Hadoop has limitations. But the relational database management systems used for data warehousing do, too. Luckily, their strengths are complementary.
By Philip Russom, TDWI Research Director for Data Management
In a recent blog in this series, I discussed “The Roles of Hadoop” in evolving data warehouse architectures. (There’s a link to that blog at the end of this blog.) In response, a few people asked me (I’m paraphrasing): “Since the Hadoop Distributed File System (HDFS) is so useful, can it replace the relational database management system (RDBMS) that’s at the base of my current data warehouse and its architecture?”
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Posted by Philip Russom, Ph.D. on September 2, 20130 comments
I am in the process of collecting data for my TDWI Best Practices Report on predictive analytics. The report will look at trends and best practices for predictive analytics. Some specific issues being investigated in the survey include: Who is using predictive analytics? What skills are needed for it? Is it being used in big data analysis? Is it being used in the cloud? What kind of data is being used for predictive analytics? What infrastructure is supporting it? What is the value that people using it are getting from it? The survey is slated to run another week, so if you haven’t had the chance to take it yet, please do. Here is the link:
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Posted by Fern Halper, Ph.D. on August 12, 20130 comments
HDFS and other Hadoop tools promise to extend and improve some areas within data warehouse architectures
By Philip Russom, TDWI Research Director for Data Management
In a TDWI survey I designed and ran in 2012, 88% of the users surveyed reported that the Hadoop ecosystem of products is a business opportunity (not a technology problem) because it enables new types of applications. When asked which types of applications benefit most from Hadoop, survey respondents chose (in priority order) big data analytics, advanced analytics (i.e., data mining, statistical analysis, and complex SQL), and discovery analytics. After these three analytic application types, respondents then chose two data management use cases for Hadoop, namely information exploration and complementing a data warehouse. Other data management uses seen in the survey include data archiving, transforming big data for analytics, and data staging.
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Posted by Philip Russom, Ph.D. on August 4, 20130 comments
Many Enterprise Data Warehouses (EDWs) are evolving into multi-platform Data Warehouse Environments (DWEs)
By Philip Russom, TDWI Research Director for Data Management
Analytics, big data, real time, and unstructured data present new data warehouse (DW) workloads.
Workload-centric DW architecture. One way to measure a data warehouse’s architecture is to count the number of workloads it supports. According to the TDWI Survey on High-Performance Data Warehousing of 2012, a little over half of user organizations surveyed (55%) support only the most common workloads, namely those for standard reports, performance management, and online analytic processing (OLAP). The other half (45%) also supports workloads for advanced analytics, detailed source data, various forms of big data, and real-time data feeds.
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Posted by Philip Russom, Ph.D. on July 26, 20130 comments
Good information and analytics are vital to enabling organizations of all stripes to survive tumultuous changes in the healthcare landscape. The latest issue of TDWI’s
What Works
in Healthcare focuses on data-driven transformations in healthcare. I wrote an article for the issue that looks at some of the business intelligence and analytics issues surrounding the transition from a traditional, fee-for-service system to a value-based, “continuum of care” approach. One thing is clear: The importance of data and information integration as the fabric of this approach cannot be overstated.
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Posted by David Stodder on July 17, 20130 comments
Geospatial data can be extremely powerful for a wide variety of use cases. Geospatial analysis – i.e. the practice of incorporating spatial characteristics in various kinds of analysis- has been incorporated in BI and visualization solutions for at least several years. Recently I’ve been hearing a lot from vendors about geospatial applications and using geospatial data in a range of more advanced analytics. In a recent TDWI technology survey, you can see that the geospatial analytics is growing in importance. We asked the respondents, “What kind of analytics are you currently using in your organization today to analyze data? In three years?” and “What kinds of techniques and tools is your organization using for big data analysis both today and in three years?” In the figure below, the 39% of respondents were currently using geospatial analysis and this number jumped to 79% in three years. The number of respondents answering affirmatively that geospatial analysis would be used in their big data solutions in three years was 81%.
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Posted by Fern Halper, Ph.D. on July 1, 20130 comments