This white paper highlights the performance and scalability potential of InfoSphere DataStage 8.1 based on a benchmark test simulating a data warehouse scenario. The benchmark is designed to use the profiled situation to provide insight about how InfoSphere DataStage addresses key questions customers frequently ask when designing their information integration architecture.
This paper describes what operational analytics is and what it offers to the business. We explore its relationship to business intelligence (BI) and see how traditional data warehouse architectures struggle to support it.
IBM DB2 offers multi-platform flexibility and optimized capabilities for a variety of workloads. This e-book highlights some common scenarios where DB2 helps businesses derive unprecedented value from expanding data stores—affordably and reliably.
Speed, simplicity and affordability: 3 capabilities businesses need from their warehousing environment. IBM DB2 with BLU Acceleration gives organizations a complete, multipurpose environment to rapidly distill insight from their data, make timely decisions and capitalize on opportunities.
According to Dr. Barry Devlin of 9sight Consulting, the truth behind all the talk about big data and the possibilities it can offer is not hard to see, provided that organizations are willing to return to the principles of good data management processes.
For many years, companies have been building data warehouses to analyze business activity and produce insights for decision makers to act on to improve business performance. These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse. Typically, a history of business activity is built up over a number of years allowing organizations to use business intelligence (BI) tools to analyze, compare and report on business performance over time. In addition, subsets of this data are often extracted from data warehouses into data marts that have been optimized for more detailed multi-dimensional analysis.
Speed, simplicity and affordability: 3 capabilities businesses need from their warehousing environment. IBM DB2 with BLU Acceleration gives organizations a complete, multipurpose environment to rapidly distill insight from their data, make timely decisions and capitalize on opportunities
These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse.
Business Intelligence (BI) has become a mandatory part of every enterprise’s decision-making fabric. Unfortunately in many cases, with this rise in popularity, came a significant and disturbing complexity. Many BI environments began to have a myriad of moving parts: data warehouses and data marts deployed on multiple platforms and technologies – each requiring significant effort to ensure performance and support for the various needs and skill sets of the business resources using the environment. These convoluted systems became hard to manage or enhance with new requirements. To remain viable and sustainable, they must be simplified.
Fortunately today, we have the ability to build simpler BI technical environments that still support the necessary business requirements but without the ensuing management complexity. This paper covers what is needed to simplify BI environments and the technologies that support this simplification.
Decision makers need data and they need it now. As the pace of business continues to accelerate, organizations are leaning heavily on data warehouses to deliver analytical grist for the mill of daily decisions. This Research Report from Aberdeen Group examines the benefits of data warehouse solutions that offer rapid information delivery while minimizing complexity for users and IT.
Download this ebook to learn the requirements for delivering trusted information to a modern data warehouse and the guiding principles for trusted information in next generation data warehouse environments.
This paper focuses on the benefits of a queryable data store and big data technologies available to support data warehouse modernization. Read the paper to understand how data can be stored and optimized with Hadoop.
IBM believes the Data Warehouse market continues to expand and adapt to address new requirements for user self-service, increased agility, requirements for new data types, lower cost solutions, adoption of open source, driving better business insight, and faster time to value.
The market offers an array of choices to organizations planning new data warehouses to manage large and varied data sets. Most vendors emphasize the speed of their products, but few address the real need: to increase speed efficiently, which reduces complexity and cost by simplifying data warehousing.
Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse.
In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative. Is the new technology really complementary or is the reign of the data warehouse nearing an end?