White Papers

An Oracle® audit can be time-consuming, expensive, and stressful. This paper describes the best practices, third-party expertise, and tools that not only make an Oracle audit less stressful, but also eliminate Oracle back-license costs, back-support costs, and audit fees.

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.

Why use NoSQL? Innovative companies like AT&T, GE, and PayPal have successfully transitioned from relational to NoSQL for their critical web, mobile, and IoT applications. By understanding where to introduce NoSQL, how to model and access the data, and how to manage and monitor a distributed database, you can do the same.

New technologies are rapidly accelerating digital innovation, and customer expectations are rising just as fast. For nearly every industry this means customer experience has quickly become the next competitive advantage. In response, many organizations are switching to NoSQL databases to deliver the extraordinary experiences customers demand.

Businesses have traditionally run on two types of technology platforms, analytical and transactional. But neither was designed to handle the increasingly complex sequence of real-time interactions required by today’s business applications. As a result, leading businesses are quickly moving toward a new third kind of platform – the “system of engagement” – especially for their customer-facing apps.

The Couchbase Engagement Database is built on the Couchbase Data Platform with the most powerful NoSQL technology available for unmatched flexibility, performance, and availability at any scale. Many leading companies are making the move from Oracle to Couchbase to get the best performance and highest availability possible from their mission-critical business applications across regions and data centers.

How do the latest releases of two leading NoSQL databases compare on both read/write and query performance? The emerging technologies thought leader Avalon Consulting, LLC benchmarked MongoDB 3.2 and Couchbase Server 4.5 to find out. These big data experts ran industry standard (YCSB) workloads for both read/write and query.

Today’s web, mobile, and IoT applications need to operate at increasingly demanding scale and performance levels to handle thousands to millions of users. Terabytes or petabytes of data. Submillisecond response times. Multiple device types. Global reach. Caching frequently used data in memory can dramatically improve application response times, typically by orders of magnitude.

Today’s consumers rely on their mobile apps no matter where they are. If your apps are sluggish and slow or don’t work at all when there’s no internet connection, your customers will start looking for other apps that work all the time. To help you provide the always-on experience customers demand, database solutions like Couchbase Mobile have added synchronization and offline capabilities to their mobile database offerings.

Nearly every industry today is in the midst of a digital disruption driven by the unprecedented acceleration of new technology. This unstoppable transformation is redefining the rules of success and driving customer expectations higher every day. This Couchbase report reveals that to remain competitive – or even to remain in existence – businesses have no choice but to deliver consistently amazing customer experiences powered by the agile, responsive, and scalable use of data.

Introducing the Engagement Database – and why it’s a vital part of your digital transformation. Digital transformation is changing everything for customers. Their interactions. Their transactions. And most importantly, their expectations. Yesterday’s experience won’t bring customers back today. And yesterday’s transactional and analytical databases certainly can’t deliver the exceptional experiences those customers will demand tomorrow. Get your Engagement Database whitepaper now to learn: • What an Engagement Database is • Why engagements and interactions trump transactions in the digital economy • Why transactional and analytical databases can’t keep up • How to evaluate and adopt the best engagement database for your business You’ll also find out how Couchbase designed the world’s first Engagement Database with unparalleled agility, manageability, and performance at any scale. Get your whitepaper now and learn how Couchbase is built to deliver customer experience

Read this Forrester report to gain a better understanding of how Deep Learning will disrupt customer insights professionals in their mission to understand and predict customer behaviors.

With 15,000+ employees and annual revenues exceeding $4 billion (USD), Experian is a global leader in credit reporting and marketing services. The company is comprised of four main business units: Credit Information Services, Decision Analytics, Business Information Services, and Marketing Services.

Digital transformation is rapidly changing our lives and influencing how we interact with brands, as well as with each other. The digitization of everything, particularly the widespread use of mobile and sensor data, has significantly increased user expectations. This rapid adoption of newer technologies—mobile, digital goods, video, audio, IoT, and an app-driven culture—has resulted in new ways to engage customers with improved products and services. At the heart of this transformation is how organizations use data and insights from the data to drive competitive advantage. Gaining meaningful customer insights can help drive customer loyalty, improved customer experience, revenue and reduce cost.

Insurers have long struggled with data silos. Getting the right information at the right time is a challenge. Cloudera provides a new paradigm for breaking data silos. For the first time, insurers can blend and analyze data from any source, in any amount and for all types of workloads. The Insurance industry is undergoing a digital transformation, in which big data, machine learning and IoT are playing a central role.

Data is driving modern business. Supplied with the right data at the right time, decision makers across industries can guide their organizations toward improved efficiency, new customer insights, better products, better services, and decreased risk.

Ponemon Institute is pleased to present the findings of Big Data Cybersecurity Analytics, sponsored by Cloudera. The purpose of this study is to understand the current state of cybersecurity big data analytics and how Apache Hadoop based cybersecurity applications intersect with cybersecurity big data analytics.

The cloud is fundamently changing the way companies think about deploying and using IT resources. What as once rigid and permanent can now be elastic, transient, and available on-demand. Learn how Cloudera's modern data platform is optimized for cloud infrastructure.

Read this Forrester report to gain a better understanding of the revolution that is Deep learning.

IoT projects are far beyond the pilot stage and have spurred IT leaders to implement hybrid strategies, processing some IoT data at the edge of the enterprise, while sending much of it to a central hub for deep analytics, according to a recent IDG Quick Pulse survey. As competition heats up, the companies that can find the right balance between edge and hub are likely to fare best. Download the results of the IDG survey.

As companies scramble to protect digital assets from a new generation of threat actors, big data and machine learning are yielding critical threat intelligence. A new IDG Research survey finds greater visibility is essential.

How a comprehensive, 360-degree view of customers based on a spectrum of data can enrich actionable insights. This TDWI checklist focuses on six strategies for advancing customer knowledge with big data analytics. It begins with the all-important first step: gaining as close to a complete, 360-degree view of customers as possible. Big data platforms that implement open source Apache Hadoop technologies enable organizations to assemble data for a 360-degree view. The checklist explores how to expand the impact of big data analytics while applying governance to ensure proper care of customer data. Taken together, the six strategies will help you apply big data analytics to attracting and retaining your organization’s most valuable asset: its customers.

The growth of large-scale business services that incorporate web and mobile applications has exploded. These services – which include transactions that originate on a tablet, phone, etc. and connect to back-end applications hosted on the mainframe – tend to be very visible to customers, partners, executives and investors. Unacceptable response times and unreliable performance do not go unnoticed. Read this whitepaper to discover how organizations can get deep insight into web-based and mobile transactions’ impact on the mainframe, enabling them to monitor and improve IT operations and application performance.

Today’s computing environments are a complex arrangement of many hardware components and several software layers. This means that the failure of one element can directly impact other users. An IT service intelligence solution with machine learning capabilities can help to provide a comprehensive view of your organization’s service delivery and allowing you to effectively set SLAs, identify potential problems and plan for changes in the IT environment.

Results of the 2018 State of the Mainframe Survey show that mainframe machine data analytics will grow to support security and compliance initiatives this year. As security threats and breaches continue to rise worldwide, the cost of conducting an audit or failing a compliance mandate far exceeds the cost of the technologies that can be put into place to help address these initiatives. Organizations will look at leveraging analytics platforms for security and compliance using log data and emerging Big Data analytics platforms such as Splunk and Hadoop. Read this Ebook to for more insights and key mainframe data analytics trends to watch for this year.

Fraud and cybersecurity attacks cost companies hundreds of billions of dollars a year. If your organization falls victim to one of these attacks, the impact is greater than just financial. Read this Ebook to learn how integrating all machine data generated by networks and endpoints across the enterprise – including mainframes – can give you total visibility into the most common and dangerous threats to your organization so you can stop cyber-attackers in their tracks.

Syncsort’s 2018 Big Data Trends Survey results are in and one thing is clear; Big Data will be stronger than ever! Although the names and technologies may change (e.g., Hadoop is giving way to Spark), initiatives involving the processing of massive data volumes for greater insights are here to stay.

The techniques of NLP and text analytics overlap a great deal. The differences mainly lie in the problem that each tries to solve. In the search world, natural language processing analyzes user inputs (queries) to understand their intent. It allows a user to communicate with a machine in a way that is natural for the user, which, of course, is not natural for the machine.To accomplish this, NLP operates on data so that a computer can understand a document—and the relationships it may infer—in the same way a user understands it. It’s wise here to remember that infer means to “make an educated guess.” This is where NLP and text analytics use many of the same methods.

Whether it’s through a contact center or self-service web portal, the support function is often where customers engage with your company. And in either context, giving answers quickly increases satisfaction and reduces churn. With AI-powered search at the core of your customer support systems, you can make the support experience much better for customers and easier for support staff.

Right now, we're surrounded by examples of machine learning, such as Google’s page ranking system, photo tagging on Facebook, customized product recommendations from Amazon, and automatic spam filtering on Gmail. Download this quick guide to learn how machine learning works and how businesses can use it to expand their analytics capabilities.

The unified digital workplace removes “friction” from the everyday activities that consume the time and effort of knowledge workers. AI-powered search is the lubricant that makes a frictionless digital workplace possible. It’s not marketing hype. It’s real. And it can transform your organization.

Massive declines in the cost of storage and computation have finally made cognitive computing economical. With the emergence of these methods from academia, organizations now have access to tools, solutions, and platforms that can deliver a better experience finding and discovering new insights. The focus has shifted to accelerating time-to-value in the deployment of cognitive search.

It used to take years for the improvements in search technology that emerged from academic research to filter down to commercial enterprises. Not any longer. Now it’s often a matter of months, which has accelerated the pace of change in and adoption of cognitive search. Cognitive search can speed innovation in the life sciences while increasing productivity and lowering cost.

Over the past several years, HVR has worked with a variety of customers as they adopt the cloud, specifically the AWS cloud. We created this guide to share best practices uncovered in working with them on their cloud data integration projects. With this guide, we hope to provide key considerations when architecting a data integration solution that enables a cloud solution for today and the future.

While the Internet of Things (IoT) may still be unfamiliar to many consumers, businesses are well aware of its potential. More than 90% of participants in our research said that IoT is important to their future operations. Most said they view IoT as very important to speed the flow of information and improve responsiveness within business processes and nearly half are using IoT in their analytics and business intelligence functions. In implementing IoT systems, however, organizations face challenges. In particular, many struggle to maximize the value of IoT event data.

Data lakes have emerged as a primary platform on which data architects can harness Big Data and enable analytics for data scientists, analysts and decision makers. Analysis of a wide variety of data is becoming essential in nearly all industries to costeffectively address analytics use cases such as fraud detection, real-time customer offers, market trend/pricing analysis, social media monitoring and more.

The changing nature of data integration demands a new approach. With the Attunity data integration platform, users benefit from a simple, automated, drag-and-drop GUI design that enables very high-volume universal data replication and ingestion as well as real-time, continuous data integration and loading: an approach that’s so easy, you could say it’s magic.

Over the last 10 years, major trends in technology have shaped and reshaped the ongoing role of the DBA in many organizations. New data types coupled with emerging applications have led to the growth of non-relational data management systems. Cloud technology has enabled enterprises to move some data off-premises, complicating the overall data infrastructure. And, with the growth of DevOps, many DBAs are more deeply involved with application and database development. To gain insight into the evolving challenges for DBAs, Quest commissioned Unisphere Research, a division of Information Today, Inc., to survey DBAs and those responsible for the management of the corporate data management infrastructure. Download this special report for insights on the current state of database administration, including new technologies in use and planned for adoption, changing responsibilities and priorities today, and what’s in store for the future.

The explosion in data volume got most business users excited about the possibility of uncovering new insights to make better business decisions. But many organizations are struggling to deliver on the expected value from big data. Traditional data governance is a good first step, but in today’s rapidly changing data world, it’s not enough.

The General Data Protection Regulation (GDPR) goes into effect on May 25, 2018. Are you ready? If you’re like most organizations, the answer is probably no. But with fines up to 2-4% of global revenue for non-compliance, the pressure is on to comply.

Conquering data governance may seem like a superhuman task. But when you activate these five super powers, it gets a whole lot easier.

A new year means new challenges and opportunities (& budgets!). Learn how you can get ready for the year ahead by downloading the Collibra e-book, 7 Data Predictions for 2018.

For data to be actionable, it must be discoverable. But too often, business users spend too much time wandering the data aisles searching for the information they need. It’s time to end the data search grind.

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.

Cyber security has become the topic of conversation for organizations across every industry. With the average breach costing $200 per lost customer record, and even more for lost intellectual property, organizations are looking for new solutions. Forward-thinking organizations have discovered a new class of solutions that can detect sophisticated, novel threats designed to look like typical behavior.

Internet of Things (IoT)-enabled applications are poised to revolutionize digital customer experience and enhance digital operational excellence — but where will they apply at your company? This report helps you identify where the ripest opportunities lie.

As an early entrant to the telematics service provider (TSP) market, Octo Telematics has established a global market-leading position over the last decade. To further drive the IoT insurance market and build on its market position, Octo Telematics needed to develop an IoT and telematics platform with the functionality, flexibility, and scale to support the next evolution of IoT-based insurance propositions. Download the report to learn how Octo Telematics implemented a next-generation IoT and telematics platform.

The world of data management is changing rapidly, from the way we work, to the underlying technologies we rely upon. Since the term “big data” swept the world off its feet, Hadoop, NoSQL and Spark have become members of the enterprise landscape, data lakes have evolved as a real strategy and migration to the cloud has accelerated across service and deployment models. From systems of record, to systems of engagement, data architecture is an important as ever and the traditional models aren’t working anymore. Download this report for the key technologies and trends shaping modern data architectures, from real-time analytics and IoT, to data security and governance.

This white paper, written by Java Champion Ben Evans, provides an introduction for architects and developers to Hazelcast’s distributed computing technology.

This white paper outlines the four key pillars of analytics speed that are essential to deliver transformational value with analytics: speed of development, speed of data processing, speed of deployment, and speed of response.

Beyond blazing speed and expansive functionality, learn what the Actian Analytics Platform can do for your organization and how it can be used to create transformational value in areas like customer delight and sustained competitive advantage.

For the second year-in-a-row, MarkLogic was recognized as a next generation Challenger and only NoSQL vendor to remain a Challenger in the 2017 report. Gartner placed MarkLogic highest for both its ability to execute and furthest for its completeness of vision in the Challengers quadrant in the new Gartner 2017 Magic Quadrant for Operational Database Management Solutions. Trusted by large enterprises worldwide and across industries to build and secure their mission critical applications and multi-model database management systems, we continue to deliver on our promise to integrate data better, faster, with less cost. Download your complimentary copy of the Gartner 2017 ODBMS Report.

This white paper addresses key methods for successfully managing today’s complex database infrastructures, including balancing key business metrics, understanding the challenges DBAs face, and finding the right tools to monitor and manage the database environment. A slow relational database can substantially impact the performance of the applications it supports. Users may issue thousands of transactions every minute, which are serviced by perhaps dozens of redundant web and application servers – and a single database. The relational database must preserve consistency and availability, making it a highly centralized asset. It concentrates the transactions and places a great deal of pressure on the database stack to operate at optimal levels of performance and availability. This is why the database is so critical, and it’s also why the DBAs who manage it are more than average administrators. To successfully manage these complex database environments, one must balance key business

DBAs have a need to understand and manage more kinds of databases than ever before and they need access to better tools to administer them. This technical brief covers the history of the database, a snapshot of the current database market and the priorities of today's DBAs as they manage increasing numbers of different kinds of database environments. Learn more about Foglight for Cross-Platform Databases, a tool DBAs can use to simplify database performance monitoring and management.

Managing your Oracle databases without the Diagnostics and Tuning packs can be extremely challenging and frustrating, since you don’t have the deep-dive performance diagnostics capabilities you need to ensure high quality of service for your organization. Foglight for Oracle provides a range of extremely powerful performance diagnostics features that will help you be more efficient and more effective at ensuring peak database performance. With Foglight for Oracle, you can proactively monitor your Oracle databases — without breaking the bank.

As your organization’s data becomes more and more critical, you need a way to ensure it’s never compromised by unscheduled downtime – due to a system crash or malfunction – or scheduled downtime – due to patches or upgrades to Oracle, the operating system, or applications, and storage replacement.

Migrating data from one platform to another requires a lot of planning. Some traditional migration methods are easy to use, but they only work for migrations on the same platform. Quest® SharePlex® can replicate data across platforms, from Oracle to SQL Server, with next to no downtime, offering a flexible, low-cost alternative.

If you're planning on migrating your on-premises production database to the cloud, while keeping test or development environments refreshed and in sync, this on-demand webcast is for you.

Now that Oracle has formally announced the deprecation of Oracle Streams, Oracle Database Advanced Replication, and Change Data Capture in Oracle Database 12c, what’s the best alternative? SharePlex. This technical brief details why SharePlex is the best and most comprehensive solution for all your future data-sharing needs.

You can say “migrations and upgrades” to a database administrator (DBA) or systems administrator, but what they usually hear is “risk and downtime". This e-book shows you how to simplify the migration and upgrade process so you can avoid the risk, downtime and long hours normally associated with database migrations and upgrades.

Maintaining IT security is more of a challenge than it ever has been with the exponential growth and complexity of modern enterprise IT estates, the increasing rate of change of those estates and the explosion in threat sophistication. Rather than working on increasing the complexity of network security policy, IT organizations need to focus on how users access a company’s most valuable assets: databases, applications, data and servers. Download this special white paper to learn why current security strategies are falling short and machine-learning is bring a new level of sophistication to cybersecurity threat prediction, prevention, detection and response.

This document aims to describe different strategies for application caching strategies. It will explain the advantages and disadvantages, and when to apply the appropriate strategy. Additionally, it will give a short introduction to JCache, the standard Java Caching API, as well as insight into the characteristics of Hazelcast’s JCache implementation and how it helps to integrate the different caching strategies into your application landscape.

There are many misconceptions about deploying traditional Oracle applications in the cloud. Watch this special presentation to learn best practices for deploying on private cloud, Oracle Public Cloud, and Amazon Web Services platforms. You'll also get an understanding of the pros and cons of the deployment options, and understand how your management practices will need to be adopted for a cloud deployment.

A new era of cognitive computing and machine learning is unfolding and its impact is already being felt across industries, from preventative maintenance at manufacturing plants and patient diagnosis at hospitals, to the rise of sophisticated chatbots ready to assist us across the connected world. Through the development of inexpensive options for storing and processing data, innovative open source tools, and sophisticated data platforms and cloud services, organizations of all types and sizes can tap into the value of intelligent systems and applications. Download this report today to learn about the key enabling technologies and emerging success factors.

The convergence of advertising technology (AdTech) and marketing technology (MarTech) is a popular topic among advertising and marketing leaders today. Eliminating silos between these industries translates to more personalized customer experiences and a greater ability to quantify the impact of specific advertising and marketing expenditures. The exchange of value occurs by connecting and delivering customer interactions across touchpoints and devices.

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.

The era of digital transformation and IoT is driving the necessity for modern data management, application management and IT automation as a cohesive, integrated set of capabilities. In this paper we will outline the need, challenges and solution for a unified integration platform for big data, provide an overview of its design principles, and highlight some of the technical components that help deliver increased business agility.

Explore this white paper to learn how DBAs can use Quest Benchmark Factory to ensure that changes to their databases don’t degrade the user experience.

Read this e-book to see how Toad can help you manage code changes, test early and often, ensure quality with standards, deploy faster and automate everything.

Read this e-book for walk-throughs, implementation guidelines and links to videos that show how to use Toad® for Oracle Developer Edition and Toad Intelligence Central to automate database development processes, and realize the full promise of agile: the ability to release software in prompt response to market changes.

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 has become a central part of the enterprise IT landscape, along with NoSQL databases. Data lakes have become a reality, and data architectures are being designed with agility in mind. And migration to the cloud has continued to accelerate, from storage, to databases, applications, and beyond. Download this special report to get the latest technology developments, use case and best practices.

When it comes to business intelligence and analytics, high performance and scalability are no longer luxuries—they are requirements for modern organizations. The obsession with performance is driven in large part by user expectations. Research has shown that most users typically only have an eight-second attention span, making it imperative for software to deliver the fastest possible response times and query performance. MicroStrategy 10 is an enterprise analytics and mobility platform that’s architected from the ground up to deliver results with sub-second response times and to scale to hundreds of thousands of users without any restrictions on data size. The platform’s unified architecture and industry-leading in-memory capabilities help MicroStrategy 10 offer best-in-class performance.

A successful sales team is at the heart of every successful organization, with every salesperson responsible for driving revenue, bringing in new business, and nurturing existing customer relationships. Today, organizations have an unprecedented opportunity—a chance to arm their sales reps with timely, relevant customer information. Access to this information gives salespeople the ability to research prospects’ interests, personalize outreach, identify new opportunities, and get the information they need to answer tough questions during face-to-face interactions.

Enterprises today are constantly on the lookout to deliver enhanced services to stay competitive and generate new revenue streams. In a data driven world, the most popular applications are the ones that deliver more “fact-based” insight to the enduser, that helps them make their next move. From banking services, to travel websites, online stores, social media sites and more, every application today collects data that can be used to provide value to their customers. To that extent, customers everywhere are also expecting more than just a “good experience” with their web and mobile applications. Data analytics is that hook. It has the means to harness data that is being collected, by organizing and presenting information to the end-user, and can provide informative insights.

Entrusted with delivering programs and services to millions of citizens dispersed across wide geographies, federal governments are inherently complex and highscale operations. Their agencies have huge workforces to manage, a multitude of facilities and assets to maintain, and massive budgets to monitor and regulate. They have massive budgets to monitor and regulate. With all these moving parts, government agencies face the continual challenge of making well-informed decisions, maintaining operational efficiency, and avoiding waste and fraud.

With increasing competition for students, faculty, and funding, institutions of higher education need to harness the power of data to streamline operations and enhance the student experience. At the same time, integration with legacy banner systems can result in deployment costs that strain tight budgets. Furthermore, threats to colleges and universities, both online and on campus, demonstrate an urgent need for security and intelligence about students and staff more than ever before.

The energy and utilities industry faces a vast number of challenges today. Organizations must navigate price volatility, seek out and replace reserves, run large global operations, and effectively manage risk and changing regulations—all while staying profitable. Aging infrastructure, increasing customer demands, and the emergence of new technologies also contribute to this challenging business environment. To stay relevant, it’s critical for energy and utility companies to harness the power of data to drive performance and efficiency.

With twelve major providers offering fixed line and/or mobile services, the telecommunications industry is highly competitive. Telecoms face added competition in the form of over-the-top applications that cut into profits. And with consumers in this industry being highly informed, they demand and expect regular service improvements. If unsatisfied, they are willing to switch network providers with little remorse. To maintain profitability, telecommunications and broadcast network providers must offset substantial infrastructure costs by maximizing network utilization, delivering the highest value-to-service ratio, and retaining/acquiring customers to grow market share.

The rise of new distribution channels in the insurance market has led to increased price parity among providers. In response, insurers need to be able to differentiate themselves on the basis of the variety and quality of their product and services offerings. It’s essential that insurance companies are able to quickly deliver quotes and process claims, effectively manage risk, and comply with a wide range of regulatory requirements. The ability to harness the power of data is critical to understanding trends and risk exposure, streamlining processes, and delivering better overall customer service.

With the ubiquity of internet access, the proliferation of mobile devices, and the emergence of wireless streaming services, today’s consumers can access media content on their own terms – from any place, at any time, on any device. For media companies, this diverse mix of distribution pathways makes it much harder to track the consumption behaviors and demographics of their audiences, which can undermine traditional advertising revenues. With less direct control over the end-user relationship, media companies must retain their relevance by delivering exceptional content, employing more sophisticated audience analysis, and running better targeted marketing campaigns.

Explore how to index and search with RedisSearch, and compare RediSearch’s performance metrics with that of the other popular search engines.

Get a simple, high performance recommendations engine built in Go with Redis, that applies to manypersonalization use cases.

Explore how Redis Enterprise delivers inline analytics in real-time at any scale or volume.

In today's business environment, the only constant is change. Organizations are increasingly keeping their information in the cloud for better agility and flexibility. The use of cloud-based drives, boxes and repositories in the average organization is multiplying. Download this special report to understand the key challenges, opportunities, technologies and success factors.

How can you beat the competition in an era when every company is a data company? On September 7th at 1pm ET, live in New York and livestreamed around the world, IBM VP of Analytics Don Leeke, with tech host Katie Linendoll, invite you to hear IBM customers, including Sanjay Saxena, Senior VP of Governance, and industry insiders explain what you need to make the most of your company’s data – and leave your competition in the dust. This one-hour session is fully interactive with real time Q&A and insights from the panel posted throughout the session. Join us and see how you can get up and running in 15 minutes; how self-service governance streamlines regulation; and how open standards and a platform-agnostic approach can liberate your data while putting you back in control.

Data is complex. There are domains of data covering an entire universe of information, including customers, products, location, finance, employees, assets, and more. But data doesn’t exist for its own sake; for it to be useful, it must be trusted. Learn more from Melissa.

High volume, real time data processing is at the heart of cloud native applications that scale to support millions of users and billions of events. While containers deliver many benefits to cloud-native application developers traditional data center infrastructure struggles to economically deploy and scale these modern applications in production.

Container adoption has exploded in recent years, allowing enterprises to build new digital services faster than ever and create new revenue streams. Container technology is becoming the standard for delivering these services, heavily disrupting CTO and CIO organizations as they operate in public and private cloud environments. Container adoption has exploded in recent years, allowing enterprises to build new digital services faster than ever and create new revenue streams. Container technology is becoming the standard for delivering these services, heavily disrupting CTO and CIO organizations as they operate in public and private cloud environments.

Clear patterns are emerging to successfully and economically operate containers despite the multitude of choices confronting users today. Achieving reliable outcomes at a reasonable cost point, however, has been elusive for many.

This case study explores how the media provider’s traditional enterprise infrastructure fell short in supporting agile DevOps processes, service-level guarantees, and competitive cost. 24 bare-metal and hypervisor host servers were needed to meet service levels enabling fast response times for customers viewing media and ads.

Every decade or so, IT organizations encounter new application architectures that change how infrastructure is managed and consumed. Server virtualization has been the dominant technology reshaping IT operations until the emergence of modern, containerized applications. With containers, there is near universal consensus that the newest unit of abstraction has officially arrived, beginning to supplant virtual machines. Containers are how developers now prefer to package applications (a $3.4 billion market by 2021 growing at 35% CAGR, according to 451 Research). The adoption of containerized applications has far-reaching implications for how IT operators support these applications and the developers creating them.

Containerization appears be moving rapidly from trials through test and development usage to production de¬ployments, in-line with today’s faster agile and DevOps release cycles. A recent survey completed by 451 Research’s Voice of the Enterprise (VotE) service found that of over 300 enterprise respondents, 19% had begun production deployment of containerized applications, and 8% were in broad production implementation.

This white paper is the second in a two-part series on flexible data modeling for developers, architects and database administrators. Through sample data and queries, it explains how to create flexible schemas using JSON functions.

Often driven by technological needs and familiarity to developers and administrators, deciding on a database can be influenced by past experiences and decisions. The goal of this document is to evaluate MariaDB and MySQL side by side to better inform the decision-making process.

Today, companies are undergoing a digital transformation: offline operations are becoming online operations, enterprise applications are becoming customer-facing applications, and engagement is happening anywhere and everywhere via web, mobile and Internet of Things applications – and when it comes to customer experience, availability is not a preference, it is a requirement.

Marketing is one of the most successful business functions to date within the modern digital enterprise. Much of the success comes from significant advances in data management, software automation, and customer analytics at unprecedented scale that enable a single view of the customer. Success also comes from new sophisticated practices in omnichannel marketing, which leverages the single customer view and related technical practices to market to customers and prospects in a holistic and coordinated fashion. This Checklist Report drills into the data requirements of modern digital marketing, with a focus on the single customer view and omnichannel marketing. The goal of the report is to accelerate user understanding of evolving data and marketing best practices and tools so user organizations are better equipped to initiate or extend omnichannel marketing programs.

While it might be easier than ever to collect data about your customers, it needs to be complete and accurate for it to help an organization. The latter half of that is often the most difficult challenge – making sure the data you collect is data you can trust. Furthermore, for effective customer engagement, you need an integrated view of all your customers’ data. Download our new eBook, “Getting Closer to Your Customers in a Big Data World” to learn more about the different sources of this data, which data points are critical in obtaining, and tips for customer 360 success.

Everything about data is changing – its rate of growth, how it flows, and how it takes shape. Consequently, the division of labor around data is also changing. IT and the business represent a key partnership, but new tools and ways of thinking are empowering the business to do more. Download our eBook today and join us on a journey describing the new rules that are transforming the relationship between business and IT and unleashing the power of data.

Today’s computing environments are a complex arrangement of many hardware components and several software layers. This means that the failure of one element can impact hundreds, thousands, or even millions of users. See how an ITSI solution with machine learning capabilities can provide a comprehensive view of your organization’s service delivery, allowing you to effectively set SLAs, identify potential problems, and plan for changes in the IT environment.

We all know that data has become a critical asset, but it’s not just new sources of data, like mobile and social media, that enterprises should be concerned about. Often untapped, raw data generated by IT systems like servers, mainframes, and databases should be analyzed to gain valuable operational intelligence. That said, this relies on IT operations analytics, or ITOA, which is sometimes difficult to attain. Machine data is unstructured, sequential, and can be overwhelming in volume. Not to mention, machine data loses value exponentially over time, making it more important than ever to have real-time data analytics. With Splunk and Syncsort Ironstream, enterprises can see a real-time view of their IT infrastructure that provides healthier IT operations, higher operational efficiency, and other benefits Download this eBook to learn about the difficulties enterprises face when considering ITOA and how Splunk + Syncsort Ironstream can help overcome those challenges.

There are unique issues and challenges faced by enterprises with mainframes, among the top of which are security and automation of operations. As the sheer amount of data housed on mainframes rises, daily operations have become more complex and more difficult to handle manually or with traditional tools and techniques. In this white paper, learn what “Machine Learning” really is and help separate the reality from both the near-term vision and the industry hype. The paper also discusses how machine data and machine learning may be used to address the challenges driving automated mainframe operations as well as the use cases for Machine Learning at mainframe enterprises today, including operations analytics (ITOA).

Business-altering system slowdowns and security hacks are a new, ever-present reality. How do you know your data is secure – really secure? For starters, information management and events monitoring is a crucial puzzle piece in ensuring you aren’t the next hacked headline. In our new whitepaper, Enterprise Security Outlook: New SIEMs Take Center Stage in Compliance & Cyber-Security, learn how to proactively face fast-evolving cyber threats: What are the latest trends in mainframe security and compliance you should know? How can you leverage big data analytics for security and compliance? What measures can ensure effective fulfillment of mandatory security and compliance audits? How is complete security visibility on an enterprise-wide basis achieved?

Gone are the days of mystique surrounding the Mainframe and its ability to preserve critical enterprise data. Welcome to the advantageous era of knowledgeable insight around Mainframe log data without the need for IBM Mainframe expertise or specialized equipment. This eBook will unearth both the benefits and evolution of having Mainframe SMF data readily available for analytics and visualization through the use of Syncsort Ironstream® for consumption with Splunk® Enterprise. Download this eBook for greater detail and more background on: Key advantages of a 360-degree view of your entire IT infrastructure Understanding SMF records and their value

The significance of mainframe data is ever more apparent in our daily lives. Every time you swipe your credit card, you are accessing a mainframe; every time you make a payment with your mobile phone, you are accessing a mainframe; and of course, your social security checks are generated based on data on mainframes. If we leave these critical data assets outside of the big data analytics platforms and exclude from the enterprise data lakes, it is a missed opportunity. Making these data assets available in the data lake for predictive and advanced analytics opens up new business opportunities and significantly increases business agility. In this eBook, we’ll explore the challenges associated with integrating mainframe data into Hadoop, while allowing organizations to work with mainframe data in Hadoop or Spark in its native format – and how to solve them.

Many organizations have realized the benefits of bringing their mainframe data to Hadoop. However, they quickly face challenges when they attempt to do so – specifically around connectivity, data and file types, security, compliance and overall expertise. In this whitepaper, you’ll learn about the architecture and technical capabilities that make Syncsort DMX-h the best solution for accessing the most complex application data from mainframes and integrating that data using Hadoop.

For most organizations, assuring compliance with regulatory policies that govern how mainframe data is stored and accessed is challenging. This is, in part, due to the traditional cost structure of mainframe storage, which has led companies to store massive amounts of mainframe data on tape. By securely transferring your data from mainframe to Hadoop using Syncsort DMX-h, you can replace the expensive and time-consuming practice of archiving data to tape altogether and take advantage of a compliance-friendly environment.

Fraud and cybersecurity attacks cost companies around the world hundreds of billions of dollars a year. If your organization falls victim to one of these attacks, the impact is greater than just financial. By integrating all machine data generated by networks and endpoints across the enterprise — including mainframes — you can get total visibility into the most common and dangerous threats to your organization so you can stop cyber-attackers in their tracks.

To truly expand their analytical capabilities, enterprises need more flexible, agile and efficient data integration approaches. Thankfully, a new generation of technologies is emerging to help enterprise realize this goal, from self-service tools and platforms, to cloud-based services, and real-time solutions. Download this new report for the latest technical developments and strategies in data integration today.

Enterprises use hundreds of cloud applications, and many haven’t been evaluated by IT. This form of shadow IT is often counterproductive. But both IT and business requirements can be met. This guide explains: why integration is crucial in today’s eco-system and when you should buy or build an integration platform.

After “Big Data” came “Fast Data,” the near real-time application of analytics to data so action can be taken. However, efforts have been bogged down by timeline concerns and budget constraints. This guide explains: why Fast Data is important and ways to avoid intimidating megaprojects when transforming your data architecture.

This paper proposes a different perspective on big data and asserts that it’s not the “what” of data but, rather, the “how” that really matters. It also argues that if you don’t have a well-thought strategy, you’re not going to get very far and will find yourself at a competitive disadvantage.

Odyssey has implemented predictive models that leverage streaming data and data at rest to enhance the detection of cyber threats, including botnets, malware, and zero-day exploits. In addition, behavioral models help expose abnormal user activity that may be related to potential malicious activity or insider threats.

We live in a unique time. A time when data—big or small—is forcing us to rethink everything, challenge the status quo, and solve problems we previously thought unsolvable. This paper shares a few areas we find big data, making big influences.

Concrete examples of organizations that have used the power of Apache Hadoop to advance the state of their data analytics and create efficiencies or advantages.

Learn how Komatsu Mining helps its customers optimize mine production using an IIoT analytics platform powered by Cloudera Enterprise and Microsoft Azure.

Cybraics nLighten platform, powered by Cloudera Enterprise and machine learning, detects threats conventional cybersecurity solutions miss, and decreases customers’ incident false positive rate from as much as 95 percent to less than five percent.

This TDWI report educates organizations in best practices and options for cloud business intelligence (BI) and analytics. This includes organizational strategies for the cloud as well as new platform options and other considerations. The report also examines how organizations are using cloud BI and analytics and gaining value from them.

This IDC study offers IDC analysts' collective advice to IT and business decision makers to consider in their planning for big data and analytics (BDA) initiatives.

Understand your big data and analytics maturity level against industry benchmarks and make data-driven decisions based on organizational goals.

Cloudera, along with Hortonworks, MapR, Cognizant, Trifacta and Tableau, worked with AtScale to create a better understanding of the state of Big Data today, and where it is headed tomorrow on a global scale.

By deploying Impala and allowing for self-service data discovery, Magnify can offer clients a web-based solution through which they interact directly with Hadoop. Where they used to distribute Microsoft Excel reports to customers every one or two days, deal­ers now can search on their own by customer, sales deal, or even service type. Impala is used to query millions of rows to identify specific records that match the dealers’ criteria.

Today's digital economy demands that applications be ready for anything, including growth, mixed workloads, and even catastrophic failure. The good news is that Cassandra (Apache OSS version, DataStax Enterprise Edition / DSE), a NoSQL non-relational database that is becoming increasingly popular with the emergence of enterprise use cases such as customer 360, IoT, and personalization, meets the needs for scalability and high availability without compromising performance.

In this era of big data, enterprise applications create a large volume of data that may be structured, semi-structured or unstructured in nature. In addition, application development cycles are much shorter and application availability is a critical requirement. Given these requirements, enterprises are forced to look beyond traditional relational databases to onboard the next-generation applications (on IaaS or cloud-based PaaS). NoSQL databases such as MongoDB are now being adopted and evaluated by enterprises for these applications (eCommerce, content management, etc.).

Datos IO is the first product designed specifically to meet the cloud-scale backup and recovery needs of modern, scalable, non-relational databases such as MongoDB and Apache Cassandra (DataStax), and cloud-native databases such as Amazon DynamoDB, Microsoft DocumentDB and others. These databases, and the cloud-native applications, such as analytics, IoT, and eCommerce written on them, don’t play by the same rules as we are used to in traditional data protection. This product analysis will therefore first cover how and why these new-age databases are so different from traditional relational databases, followed by an analysis of how Datos IO is addressing the problem.

In our current hyper-competitive economy, data analytics is the next frontier for innovation, competition and productivity. Many techniques and technologies are making their way into the enterprise mainstream – from embedded analytics and machine learning, to data science and prescriptive insights. Download this new report for a complete overview of the hottest trends in analytics today.

This study clearly demonstrates the benefits, timeliness and relevance of data preparation for analytics. It shows how and by whom data preparation is being driven and how the balancing act between governance and flexibility can be achieved by specifying the requirements for data preparation governance.

Many of the world’s enterprises are still running critical processes with mainframes – estimates range as high as 80% of transactional corporation data is in mainframe systems. While mainframes are well-established as systems of record, systems of engagement are emerging on a variety of open source and cloud platforms, including Hadoop and Spark. Unfortunately, in many enterprises, current mainframe data is inaccessible to these new platforms. There is a tremendous opportunity to surface mainframe data into this new world of fast-moving open technology. Organizations that have freed up mainframe data for cross-enterprise consumption are achieving greater agility, flexibility and lower costs.

In-memory technologies assure that business analytics will deliver insights to decision makers rapidly, as they need them. Now, as organizations seek to leverage real-time capabilities, in-memory has become more important than ever to strategies going forward. Along with adjacent speed-enhancing technologies, in-memory has the potential to make real-time data analytics a reality for enterprises of all types and sizes. Download this special report today to gain a deeper understanding of the key technology developments and best practices.

The complete and uninhibited portability of applications and data across any technology platform has been the dream of IT leaders, vendors, and analysts alike for decades. Now, an emerging set of solutions—containers—is making this a reality. However, data enterprises need to proceed cautiously as they embrace this promising new approach.

Building and scaling new business models to gain insights from disparate data faster, while reducing IT costs, requires an architecture that can go from prototype to petabyte scale as your needs evolve. Google BigQuery’s serverless architecture can help ensure that your enterprise data warehouse withstands growth at any scale. Informatica helps you unlock the power of hybrid data with high performance, highly scalable data management solutions that efficiently move and manage large volumes of data to Google BigQuery. Join us to get a peek under the hood and see what makes Informatica and Google BigQuery the best combination for modernizing your data architecture.

The global Telecom industry is continuing its breakneck journey with more users, more devices, more data and a shifting landscape of players and value creation. Telecom service providers (SPs) are hence rebuilding their market positions and reimagining their business systems to drive efficiencies and foster innovation.

The global payments industry is being disrupted by digitalization. A confluence of trends in technology, business, global regulations, and consumer behavior is redefining how payment transactions are executed. The industry is witnessing rapid innovation growth across the value chain, not to mention disintermediation and fragmentation.

Data continues to spread across a variety of sources. And SaaS adoption is increasing significantly, resulting in an explosion of cloud-created data. Read the report to analyze the impact of this changing landscape of disruptive data sources, including: • Use of RDBMS, big data, NoSQL and SaaS • Biggest data integration challenges • How standards can help solve data integration challenges • Current state of application adoption • Firewall concerns and solutions for connecting cloud and ground • Open analytics trend among ISVs • Data compliance requirements across industries

Today, it’s unlikely that a single database will meet all your needs. For a variety of reasons—including the need to support cloud-scale solutions and increasingly dynamic app ecosystems—startups and enterprises alike are embracing a wide variety of open source databases. These varied databases—including MongoDB, Redis and PostgreSQL— open doors to building sophisticated and scalable applications on battle-hardened, non-proprietary databases.

This report is designed to help you make an informed decision about IBM Cloudant. It is based on 43 ratings and reviews of Cloudant on TrustRadius, the trusted user review site for business software.

For developers, data scientists and IT decision-marketers.

There are many types of databases and data analysis tools to choose from when building your application. Should you use a relational database? How about a key-value store? Maybe a document database? Is a graph database the right? What about polyglot persistence and the need for advanced analytics?

The world of data management in 2017 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. As a result, two trends will continue to dominate data management discussions this year. The adoption of new “big data” technologies and the movement to the cloud. Download this report to learn about the key developments and emerging best practices for tackling the challenges and opportunities in databases today.

Check out this infographic to see how SharePlex excels at everything that matters in a database replication solution, including near-zero downtime, fast deployments, lowest total cost of ownership and more.

SQL Server 2016 is by far the best of the SQL Server family, with its outstanding in-memory performance, new security innovations, and high availability – but that's not all. Database engine rankings have already placed SQL Server 2016 over MySQL and Oracle in popularity. We will consider the new features and the upgrades to existing features offered by the latest addition to the SQL Server family. We’ll also looks at how the role of DBAs will be shaped with what SQL Server 2016 offers. This white paper also examines the system configuration in terms of hardware and software requirements necessary for SQL Server 2016 to function properly and offers insight into what you could expect if you are planning to make a move to SQL Server 2016.

Learn how to simplify the migration and upgrade process so you can avoid the risk, downtime and long hours normally associated with it – especially with a database upgrade.

This CITO Research paper explains why organizations are looking to migrate applications from Oracle to a cost-effective scale-out architecture based on Hadoop and the considerations involved in making such a decision for some or all of your workloads.

Cloud computing offers endless benefits to small and medium-sized businesses (SMEs). The world of cloud computing is currently dominated by three popular cloudcomputing services: Windows Azure by Microsoft, AWS (Amazon Web Services) by Amazon, and Google Cloud by Google. These three services have their own pros and cons as well as unique features, which lead to different pricing strategies.

To stay relevant in today’s competitive, digitally disruptive market, and to stay ahead of your competition, you have to do more than just store, extract, and analyze your data — you have to draw the true business value out of it. Fail to evolve, and your organization might be left behind as companies ramp up and speed up their competitive, decision-making environments. This means deploying cost-effective, energy-efficient solutions that allow you to quickly mine and analyze your data for valuable information, patterns, and trends, which in turn can enable you to make faster ad-hoc decisions, reduce risk, and drive innovation.

Your company already has data in Oracle® databases. But are you using additional Oracle® data solutions and the right underlying infrastructure to truly maximize the value of Oracle databases and turn data into your most important asset? If not, it’s time to start, because innovation begins not with data stored somewhere in your database landscape, but when your business managers can quickly and easily query and correlate historical, transactional, operational, non-operational, structured, and unstructured data to find patterns and trends and make reliable, fast, ad-hoc decisions. It’s that ability to use data for predictive analytics that becomes business intelligence and a competitive edge in the digital economy.

Are you analyzing data to provide value to your business? Are you making use of big data, such as audio, video (including surveillance videos), sensor data, social profiling, clickstream logs, location data from mobile devices, customer support emails, and chat transcripts? Are you analyzing big data and structured operational data side-by-side, so your business users can query it and find innovative approaches and solutions faster than ever? If not, it’s time to start, especially if your competitors have already started. Getting there might be easier than you think.

Datavail's annual assessment of the top trends in databasemanagement is based on surveys of hundreds of ITexecutives around the globe and input from our hundreds of DBAs with expertise data storage, migration, security, processing, and analytics. There are 10 trends on the shortlist this year, all springing from the same trio of forces: lower costs for data storage, greater capabilities in data processing, and cloud computing. The direction is a newkind of IT – one that is instant, invisible, and intelligent.

Oracle recently announced an impressive set of enhancements to their cloud services to remain competitive with other cloud providers such as Amazon and Microsoft. The new set of improvements includes offering selfprovisioning of new cloud services and how to integrate with legacy systems.

Today, the average enterprise has data streaming into business-critical applications and systems from a dizzying array of endpoints, from smart devices and sensor networks, to web logs and financial transactions. This onslaught of fast data is growing in size, complexity and speed, fueled by increasing business demands along with the rise of the Internet of Things. Therefore, it is no surprise that operationalizing insights at the point-of-action has become a top priority. Download this report to learn the key ingredients for success in building a fast data system.

VividCortex discuss some of the concepts that help engineering teams operate and build safely.

Is your application easy to monitor in production? Many applications are, but sadly, some are designed with observability as an afterthought.

These capabilities aren’t mere bells and whistles—the features described here are more fundamental, and though some product-specific characteristics may touch on these concepts, they also represent bigger philosophical differences in how a solution approaches its task and goals.

The Universal Scalability Law models how systems perform as they grow. This 52-page book demystifies the USL and shows how to use it for many practical purposes such as capacity planning.

Queueing theory rules everything around you. This newest version of our highly accessible, 30-page introduction to queueing theory demystifies the subject without requiring pages full of equations.

Managing databases in today's environments involves many complex problems and scenarios. This ebook presents the results of a 2016 survey conducted with the goal of discovering what data engineering teams across the country require to perform their jobs effectively, according to the team members themselves.

This buyer’s guide is designed to help you understand what database management really requires, so your investments in a solution provide the greatest possible ultimate value.

We’ve been deluged with statistics on data’s rapid growth to the point that the numbers and bytes have become almost meaningless. No one would deny that data growth is an unstoppable trend. But that’s not the issue. The real issue is how organizations can make big data meaningful when IT resources are shrinking.

Early success stories highlight the potential benefits of adopting the Apache Hadoop1 ecosystem, and within the past few years, a growing number of organizations have launched programs to evaluate, pilot, and integrate Hadoop into the enterprise. Our recent research survey sought to solicit information about the Hadoop adoption and productionalization process, and to provide insight into the current state of integration among a variety of organizations spanning different industries and levels of both individual and corporate experience. The results of the survey, presented in this research report, revealed some noteworthy findings.

Data warehouse automation (DWA) tools eliminate the manual effort required to design, deploy and operate a data warehouse. By providing an integrated development environment, DWA tools enable developers and business users to collaborate around designs and iteratively create data warehouses and data marts. This turns data warehouse development form a laborious, time consuming exercise into an agile one.

Data visualization is the most in-demand job skill in America. According to The Economist, demand for this skill increased 25-fold between 2011 and 2016. It's a specialty in the fast-growing field of data analytics where, according to Gartner, half of the four million job openings in 2015 went unfilled. This Datavail white paper explores the growing landscape of data visualization, the difficulties caused by the lack of data visualization experts, and the alternatives for dealing with these problems.

Business process and project documentation is very complex and challenging to execute well. Most companies do not have the time, resources or fortitude to accurately document their processes, nor keep those documents up to date. Good process documentation is required under standards set by ITIL, CMM and ISO 9000/9001. Documenting and updating processes is especially important in a world of increased automation. This is also the case when implementing a new solution during a project. Project documentation can often be lacking and not comprehensive enough to be referenceable for future maintenance or subsequent projects. An often-overlooked advantage of engaging with a managed services provider is the necessary documentation of your IT processes.

Business intelligence is no longer a luxury limited to enterprise-level organizations. Entrepreneurs and marketers alike understand that now, more than ever, it’s important to make data-driven decisions. The secret to making BI tools work successfully for you is a seamless collaboration with operations and IT. We’ve identified the five most common BI challenges faced by businesses like yours, with crystal clear direction on how to solve them.

To deal with an avalanche of data, many IT departments are requesting more budget. This is posing a challenge to IT executives, as they must determine whether there truly is a need to increase the budget. Is it possible that an organization can successfully carry out its IT activities without requiring more funding?

This white paper looks at both the reasons for the disinterest amongst users and the compelling reasons that users should upgrade their version of Oracle database to 12.1.

The volume of data produced by IoT devices is exploding and overwhelming traditional databases, leading to failing applications and missed opportunities. Even if applications can ingest data at the speed it arrives, the latency involved in preparing that data for analysis and decision-making can inflate the time-to-value by minutes, hours, or even days. The goal of modern IoT applications is continuous decision-making based on real-time insights derived from the most up-to-date data. To do so, they must be hosted on a data platform that meets the real-time requirements of these IoT applications. In this whitepaper, we discuss the data platform requirements of IoT applications.

SharePoint Enterprise Server. With the release of SharePoint Server 2016, running SharePoint on AWS has the same buildout as SharePoint on premises. More companies are operating SharePoint on AWS as part of a hybrid solution. Indeed, Amazon.com's IT department ran SharePoint in a hybrid environment themselves for many years. For those companies that are legally able to operate in a completely cloud environment or those running hybrid environments, SharePoint on AWS offers advantages in security, availability and scalability.

Cerner’s goal is to deliver more than software and solutions. The company is expanding its historical focus on electronic medical records (EMR) to help improve health and care across the board. Cerner aims to assimilate and normalize the world's healthcare data in order to reduce cost and increase e iciency of delivering healthcare, while improving patient outcomes. The firm is accomplishing this by building a comprehensive view of population health on a Big Data platform that’s powered by a Cloudera enterprise data hub (EDH).

Cloudera offers a fast, easy, and secure data-in-motion solution that encompasses best-in-class software for ingestion, processing, and serving of your data. Our expertise, gained over hundreds of real-time use cases, ensures we can get your data-in-motion solution into production quickly, yielding a rapid return on investment.

To stay on top of the changing nature of the data connectivity world and to help enterprises navigate these changes, this paper explores the results of the 2016 Data Connectivity Outlook survey. In this third annual, vendor-neutral survey, 680 global companies of every size and across many industries have shared their responses to questions about their currently installed database technology as well as planned direction for the next two years. The objective of this report is to provide an overview and analysis of the current state of the data connectivity marketplace as well as anticipated trends. When deciding what database technologies you need in the future, it is important to consider a technology that not only meets your current needs but will remain a prominent force in the marketplace. This report also gives you critical insight into how organizations are leveraging their on-premise legacy data to provide essential business intelligence.

The urgency to compete on analytics has spread across industries. However, many companies are finding that the traditional approach to data warehousing is no longer sufficient to meet new analytics demands. The rise of cloud-based technologies and services will continue to play a huge role in the future of data warehousing, accompanied by greater automation and self-service capabilities. The incorporation of Hadoop and other big data technologies, including in-memory computing, will also continue to grow significantly and pave the way for brand new applications.

This Datavail white paper identifies and explains the major components of Oracle BI Cloud Service (BICS) and how to get started. You’ll learn what BICS is, what the features and benefits are, the difference between on-premise and cloud, and how to decide what will work best for your organization.

According to a recent survey, business and IT professionals cite “overcoming organizational culture” as the biggest challenge they face when trying to adopt or implement an enterprise data governance strategy. Without effective cross-functional communication and collaboration, you cannot create a culture that embraces data governance as an underlying principle of successful business. Professionals trying to establish a data governance strategy should take advantage of a framework of best practices that identifies business problems and their impact and facilitates a culture of cooperation. Using such a framework as a guide, you can set a data governance strategy in motion, secure executive sponsorship, and realize early success that can support broader initiatives. In this white paper, learn best practices for designing and implementing a successful, long-term enterprise data governance strategy.

We are living in a new age, one in which your business success depends on access to trusted data across more systems and more users faster than ever before. Whether you’re responsible for technology or information strategy, you need to enable your business to have real-time access to reliable information to make rapid, accurate decisions faster than your competitors. Otherwise, your company will simply be left behind. By taking the actions detailed in this paper, you can create and set in motion a data quality strategy that supports your existing business initiatives and easily scale to meet future needs.

The results from Syncsort’s third annual Hadoop Market Adoption Survey are in! As users gain more experience with Hadoop, they are building on their early success and expanding the size and scope of Hadoop projects. Download this report to find out what 250+ IT decision-makers have to say.

The significance of mainframe data is ever more apparent in our daily lives. If we leave these critical data assets outside of the big data analytics platforms and exclude from the enterprise data lakes, it is a missed opportunity. Making these data assets available in the data lake for predictive and advanced analytics opens up new business opportunities and significantly increases business agility.

Liberating your mainframe data for bigger insights is critically important. Syncsort combines cutting-edge technology and decades of experience with both mainframe and Big Data platforms to offer the best solutions for accessing and integrating mainframe data with Hadoop. • Get mainframe data into Hadoop - in a mainframe format - and work with it like any other data source • Cleanse, blend & transform data on the cluster • Take advantage of common skillsets in your organization • Secure the entire process

According to Gartner, nearly 70% of all data warehouses are performance and capacity constrained -- so it's no surprise that total cost of ownership is the #1 challenge most organizations face with their data integration tools. This guide offers expert advice to help you get started with offloading your EDW to Hadoop. Follow these 5 steps to overcome some of the biggest challenges & learn best practices for freeing up your EDW.

New capabilities have emerged to help organizations of all sizes — but especially large, dispersed organizations — to manage their security and compliance needs as well as their overall operational efficiency. Especially important is the way IT managers in the open-systems environment can also have easy, cost-effective access in real time to the wealth of operational and security data about their organizations that resides only on z/OS systems.

When it comes to monitoring today’s complex, heterogeneous, multi-platform computing environments it is the power of IT Service Support Management tools that help infrastructure and operations teams keep a proactive eye on their key systems. Learn how an IT Service Intelligence approach extends ITSSM to provide visibility and insight into the operational health of critical IT and business services, spanning distributed systems, mainframe, and even mobile devices.

IT operations analytics is often a primary case that companies strive to address in order to lower costs and increase efficiency. One banking and financial services firm was struggling to do just that. After selecting Splunk Enterprise for IT monitoring and analytics, they found streaming real-time performance log data from the mainframe was a serious challenge. Given the track record of success in the industry, Syncsort Ironstream was the easy choice.

Contrary to industry lore, the mainframe is not “inherently” secure. While it is generally more secure than its distributed counterparts, stricter security measures are nevertheless required for today’s compliance needs — not to mention peace of mind. Download this whitepaper to learn how to get a complete view of your security environment across the entire IT infrastructure with Syncsort Ironstream.

A healthcare company needed to meet SOC2 compliance requirements, driving the need to find a solution to handle the sensitive data efficiently and securely. The company turned to Ironstream and Splunk Enterprise to be the solution they were looking for, helping them eliminate the manual processes, efforts, and costs associated with IBM’s zSecure, while also meeting the audit and compliance thresholds for SOC2 certification.

Although DBAs, at a high level, are tasked with managing and assuring the efficiency of database systems, there are actually many different types of DBAs. Some focus on logical design, others focus on physical design, some DBAs specialize in building systems and others specialize in maintaining and tuning systems. There are specialty DBAs and general-purpose DBAs. Truly, the job of DBA encompasses many roles. In this whitepaper, we’ll look at some of the different types of DBAs.

Since 80% of the work in a big data project goes toward data integration, it is vitally important to manage big data effectively.

Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage. An open source software project that enables the distributed processing and storage of large data sets across clusters of commodity servers, Hadoop can scale from a single server to thousands, as demands change. Primary Hadoop components include the Hadoop Distributed File System for storing large files and the Hadoop distributed parallel processing framework (known as MapReduce).

Cloud-baseddata presents a wealth of potential information for organizations seeking to build and maintain competitive advantage in their industries. However, as discussed in “The truth about information governance and the cloud,” most organizations will be challenged to reconcile their legacy on-premises data with new third-party cloud-based data. It is within these “hybrid” environments that people will look for insights to make critical decisions.

Any organisation wishing to process big data from newly identified datasources, needs to first determine the characteristics of the data and then definethe requirements that need to be met to be able to ingest, profile, clean,transform and integrate this data to ready it for analysis. Having done that, itmay well be the case that existing tools may not cater for the data variety, data volume and data velocity that these new data sources bring. If this occurs thenclearly new technology will need to be considered to meet the needs of thebusiness going forward.

Data is only as good as the insights it produces, the actions it influences, and the results it fosters. That’s the secret recipe for data management. Your business stakeholders depend on data-based insights to drive decisions and priorities throughout the organization. Insights based on sound data practices can give your business a competitive advantage in the marketplace. Is your data management system ready to support your business?

IBM commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying InfoSphere Information Server as part of their overall information architecture integration strategy. The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of the InfoSphere Information Server on their organizations.

Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business.

Download this white paper for free tips on how to distinguish between different types of business analytics vendors.

Read this whitepaper to learn how Cambridge Semantics has changed the game of data exploration, discovery, analytics and governance for the enterprise.

Data lakes are forming as a response to today’s big data challenges, offering a cost-effective way to maintain and manage immense data resources that hold both current and future potential to the enterprise. However, enterprises need to build these environments with great care and consideration, as these potentially critical business resources could quickly lose their way with loose governance, insecure protocols, and redundant data. Download this special report to understand the key success factors.

The Database Administrator (DBA) job description has historically involved time-consuming tasks such as installation, configuration, and troubleshooting. The arduous on-call nature of the role restricts the capacity of the DBA and, for many companies, has led to high turnover. With the advent of managed services, companies that take advantage of 24x7 support for their DBAs are liberating their DBAs from traditional roles so they have more time to engage in constructive business tasks that take advantage of their most valuable skills – their superpowers.

Robots aren't taking over the world anytime soon – but machine learning has been and will be strengthening security and automating operations for mainframes in the future. As the sheer amount of data housed on mainframes rises, daily operations have become more complex and more difficult to handle manually. Download this new eBook to learn about the challenges and issues facing mainframes today, and how the benefits of machine learning could help alleviate some of these issues.

A new generation of applications is emerging, spawned in large part by the convergence of big data, mobile computing, social media, and the Cloud. This new generation of applications, also known as “systems of engagement,” connect customers, employees, suppliers, and business partners in real time. The need for speed and enormous scale that characterize systems of engagement has exposed gaps in legacy database technologies that pose significant challenges for deployment teams tasked with ensuring that all system components integrate efficiently and reliably. Download this white paper to learn how to modernize your enterprise database architecture.

MongoDB is the leading NoSQL software. It is open-source and supported by a strong community of users. It can be used unsupported, in the community version, or supported with the enterprise edition. But it is important to keep your version updated with the latest upgrade. As of 2017, it also comes in a hosted version called Atlas, which provides both storage and software in the cloud. MongoDB can be used with a variety of storage solutions — on premises, in the cloud, or hybrid.

What if your DBA announced his or her retirement tomorrow? Or, worse, just quit on the spot? What would you do? In this white paper, we will take a deep dive into DBA supply and demand realities and what they mean for organizations across the board, issues surrounding DBA training and industry burnout, and what today’s companies can do to avoid creating a situation where DBAs become a Single Point of Failure for database management and maintenance.

One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud.

Take a closer look at how three companies capitalized on more data—almost instantly—with IBM® BigInsights® on Cloud.

So, how do you ensure availability? How do you know your complex car or your complex computing system is running smoothly? How do you tell when a small, yet critical component is failing, and disaster is just around the corner? Download this white paper to learn the answer!

Contrary to industry lore, the mainframe is not “inherently” secure. While it is generally more secure than its distributed counterparts, stricter security measures are nevertheless required for today’s compliance needs — not to mention peace of mind.

There are a number of different data sources that are available within the IBM z/OS mainframe that can be leveraged to provide insight into the operational health of the system and applications as well as visibility into security and compliance issues. This new white paper discusses emerging technologies, Splunk and Ironstream, that enable organizations to capture mainframe information and quickly move it to open-system platforms where it can be integrated and correlated with information from other platforms, analyzed for anomalies and issues, and visualized using a platform that is familiar and comfortable for today’s workforce.

When it comes to extracting actionable knowledge from data, there are two really important variables: the amount of data analyzed, and the speed of analysis. The amount of data available for analysis is piling up at a phenomenal rate, causing problems with finding room and time to analyze that data without hurting operations. Turning this data deluge into an asset has become a key priority for IT executives. Data analysis used to be done with backups of the database in off-peak hours. But in today's demanding IT environment, there isn't really an off-peak moment, and data gets stale fast. Accurate analysis requires fresh data. In many operations, two-minute-old data is expected, two-hour-old data is suspect, and two-day-old data is useless. A major solution to issues of space and latency is Oracle GoldenGate — a program designed for the easy and fast replication and migration of data from many sources to many sources.

While every company has its own specific set of requirements for the NoSQL database technology that best fits its use case(s), there’s a core set of requirements that figure into most evaluations. Those requirements fall into eight categories: Data Access, Performance, Scalability, Availability, Multiple Data Centers, Big Data Integration, Administration, and Mobile. This paper delves deeply into each core requirement and provides a comparison of leading NoSQL databases against the eight core requirements.

Today’s web, mobile, and IoT applications need to operate at increasingly demanding scale and performance levels to handle thousands to millions of users. Terabytes or petabytes of data. Submillisecond response times. Multiple device types. Global reach. Caching frequently used data in memory can dramatically improve application response times, typically by orders of magnitude.

Today’s consumers rely on their mobile apps no matter where they are. If your apps are sluggish and slow or don’t work at all when there’s no internet connection, your customers will start looking for other apps that work all the time. To help you provide the always-on experience customers demand, database solutions like Couchbase Mobile have added synchronization and offline capabilities to their mobile database offerings.

Couchbase has been named a leader in “The Forrester Wave™: Big Data NoSQL, Q3 2016,” by enabling enterprises to successfully compete in the Digital Economy across industries including eCommerce, streaming media, gaming, finance, healthcare, and more. The report examined 26 criteria to evaluate the current offering, strategy, and market presence of 15 Big Data NoSQL solutions. It cited scalability, flexibility, performance, simplicity, and cost as the primary reasons to embrace NoSQL. Couchbase was recognized for “ease of use” and the report noted that Couchbase customers use it “to support various mission-critical workloads, including operational, analytical, and mixed workloads.” Couchbase also received high scores for development, deployment, and support.

Today, you can’t pick up a magazine, read a blog, or hear a webcast without the mention of Big Data. It has had a profound effect on our traditional analytical architectures, our data management functions, and of course, the analytical outputs. This paper describes an analytical architecture that expands on the existing Enterprise Data Warehouse (EDW) to include new data sources, storage mechanisms, and data handling techniques needed to support both conventional sources of data and those supplying Big Data.

This white paper discusses the importance of employing advanced data visualization and data discovery as part of a broader enterprise business intelligence and business analytics strategy. It demonstrates how this approach will expand the scope of analytic capabilities to include self-service reporting and dashboard creation, so employees at all levels of the organizations can uncover insights and measure related outcomes – while leveraging existing tools, talent, and infrastructure.

Download this special report to under the challenges facing enterprises from Big Data, and the limitations of physical data lakes in addressing these challenges. You’ll learn how to effectively manage data lakes for improved agility in data access and enhanced governance, and discover four key business benefits of using data virtualization to fulfill the promise of data lakes.

There are many benefits that can be gained by moving database processes off-premises, including consolidating critical applications, analyzing data, enabling insights quickly and effectively running development and test environments in the cloud. The question is: how do you easily extend your data center to the cloud and keep it in sync with critical systems running on premises? Oracle GoldenGate Cloud Service enables you to move information from mission-critical, on-premises systems to the cloud—in real-time, without compromising the availability or performance of source systems, or the security of your data.

The field of business intelligence is always changing. What’s ahead? Download this eBook to find out the six critical trends and how users are becoming information activists.

This paper demystifies query tuning by providing a rigorous 12-step process that database professionals at any level can use to systematically assess and adjust query performance, starting from the basics and moving to more advanced query tuning techniques like indexing. When you apply this process from start to finish, you will improve query performance in a measurable way, and you will know that you have optimized the query as much as is possible.

For database administrators the most essential performance question is: how well is my database running? Traditionally, the answer has come from analysis of system counters and overall server health metrics. Yet, because the primary purpose of a database is to provide end users with a service, none of these counters or metrics provides a relevant and actionable picture of performance. To accurately assess database instance performance from the perspective of service provided, the question must become: how much time do end users wait on a response? To answer this question, you need a way to assess what’s happening inside the database instance that can be related to end users. Download this special white paper to learn about the response time analysis approach.

Introduced in Microsoft SQL Server 2008, Extended Events are a lightweight event-handling mechanism you can use to capture event information about the inner workings of SQL Server. Extended Events replace SQL Trace as the interface for diagnostic tracing in SQL Server 2012 and later. Download this white paper to learn how you can ruse Extended Events to improve SQL Server Performance Management.

Recent years have seen a surge in demand for easy-to-use, agile tools that provide more data analysis capabilities to business users for faster, more accurate decision-making. Both IT personnel and business users agree that business intelligence (BI) solutions should involve more users and facilitate information sharing and collaboration between teams, in order to increase content creation and consumption. This white paper covers how to deploy secure governed self-service analytics.

The desire to compete on analytics is driving the evaluation of new technologies on a large scale. Many businesses are currently starting down the path to modify their existing environments with new platforms and tools to better connect the dots between the data world and the business world. However, to be successful, the right mix of people, processes and technologies needs to be in place. This requires an analytical culture that empowers its users and improves its processes through both scalable and agile systems. Download this special report to learn about about the key technologies, strategies, best practices and pitfalls to avoid in the evolving world of BI and analytics.

This paper describes the “finger-pointing” challenge faced by DBAs and how the advanced and unique capabilities of Foglight for Databases enable DBAs to meet that challenge.

Using NoSQL does not necessarily involve scrapping your existing RDBMS and starting from scratch. NoSQL should be thought of as a tool that can be used to solve the new types of challenges associated with big data. Download this white paper to understand the key issues NoSQL can help enterprises solve.

Backing up transactional databases such as Oracle is often viewed as a complicated matter. Of particular concern is making sure the appropriate type of backup solution is in place and, importantly, that backups are actually working meaning they can ultimately be recovered. As the saying popularized by storage strategy guru Fred Moore goes, “Backup is one thing…recovery is everything.”

Oracle Mobile Cloud Service helps mobile app developers easily build engaging apps that can connect to any backend system.

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.

Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.

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.

Are you getting the most business value from your data? In this new eBook, discover five ways to overcome the barriers to better data analytics.

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.

This document briefly introduces Database In-Memory, enumerates high-level use cases, and explains the scenarios under which it provides a performance benefit. The purpose of this document is to give you some general guidelines so that you can determine whether your use case is a good match for this exciting new technology.

One of the biggest problems facing companies is how to avoid the potentially disastrous commercial consequences--and the inevitable media embarrassment--of having customer data stolen and paraded publicly. This paper provides a hands-on walk through Oracle Database Vault with Oracle Database 12c by looking at how some of its features can be used to protect real data in real world organizations.

In this whitepaper, we will discuss how key data governance capabilities are enabled by Oracle Enterprise Metadata Manager (OEMM) and Oracle Enterprise Data Quality (EDQ).

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.

In order to help customers reduce the cost of developing, testing, and deploying applications, Oracle introduced a broad portfolio of integrated cloud services. These subscription-based platform as a service (PaaS) offerings allow companies to develop and deploy nearly any type of application, including enterprise apps, lightweight container apps, web apps, mobile apps, and more.

Hybrid cloud uptake is on the rise, and the challenges of managing business-driven IT environments, in which public and private clouds can thrive, are becoming increasingly important and critical. How can you manage a hybrid cloud as one cohesive entity when the journey to cloud is so complex? How do you enable lines of business to consume IT services on-demand when you have competing stakeholder priorities? How do you manage multiple clouds when there’s a lack of insight and visibility?

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.

Oracle GoldenGate for Big Data 12c product streams transactional data into big data systems in real time, without impacting the performance of source systems. It streamlines real-time data delivery into most popular big data solutions, including Apache Hadoop, Apache HBase, Apache Hive, and Apache Flume, and facilitates improved insight and timely action.

The success of any big data project fundamentally depends on an enterprise’s ability to capture, store and govern its data. The better an enterprise can provide fast, trustworthy and secure data to business decision maker’s the higher the chances of success in exploiting big data, obtaining planned return on investments and justifying further investments. In this paper, we focus on big data integration and take a look at the top five most common mistakes enterprises make when approaching big data integration initiatives and how to avoid them.

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.

Competing in this new hyper-connected and digitized world requires a new business platform that meets the demand for speed and innovation while reducing complexity. Learn how the SAP HANA platform transforms existing systems while enabling innovation to meet future business needs nondestructively.

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.

In this paper we will review Oracle GoldenGate’s capabilities and how it can be used to achieve zero downtime migration and consolidation to Oracle Exadata. We will provide high-level implementation steps for migration with GoldenGate and a customer case study example.

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".