White Papers

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.

Better business insight comes from data - but data is often dirty, incomplete and complicated. As any analyst would admit, what passes for data science is more like janitorial work. Find out why that is - and how you can avoid the painful, manual and error-prone processes that have bogged down the analytics process for 30 years.

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

In today’s fast-paced mobile age, data continues to accrue by leaps and bounds. To support strategic, operational and tactical business decisions, organizations need effective data management that enables them to both consolidate data from multiple sources and distribute data to multiple targets in real time. For example, a department store chain could consolidate and analyze sales data from geographically dispersed stores to provide valuable insight for inventory management, and it could use data distribution to send selective data updates based on demographics to individual stores in order to increase sales. Download this tech brief to learn how database replication provides a cost-effective way to consolidate and distribute data in real time.

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

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.

This white paper summarizes the results of the Edison Group’s evaluation of web portals for viewing and interacting with data models. The web portals evaluated were created for the following data modeling tools: CA ERwin Data Modeler (ERwin), Embarcadero ER/Studio (ER/Studio), and Sybase PowerDesigner (PowerDesigner).

TransLattice Elastic Database (TED) is the first geographically distributed relational database management system (RDBMS) that spans multiple sites simultaneously. TED’s performance and reliability are not determined by conditions at any single site because there is no single point of vulnerability. TED is truly a single database without a single repository designed to allow any application to move to the cloud or virtualized environments while providing superior availability, reliability and accessibility.

Got DBMS challenges? Solve them with NuoDB - the leading distributed SQL database. Download this technical whitepaper for an under-the-hood look at the NuoDB architecture. Explore the internals of the database, the management model and the key differentiators of the technology. Unlike traditional, relational databases or popular NoSQL solutions, the NuoDB distributed database is designed for the needs of a modern, connected company like yours. It can be deployed in any datacenter, in any cloud, anywhere, without the compromises of other NewSQL database solutions.

Learn about what a distributed database is, the four main design approaches and why you need one. See how NuoDB, the leading distributed cloud database can provide scale-out on demand, geo-distributed data management, and resilience to failure.

Explore how ISVs are differentiating their offering with NuoDB’s cloud-scale DBMS, whether in the cloud, on premise or as SaaS.

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.

Oracle Enterprise Manager Grid Control (OEM Grid) has capabilities which, when properly configured, can dramatically improve the performance of the entire Oracle software and hardware stack, from applications to disk storage. These capabilities include the OEM Grid, the Oracle Management Repository, and OEM Grid metric templates. OEM Grid configuration using Datavail's Alert Optimizer and custom templates helps eliminate unwanted alerts, while enriching actionable alerts, and improving the performance of the entire database system. This whitepaper explains how.

Today’s exponential data growth is stressing databases and DBAs alike. That’s why many companies are looking for options on how to manage the day-to-day operations of their databases and still have time to gain traction on strategic projects, such as upgrades, migrations, tuning and integration. Whether it’s augmenting an existing team, handling production support, enabling coverage of second and third shifts, supplying half a DBA, or providing complete multi-tiered coverage for one or more database environments, Datavail can deliver the solution.

Microsoft's SharePoint collaboration software is an excellent tool for enterprise users, but some individuals have pointed to it as the source of data leaks — incorrectly so. SharePoint requires the same security planning applied to any other network asset. It also must be properly implemented to prevent hackers from taking advantage of default or misconfigured settings. Six ideas are offered here to help IT professionals bolster their SharePoint security.

Trends come and go, but some new ideas in database management are not simply flavor-of-the-month fads. Many have staying power and the potential to transform organizations. What are the current trends in database management and how can you take best advantage of them to benefit your organization?

Successful businesses recognize that information is a strategic tool that can help them gain advantage in today’s marketplace and transform the way they interact with customers. However, applications must also scale to levels that were unimaginable just a few years ago, and scaling alone isn’t enough. Companies also require that their applications are always available and lightning fast. This combination is where traditional databases fail. Technology leader O’Reilly notes that the characteristics of modern data “exceed the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the structures of your database architectures. To gain value from this data, you must choose an alternative way to process it.”

The NoSQL database market is expected to grow at a rate three times faster than that of the relational market during the next few years, understandably making newcomers to big data technology eager to understand why and how it fits into their organizations. The needs for speed, scale, continuous availability, location independence, ability to manage all types of data, and cost reduction are driving this increasing adoption.

Looking back over the decades since the database management system (DBMS) became everyday technology, the role of the database administrator (DBA) has changed significantly. Applications and operating environments have become more complex and reduced staffing levels and outsourcing mean there are fewer people to do the work.

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

MultiValue (MV) applications have a long history of successfully automating critical processes in both large and small companies worldwide. Now, after almost 40 years the technologies which underpin these solutions no longer provide the advantages they once did. The creation of new MV applications has virtually ceased and existing applications are rapidly being retired or moved to other technologies. As CIO/CTO’s contemplate the modernization of over 100,000 of these applications they are often confronted with common assertions as to the power of MV and why it would be difficult or impossible to move to other more modern tools and databases.

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 discusses how online applications and social media have changed the concept of speed of delivery for data. Mobile apps and connective devices altered the notions of how data is collected and transmitted. GPS, sensor and other types of machine-to-machine data sources disrupt the perception of what data should or could look like. Moreover, with cloud infrastructure implementation time frames measured in weeks, not months, both business and Information Technology (IT) stakeholders have changed the way they think about data management projects, and in particular how data models and business and technical metadata are addressed.

This paper provides the guidelines to adopt Model as a Service (MaaS) through technology enabling Open Data MaaS engagement and application along the Database as a Service (DaaS) lifecycle. Accordingly, we introduce MaaS agile design and deployment enabled by CA ERwin and explain how to map Open Data requirements and best practices. With MaaS, data models can be shared, off-line tested and verified to define data designing requirements, data topology, performance, placement and deployment, so that models themselves can be supplied as a service. Data models allow you to verify “on-premise” how and where data should be designed to meet the Cloud service’s requisites. As a consequence, models can be tuned based on real usage and then mapped to the Cloud service. Further, MaaS inherits the defined service’s properties so data models can be reused, shared and classified for new Cloud Services design and publication.

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.

If your organization relies on data, optimizing the performance of your database can increase your earnings and savings. Many factors, both large and small, can affect performance, so fine-tuning your database is essential. Performance-tuning expert Chuck Ezell sheds light on the right questions to get the answers you need by using a defined approach to performance-tuning, referred to as the 5 S’s.

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.

Managers of database administrators have a recurring problem: they need to hire experts to keep their systems running, only to see their high-priced talent maddeningly chained to pesky requests and problems that could be handled by less-expensive employees. Outsourcing allows organizations to have people with the exact skills required at the moment they are needed. In this white paper, we explore the top 10 issues facing managers of DBAs and how outsourcing solves some of these pressing challenges by providing reliable and flexible staffing.

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.

It’s IT’s dirty little secret. Every time you touch database schema there’s a 76% chance it will break your application. Download the whitepaper: Bringing sexy back…to the database. Learn why it’s not a tools issue. It's a process issue. Understand how a model-based approach and rich interface for authoring schema changes across multiple platforms can eliminate manual scripting, provide fast, accurate application deployment, and break through change request backlogs. After all, you don’t need more SQL. You need less.

This white paper explores the advantages of EMC for Oracle for Oracle DBAs and discusses how EMC is delivering real advantages in performance, management, and cost.

Read about the challenges affecting multi-database environments, how server virtualization is being deployed, and how EMC private cloud solutions for Oracle can meet those challenges.

This IDC Vendor Spotlight – examines the issues of choosing the right infrastructure, fast successful adoption of databases, applications versions and vendors, and finally integrated support between database and application providers without adding risk to the business.

See how Callaway Golf implemented a “split-mirror” backup solution to ensure high-performance, high-reliability and recoverability, as well as perform backups to offset the loads on primary SAP servers.

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.

The use case introduced identifies key actions, requirements and practices that can support activities to help formulate a plan for successfully moving data to the Cloud.

CA ERwin Data Modeler enables organizations to collect and serve data models from and to any web data source and data management system in the cloud.

This white paper by industry expert Alec Sharp illustrates these points and provides specific guidelines and techniques for a business-oriented approach to data modeling. Examples demonstrate how business professionals.

Relational database management systems (RDBMSs) are systems of software that manage databases as structured sets of tables containing rows and columns with references to one another through key values. They include the ability to optimize storage, process transactions, perform queries, and preserve the integrity of data structures. When used with applications, they provide the beating heart of the collection of business functions supported by those applications. They vary considerably in terms of the factors that impact the total cost of running a database application, yet users seldom perform a disciplined procedure to calculate such costs. Most users choose instead to remain with a single vendor's RDBMS and never visit the question of ongoing hardware, software, and staffing fees.

The sheer volume and complexity of information generated by today’s enterprises has created the pressing need for a next-generation of business applications and database solutions. These solutions must be capable of handling a massive number of transactions and meeting the demands of an ever-growing population of concurrent users. SAP Sybase Adaptive Server Enterprise (SAP Sybase ASE) is a high-performance transactional database platform that is optimized for SAP Business Suite software and designed to deliver the exceptional performance and reliability required in the big data era. IBM Power Systems is a highly scalable UNIX platform tuned for SAP application and data management software. In collaboration, the SAP-IBM alliance of solutions create the optimal landscape for heightened performance levels to keep up with real-time business and increased operational efficiency in the face of growing complexity – the best of both worlds.

While the database landscape is growing with information quantities and varying data types, it is evident that the RDBMS will retain its position as the system of record for the enterprise. The old criteria for selecting a RDBMS platform have been superseded and today’s RDBMS evaluation principles seek to enhance reliability, adaptability, scalability, predictability, and manageability. With its remarkable heritage and bright future as part of SAP’s flagship information management technology, SAP Sybase ASE merits inclusion onto any enterprise’s RDBMS vendor selection checklist.

Today's database technology landscape is more dynamic than ever before. With growing data volume, data types and data uses, many organizations are using an array of specialized information management technologies to manage their data. Despite fresh breakthroughs and noteworthy solutions, identifying and selecting the right relational database management (RDBMS) platform is still a vital obligation and transactional databases continue to remain at the heart of the enterprise’s information processing responsibilities. In optimizing data management technologies in the organization, one must learn how the latest trends in information management are affecting IT organizations, why a ‘one size fits all’ database no longer makes sense, and how integrated solutions such as the SAP Real Time Data Platform can provide a logical, unified approach to data management.

Find out how SAP Sybase Adaptive Server Enterprise helps companies address key challenges in the areas of performance, reliability, and efficiency when it comes to exponential data growth. SAP Sybase ASE offers high data-processing responsiveness and throughput, and predictable, consistent performance.

Oracle GoldenGate 11g Release 2 is the most feature rich, robust, and flexible data replication product on the market today. Written for business project owners, key stakeholders, and the entire IT organization, this white paper provides an overview of the new features in Oracle GoldenGate 11g Release 2.

This white paper provides a broad overview of the state of the data integration market. It examines the trends that are driving data integration technology forward and motivating businesses to undertake data-intensive projects such as business intelligence, data warehousing, data quality, consolidation, cloud computing and IT modernization initiatives.

This white paper describes Oracle’s mature, well-crafted strategy for meeting the new data integration requirements. Oracle Data Integration delivers pervasive and continuous access to timely and trusted data across heterogeneous systems. It includes a broad family of products designed to deliver maximum performance with low cost of ownership, ease of use, and reliability. Its comprehensive capabilities support the fundamental requirements of the enterprise including real-time and bulk data movement, data transformation, bi-directional replication, data services, data federation, and data quality for customer and product domains.