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Barry Devlin

As one of the founders of data warehousing back in the mid-1980s, a question I increasingly ask myself over 25 years later is: Are our prior architectural and design decisions still relevant in the light of today's business needs and technological advances? I'll pose this and related questions in this blog as I see industry announcements and changes in way businesses make decisions. I'd love to hear your answers and, indeed, questions in the same vein.

About the author >

Dr. Barry Devlin is among the foremost authorities in the world on business insight and data warehousing. He was responsible for the definition of IBM's data warehouse architecture in the mid '80s and authored the first paper on the topic in the IBM Systems Journal in 1988. He is a widely respected consultant and lecturer on this and related topics, and author of the comprehensive book Data Warehouse: From Architecture to Implementation.

Barry's interest today covers the wider field of a fully integrated business, covering informational, operational and collaborative environments and, in particular, how to present the end user with an holistic experience of the business through IT. These aims, and a growing conviction that the original data warehouse architecture struggles to meet modern business needs for near real-time business intelligence (BI) and support for big data, drove Barry’s latest book, Business unIntelligence: Insight and Innovation Beyond Analytics, now available in print and eBook editions.

Barry has worked in the IT industry for more than 30 years, mainly as a Distinguished Engineer for IBM in Dublin, Ireland. He is now founder and principal of 9sight Consulting, specializing in the human, organizational and IT implications and design of deep business insight solutions.

Editor's Note: Find more articles and resources in Barry's BeyeNETWORK Expert Channel and blog. Be sure to visit today!

Stefan Groschupf, CEO of @Datameer (from the German Sea of Data), does great one-liners. At the #BBBT last Friday, he suggested that Datameer could be seen as the Business Objects of the Hadoop world. And it's that thought that leads me to data marts.

As one of the oldest and most divisive debates in business intelligence, it's clear that the time-to-value discussions of data warehouse vs. data mart also apply to Hadoop. Hadoop is increasingly being used to integrate data from a wide variety of sources for analysis, begging the question: do it in advance for data quality or do it as part of the analysis to reduce time to value? Datameer is clearly a data mart.

And in the big data world, it's certainly not the only data mart type of offering. What's different about Datameer is that it has been around for nearly 5 years and has an impressive customer base.

At an architectural level, we should consider how the quality vs. timeliness, mart vs. warehouse trade-off applies in the world of big data. Read more on this at my new blog location.

Posted August 19, 2014 3:28 AM
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Stefan Groschupf, CEO of @Datameer (from the German Sea of Data), does great one-liners. At the #BBBT last Friday, he suggested that Datameer could be seen as the Business Objects of the Hadoop world. And it's that thought that leads me to data marts.

As one of the oldest and most divisive debates in business intelligence, it's clear that the time-to-value discussions of data warehouse vs. data mart also apply to Hadoop. Hadoop is increasingly being used to integrate data from a wide variety of sources for analysis, begging the question: do it in advance for data quality or do it as part of the analysis to reduce time to value? Datameer is clearly a data mart.

And in the big data world, it's certainly not the only data mart type of offering. What's different about Datameer is that it has been around for nearly 5 years and has an impressive customer base.

At an architectural level, we should consider how the quality vs. timeliness, mart vs. warehouse trade-off applies in the world of big data. Read more on this at my new blog location.

Posted August 19, 2014 3:28 AM
Permalink | No Comments |
eat elephant.jpgHadoop vendors Hortonworks, Cloudera and, most recently, MapR have all amassed substantial cash stashes. This has triggered much speculation about both who will win the lion's share of the the big data market and how the elephant will rampage through the data warehousing landscape. Missing from such debate is an understanding of the central role of information management and its automation in the evolution and eventual success of data warehousing.

Although showing rapid evolution, the Hadoop software environment is still focused on fundamental database, data manipulation and similar technologies. In data warehousing, the focus long ago shifted to ensuring data quality and consistency, from modeling business requirements all the way through to production delivery and ongoing maintenance. We see this in tools such as Wherescape and Kalido, built by teams who had to develop and support real, ongoing and changing business intelligence needs.

Read the full story at my new blog location: Now... Business unIntelligence.

Posted July 11, 2014 12:43 AM
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eat elephant.jpgHadoop vendors Hortonworks, Cloudera and, most recently, MapR have all amassed substantial cash stashes. This has triggered much speculation about both who will win the lion's share of the the big data market and how the elephant will rampage through the data warehousing landscape. Missing from such debate is an understanding of the central role of information management and its automation in the evolution and eventual success of data warehousing.

Although showing rapid evolution, the Hadoop software environment is still focused on fundamental database, data manipulation and similar technologies. In data warehousing, the focus long ago shifted to ensuring data quality and consistency, from modeling business requirements all the way through to production delivery and ongoing maintenance. We see this in tools such as Wherescape and Kalido, built by teams who had to develop and support real, ongoing and changing business intelligence needs.

Read the full story at my new blog location: Now... Business unIntelligence.

Posted July 11, 2014 12:43 AM
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Although the yellow elephant continues to trample all over the world of Information Management, it is becoming increasingly difficult to say where more traditional technologies end and Hadoop begins.

Flying Elephant londonjunglebook8.jpg

Actian's (@ActianCorp) presentation at the #BBBT on 24 June emphasized again that the boundaries of the Hadoop world are becoming very ill-defined indeed, as more traditional engines are adapted to run on or in the Hadoop cluster.

The Actian Analytics Platform - Hadoop SQL Edition embeds their existing X100 / Vectorwise SQL engine directly in the nodes of the Hadoop environment. The approach offers the full range of SQL support previously available in Vectorwise on Hadoop. Architecturally as interesting, is the creation and use of column-based, binary, compressed vector files by the X100 engine for improved performance and the subsequent replication of these files by the Hadoop system. These latter files support co-location of data for joins for a further performance boost.

This is, of course, the type of integration one would expect from seasoned database developers when they migrate to a new platform. Pivotal's HAWQ has Greenplum technology embedded. It would be surprising if IBM's on-Hadoop Big SQL offering is not based on DB2 knowledge at the very least.

The real point is that the mix and match of functionality and data seen here emphasizes the conundrum I posed at the top of the blog. Where does Hadoop end? And where does "NoHadoop" (well, if we can have NoSQL...) begin? What does this all mean for the evolution of Information Management technology over the coming few years?

Read full post.


Posted June 26, 2014 8:44 AM
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