TDWI Research Report Helps Organizations Prepare for New Data Warehouse Architectures
Report explores trends in data warehouse architectures and discusses how newly available, relevant technologies can be used to leverage new business opportunities for big data.
SEATTLE, WA, April 3, 2014—TDWI Research announced the release of its newest Best Practices Report, Evolving Data Warehouse Architectures in the Age of Big Data. This original, survey-based research focuses on the many recent changes in data warehouse architectures to help organizations plan for and deploy the next generation of their logical and physical data warehouse architecture.
“In the early days of data warehousing, most data warehouses were centered around a single-instance database,” notes Philip Russom, author of the report. Now, he points out, there’s a “strong trend toward more and more ‘edge’ systems, many of which are designed for workloads that the average data warehouse isn’t optimized to handle, especially when you consider big data.
“Today’s data warehouse is complemented by several types of data platforms. NoSQL databases, Hadoop, real-time technologies, and new analytic tools all complicate the modern architecture landscape,” says Russom.
Russom introduces readers to the variety of data warehouse architectures, discusses the components of these architectures, and examines what is driving their evolution. The report explores the problems and opportunities for these architectures and identifies the barriers to success that enterprises may encounter.
The report discusses:
- Multi-platform data warehouse environments
- Workload-centric and distributed architectures
- The evolution of a single-platform enterprise data warehouse to a multi-platform data warehouse platform and the architectural strategies for both
- Hadoop’s roles in data warehouse architectures and the promising uses of the technology
- The top 10 priorities for data warehouse architectures and why each is important
Russom explores in depth how integrating Hadoop’s file system with a relational database management system (RDBMS) alleviates the limitations of both and discusses Hadoop’s limitations relative to RDBMSs used for data warehousing. He also explains how the technologies can complement each other and how they should be used together.
The report also examines trends in data warehouse architectural components and reveals the results of a TDWI survey showing which architectural components are most frequently used, which enterprises say they will adopt within three years, and which they will ignore.
This research was sponsored by Actian, Cloudera, Datawatch, Dell Software, HP Vertica, and MapR Technologies.
About the Author
Philip Russom is a well-known figure in data warehousing and business intelligence, having published more than 500 research reports, magazine articles, opinion columns, speeches, Webinars, and more. Today, he’s the TDWI Research Director for Data Management at The Data Warehousing Institute (TDWI), where he oversees many of the company’s research-oriented publications, services, and events. Before joining TDWI in 2005, Russom was an industry analyst covering BI at Forrester Research and Giga Information Group. He also ran his own business as an independent industry analyst and BI consultant and was a contributing editor with leading IT magazines. Before that, Russom worked in technical and marketing positions for various database vendors. You can reach him at firstname.lastname@example.org, @prussom on Twitter, and on LinkedIn at linkedin.com/in/philiprussom.
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