White Papers

This collection of articles substantiates growing concerns over application performance in cloud and/or virtualized environments. While cost reduction is a primary reason enterprises move from traditional datacenter infrastructure to distributed computing, the economics don’t pan out if application performance and, subsequently, user adoption suffer. However, a good understanding of performance in the cloud or in virtualized environments demands a wholly different approach to monitoring, as explained in the following articles authored by technology strategists and members of the Compuware APM Center of Excellence.


In December 2013, Compuware commissioned Research in Action to conduct a global, independent survey of 740 senior IT professionals’ attitudes and concerns relating to cloud computing. The results revealed that businesses worry that the reduced visibility and control that accompanies their move to the cloud hampers their ability to deliver a high quality end-user experience with applications. The study shows that the Service Level Agreements (SLAs) offered by cloud providers are failing to address the needs of their customers, as they are too simplistic.


This collection discusses the common performance issues encountered when managing jobs in a Hadoop environment. Whether you are running Hadoop on-premise or utilizing a cloud-hosted MapReduce environment, or a combination of the two, this collection will give you real-world examples of how to improve the distribution and utilization of your big data deployment.


Any business analyst will tell you they have a love-hate relationship with Excel. While purpose-built for calculations, graphing and reporting, it has also been the only user-friendly tool available for manipulating data pre-analytics. That's where the "hate" part of the relationship comes in. Most Excel "jockeys" will tell you that they spend way too much time hand-crafting data: using filters to find flaws, creating pivot tables to find outliers, writing VLOOKUPs, scripting, blending, screaming, and yelling. As the clock ticks and deadlines loom, Excel simultaneously becomes the lock and the key to every analytic exercise. Accelerate your path to analytics with a modern approach to data preparation. This eBook shows you how.


Watch this new webcast to learn how to evolve your software and databases to support your changing business needs without affecting performance. Find out how to reduce risk, compare database objects, automate scripting and research, and replay database workload to simulate the production environment.


Big data got you down? Watch this new webcast with Oracle Ace Bert Scalzo to learn how to organize current data stores, use tools to create and maintain successful data warehousing and business intelligence solutions, transform existing OLTP models, answer critical questions and plan for the future.


While Oracle Real Application Cluster (RAC) allows you to scale databases horizontally, it’s not without its limitations. Join the webcast to learn how you can get real-time data replication that helps you reduce risk and downtime while enhancing performance and infrastructure.


Learn how leading online dating site, eHarmony, implemented a state-of-the-art data replication solution to help its subscribers find their perfect match and enhance strategic decision-making.


While enterprise adoption of Hadoop is expanding, it brings new types of challenges, from manual coding demands, skills requirements, and a lack of native real-time capabilities. Learn the steps to success for adopting Hadoop-based big data analytics, and find out about a special solution that allows you to mix and match both real-time and analytics workloads.


This paper summarizes the issues healthcare institutions face today with legacy RDBMS + SAN data environments and why the combination of MarkLogic, Apache Hadoop, and Intel provides a government-grade solution for Big Data.


This paper summarizes the issues financial services companies face today with legacy RDBMS + SAN data environments and why the combination of MarkLogic, Apache Hadoop, and Intel provides a solution for enterprise Big Data.


This paper summarizes the issues public agencies face today with legacy RDBMS + SAN data environments and why the combination of MarkLogic, Apache Hadoop, and Intel provides a government-grade solution for Big Data.


Hadoop is great for storing and analyzing data, but it still needs a database. Hadoop is simply not designed for low-latency transactions required for real-time interactive applications, or applications that require enterprise features such as government-grade security, backup and recovery, or real-time analytics. The real benefits of Hadoop are realized only when running alongside an enterprise grade database.


Are you leveraging all of Toad’s powerful features? In this on-demand webcast, Oracle ACE and Toad Product Architect Bert Scalzo discusses Toad’s hidden functionality, and how it can make your job easier. Watch it today.


In this on-demand webcast, Oracle ACE and Toad Product Architect Bert Scalzo discusses 10 powerful and hidden features in Toad® that help increase your productivity and DB performance. Watch this webcast today.


See how to migrate or upgrade your Oracle database with minimal risk and downtime, and use SharePlex to integrate with modern systems like Hadoop.


Discover how real-time replication technology can help you easily meet your business continuity goals — and reduce costs. Watch the on-demand webcast.


NoSQL databases are seen by many as a more elegant way of managing big, and occasionally small, organizational data. This paper is for technology decision-makers confronting the daunting process of selecting from this fast-growing category of data management technologies. It will introduce a set of comparative features that should be used when selecting a NoSQL technology for your workload and your enterprise. There are many common features across NoSQL databases, but even these have implementation nuances that should be understood.


Forrester Research details how one large transportation company implemented Dell Toad™ for Oracle®, and in a five year span saw a 2,667% ROI and savings of over $49 million. When evaluating Dell’s award-winning database solution, Toad for Oracle, a commissioned study conducted by Forrester Consulting on behalf of Dell analyzed four fundamental elements of total economic impact: • Costs • Benefits to the organization • Strategic flexibility options • Risk For the study, Forrester conducted in-depth interviews with a Toad customer at a large Oracle shop to determine the ways in which Toad can save organizations money and the extent to which Toad delivers return on investment. What follows are the highlights of this study.


Now more than ever, big data, social media, and the consumerization of IT have created a huge demand for data analysts. Today's analysts are highly skilled, highly empowered and highly productive. Government agencies need to understand how to capitalize on the investment they are making into these super analysts. In this article, information expert and executive consultant John Weathington discusses how organizations can take advantage of the resources they already have by increasing productivity using new Toad Business Intelligence software.


Dell Software commissioned leading government research provider Market Connections, Inc. to poll federal IT administrators on awareness of, and attitudes toward, the use of data replication and integration tools, especially the features they deem most critical when selecting a tool. This white paper explores the findings of that poll and assesses how IT managers in federal agencies are faring with their data management strategies.


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.


While the hype surrounding NoSQL database technology has become deafening, there is real substance beneath the often exaggerated claims. But like most things in life, the benefits come at a cost. Developers accustomed to data modeling and application development against relational database technology will need to approach things differently. This white paper highlights the differences between a relational database and a distributed document-oriented database, the implications for application development, and guidance that can ease the transition from relational to NoSQL database technology.


Today, enterprises are supporting hundreds or even thousands of databases to meet growing business demand. With most organizations supporting Lean and Agile application development initiatives, IT organizations are being pressured to deliver applications in months, if not weeks. Although DBMS technology has improved in automation over the years, provisioning and administering databases for application development remains a bottleneck, largely because of lack of database administration (DBA) and system resources, limited IT budget, complexity of IT infrastructure, and lack of priority to enterprise databases. As a result, many enterprises are struggling with new application development to innovate, remain competitive, and deliver improved services in the age of the customer.


Postgres has advanced significantly in recent releases. With new features and capabilities alongside several longstanding components and extensions, Postgres can support virtually all of today’s data types as well as unstructured and semi-structured data. This is meaningful for two reasons: -Postgres can power many applications written for NoSQL technologies. -Developers can build applications in Postgres that achieve the same results as NoSQL solutions.


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.


Real-time information is becoming increasingly critical to drive business decisions, retain customers and achieve a competitive advantage. However, performing data analysis in real time requires all data to be in a single system, and most organizations today have multiple real time and contextual data sources. The fact that data typically resides in multiple locations - the data center, the cloud and in SaaS applications - makes matters even more complicated. The difficulty of delivering analytics increases exponentially as businesses strive to gain valuable insights across their expanding variety of data sources and platforms. Traditional data management approaches are simply not agile enough to respond to the business demands for faster analysis.


Recent database market dynamics have led many people to question the utility of data modeling. This document will explain: Why the roles for NoSQL and Big Data are broadly misunderstood today How market enthusiasm for these loosely-defined domains has challenged traditional data modeling assumptions Why data modeling is more important than ever How organizations that seek to fully leverage database market dynamics must redouble their focus on data modeling


Today, the use of NoSQL technology is rising rapidly among Internet companies as well as the enterprise. Three interrelated megatrends – Big Data, Big Users, and Cloud Computing – are driving its adoption. Download this white paper to gain a deeper understanding of the key advantages NoSQL technology offers and if your organization should consider joining the growing ranks of users


Enterprise data growth has caused challenging infrastructure demands that continue to stretch IT budgets and resources. Many try to resolve it by throwing more hardware at the problem which introduces complexity. A growing storage and data center footprint means more dollars spent that could better be invested in innovative revenue-generating projects. How do you solve this Big Data infrastructure problem without breaking the bank?


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.


Leveraging data is one thing; tracking and managing change, auditing, and other aspects of regulatory compliance requires savvy administrators and sophisticated systems. This white paper explores these challenges as well as the tools and best practices that will help your business effectively navigate through seas of data. You’ll learn how you can: • Simplify data integration • Overcome the limitations of the Extract, Transform and Load (ETL) process • Ensure data availability through the Change Data Capture (CDC) technique • And more


Miami-Dade County needed to streamline database administration and development to ensure that critical government services are constantly available and aligned to the needs of local citizens. The county deployed Dell™ Toad for Oracle and Dell Toad for SQL Server, which accelerate database management and development tasks including the following benefits: • County improved services for its citizens • DBAs increased their productivity and quickly resolved issues • Developers worked smarter with improved data visualization • IT staff demanded additional Toad licenses


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. A new data warehousing architecture is emerging, along with a new generation of techn


Today’s users demand reports with better business insights, more information sources, real-time data, more self-service and want these delivered more quickly, making it hard for the BI professionals to meet such expectations. This white paper outlines how the use of Data Virtualization can help BI professionals to accomplish these goals.


This white paper discusses businesses making huge gains by leveraging more data than ever before for both analytics and operations—to improve customer service, speed products to market, cut costs and more. They could use large amounts of data from a growing number of sources—that is the good news. That is also the bad news. For data consumers, whether they are end-users or application developers, easy access to relevant data to speed time to market is the key need. For IT serving them, agile data provisioning that is efficient, high-performing and securely managed is the key challenge.


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.


SQL-on-Hadoop solutions have become very popular recently as companies solve the data access issues with Hadoop or seek a scale-out alternative for traditional relational database management systems. However with all of the options available, choosing which solution is right for your business can be a daunting task.


This white paper outlines the most important aspects and ingredients of successful DB2 for z/ OS performance management from DBTA columnist and data management expert Craig Mullins. It offers multiple guidelines and tips for improving performance within the three major performance tuning categories required of every DB2 implementation: the application, the database and the system.


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


Big data and cloud data are still hurtling outward from the ”Big Bang.” As dust settles, competing forces are emerging to launch the next round of database wars -- the ones that will set new rules for connectivity. Information workers at all levels require easy access to multiple data sources. With a premium cloud-based service, they can count on a single, standardized protocol to inform their most business-critical applications.


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 WORLD OF BUSINESS INTELLIGENCE IS EVOLVING. Not only do organizations need to make decisions faster, but the data sources available for reporting and analysis are growing tremendously, in both size and variety. This special report from Database Trends and Applications examines the key trends reshaping the business intelligence landscape and the key technologies you need to know about. This Best Practices feature is sponsored by Oracle, Attunity, Tableau, Objectivity and Pentaho.


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