is not only being shaped by the need for businesses to deliver faster data access to more users, but the need for a richer picture of their operations afforded by a greater variety of data for analysis. The unstructured and semistructured data that companies are collecting from social media, remote sensors, web traffic, and other sources needs to be integrated and combined for analysis to produce valuable insights for better decision making.
Listening to the pundits, you can be forgiven for thinking that the unstructured, “cloudified,” out-of-network data tsunami is poised to sweep through and shake enterprises out of their comfortable, relational worlds. But there’s more to the story than that. Enterprises still, and will likely continue to, rely on relational database systems as their transactional workhorses. These systems continue to evolve and adapt to today’s new data realities. Many relational database and data warehouse environments are opening to unstructured data, running in clouds, and supporting caches that enable real-time— or near real-time—decision making.
The next generation of databases and data platforms is coming into full fruition to help enterprises more effectively store, process, analyze and deliver value from Big Data. This report hones in on the key challenges and opportunities ahead, and provides in-depth information on leading-edge technologies and solutions. Download your copy today to stay ahead of the latest developments in NoSQL, NewSQL and Hadoop.
This DBTA Thought Leadership Series discusses new approaches to planning and laying out tracks and infrastructure, moving to a real-time analytics requires new thinking and strategies to upgrade database performance. What are needed are new tools, new methodologies, new architectures, and a new philosophy toward managing data performance.
Today’s 24/7 enterprises require a well-designed, next-generation data integration architecture. Why is data integration so difficult? For many organizations, data integration has been handled as a dark art over the years, implemented behind the scenes with ad hoc scripts, extract, transform, and load (ETL) operations, connectors, manual coding, and patching. Often, front-end applications to get at needed data are built and deployed one at a time, requiring considerable IT staff time, as well as creating a waiting period for business decision makers. This one-off, manual approach to data integration will not work in today’s competitive global economy. Decision makers need information, at a moment’s notice, that is timely and consistent. However, they are challenged by their organizations’ outdated data integration systems and methods. Often, information may be delayed for weeks, if not months, by the time it takes to develop handcoded scripts to deliver requested reports. The process
Hadoop is marching steadily into the enterprise, but key challenges remain, from manual coding demands to a lack of real-time capabilities and the time it takes to bring a Hadoop project into production. At the same time, brand-new startups and veteran software companies alike are delivering new offerings to the marketplace to make it easier to deploy, manage, and analyze Big Data on Hadoop. From data integration and business intelligence tools to integrated analytical platforms and a new wave of SQL-on-Hadoop solutions, the common goal is to help companies unleash the power of Hadoop for Big Data analytics. Download this special report to learn about the key solutions. Sponsored by MarkLogic, RainStor, Tableau, Qubole, Karmasphere, Appfluent, and Hadapt.
UNSTRUCTURED DATA: Managing, integrating, and Extracting Value While unstructured data may represent one of the greatest opportunities of the big data revolution, it is one of its most perplexing challenges. In many ways, the very core of big data is that it is unstructured, and many enterprises are not yet equipped to handle this kind of information in an organized, systematic way. Effectively capturing and capitalizing on unstructured data isn’t just a technical challenge, it represents an organizational challenge. A flexible and agile enterprise environment—supported and embraced by all business units—will elevate unstructured data processing and analysis to a position in which it can help drive the business. This Thought Leadership Series is sponsored by Objectivity and Database Plugins
THE IDEA OF THE REAL-TIME ENTERPRISE is straightforward: Increase your organizational responsiveness through automated processes and raise organizational effectiveness and competiveness. If your organization can fulfill orders, manage inventory, resolve customer issues, and implement strategies to address changing circumstances faster and more efficiently, your organization is going to be more successful. However, for most enterprises, this is still an unrealized objective. Increasing data volumes, data varieties, and business demands are now stretching the limitations of traditional data management technologies and intensifying the challenge of integrating and analyzing data in real-time. Consequently, many organizations are looking beyond their current IT infrastructures. Download this report to learn about the leading technologies enabling organizations to deliver data across the enterprise in real-time. Sponsored by Oracle, SAP, Objectivity, JackBe and BackOffice Associates.
Cloud databases are on the rise as more and more businesses look to capitalize on the advantages of cloud computing to power their business applications. In fact, a recent Unisphere Research study found that nearly one-third of organizations are currently using or plan to use a cloud database system within the next 12 months. Download this complimentary report, sponsored by NuoDB, GenieDB, 10gen, Cloudant, Progress DataDirect, Clustrix, Objectivity and TransLattice, to gain a deeper understanding of the different types of cloud databases, their unique benefits and how they are revolutionizing the IT landscape.
BIG DATA, a well-used term defining the growing volume, variety, velocity, and value of information surging through organizations-has become more than a buzz phrase thrown about at conferences and in the trade press. Big Data is now seen as the core of enterprise growth strategies. Business leaders recognize the rewards of effectively capturing and building insights from Big Data, and see the greatest opportunities for Big Data in competing more effectively and growing business revenue streams. As the amount and variety of data grows, so do the skills required to capture, manage and analyze this data. This specialized issue of Best Practices from Oracle, Attunity, Couchbase, HiT Software Inc, Progress DataDirect, LexisNexis, Confio and Objectivity focus on a more formidable challenge: making Big Data valuable to the business. Complimentary from DBTA.
The appeal of in-memory technology is growing as organizations face the challenge of Big Data, in which decision-makers seek to harvest insights from terabytes and petabytes worth of structured, semi-structured and unstructured data that is flowing into their enterprises. This special thought leadership series provides context and insight on the use of in-memory technology, and detailed explanations of new solutions from SAP, Tableau Software, Tibco Spotfire, JackBe, Terracotta and MemSQL. Complimentary from DBTA.
Is your organization’s systems and data environments ready for the Big Data surge? If not, you are not alone. A recent study conducted among Independent Oracle User Group members by DBTA’s Unisphere Research finds that fewer than one-in-five data managers are confident their IT infrastructure will be capable of handling the survey of Big Data. This special Best Practices section from DBTA provides context and insight on the need to address this issue now, and detailed explanations of new technologies for dealing with Big Data from Aster/Teradata, MarkLogic, Akiban, Progress/Data Direct, Infinitegraph, HP-Vertica and Denodo. Complimentary from DBTA.
To compete in today’s economy, organizations need the right information, at the right time, at the push of a keystroke. But the challenge of providing end users access to actionable information when they need it has also never been greater than today. Enterprise data environments are not only growing in size, but in complexity - with a dizzying array of different data sources, types and formats. The September 2012 Best Practices in Data Integration, Master Data Management, and Data Virtualization report examines the data integration challenges and opportunities that Big Data is currently presenting data-driven organizations.
With the rise of big data, the database and data management tools market is in a state of flux, the likes of which have not been seen in this sector before. Companies are now awash in big data, and end users are demanding greater capability and integration to mine and analyze new sources of information. As a result, organizations are supplementing their relational database environments with new platforms and approaches that address the variety and volume of information being handled. In this special section in Database Trends and Applications analyst Joseph McKendrick brings you up on current thinking and strategies users and vendors are pursuing to extract value from large, often unwieldy data stores. This is followed by nine separate sponsored content pieces focusing on in-memory, real-time data integration, data virtualization, BI, columnar databases, NoSQL and Hadoop.
The rise of Big Datais challenging many long-held assumptions about the way data is organized, managed, ingested, and digested. However, for many organizations, Big Data is still a new frontier that they have only begun to explore. "Many organizations leave their data to pile up; they are aware of it as a resource but haven't analyzed it. They don't know what's useful and what's worthless." This fourteen-page section from the March edition of Database Trends and Applications is an invaluable resource that provides multiple perspectives on the chief challenges our readers face and the solutions that will enable organizations to begin tapping into the power of Big Data assets.
Key extracts from Database Trends and Applications from the December print edition focus on "Data Security and Compliance".