By Philip Russom, Senior Research Director for Data Management, TDWI
As any data warehouse professional can tell you, the average data warehouse (DW) is today evolving, extending, and modernizing, to support new technology and business requirements, as well as to prove its continued relevance in the age of big data and analytics. This process has become known as data warehouse modernization; synonyms include DW augmentation, automation, and optimization. Every user organization and its DW is a unique scenario, so every modernization program is, too. Even so, a few common situations, drivers, and outcomes have arisen.
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Posted on March 15, 20160 comments
By David Stodder, Senior Research Director for Business Intelligence, TDWI
Hard to believe, but the New Year is over a month old now and moving by fast. TDWI just finished its first Conference of the year in Las Vegas, which included the co-located Executive Summit chaired by me and my TDWI colleague, Research Director Fern Halper. The Summit was fantastic; many thanks to our great speakers, sponsors, and attendees. Other industry events focused on TDWI’s core topics are coming up, including the TDWI Solution Summit in Savannah, Strata and Hadoop World, and Gartner Business Intelligence & Analytics Summit. So, it’s time to check the condition of my shoes, luggage, and lumbar vertebrae (have to stop carrying that heavy computer bag) because they are all about to get a workout.
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Posted on February 10, 20160 comments
New big data sources and data types – and the need to get business value from new data – are forcing organizations to evolve their data management practices.
By Philip Russom, TDWI Research Director for Data Management
I recently participated as a core speaker in the Informatica Big Data Ready Virtual Summit, sharing a session with Amit Walia, the Chief Product Officer at Informatica Corporation. Amit and I had an interactive conversation where we discussed one of the most pressing questions in data management today, namely: How should an organization get ready to capture and leverage big data? This is an important question, because many organizations in many industries are facing big data, with its new data sources, data types, large volumes, and fast generation rates. Organizations need to modernize their data integration (DI) infrastructure, so they can capture and leverage the new data for new business insights and analytics.
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Posted on January 6, 20160 comments
By David Stodder, TDWI Director of Research for Business Intelligence
A tsunami of big data is hitting many organizations and the demand for faster, more frequent, and more varied analytics is riding the crest of that wave. Organizations want to apply predictive analytics, stream analytics, machine learning, and other forms of advanced analytics to their key decisions and operations. They are also experiencing the rise of self-service visual analytics, which is whetting the appetite of nontechnical users throughout organizations who want do more with data than they can using standard business intelligence (BI) reports and spreadsheets.
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Posted by David Stodder on December 21, 20150 comments
An Introduction to Apache Spark and its uses in Business Intelligence (BI), Data Warehousing (DW), and Advanced Analytics
Blog by Philip Russom
Research Director for Data Management, TDWI
At TDWI, we’re hearing a lot of interest in Apache Spark, although it’s still new and most users are unfamiliar with it. So, please allow me to define Spark for you, explain its potential benefits, and describe actual use cases.
Apache Spark is a parallel processing engine. It specializes in big data, and works well with Hadoop environments. However, Apache is not just for Hadoop; it provides parallel processing for other environments, too. Spark is known for high speed and low latency, which it achieves by leveraging in-memory computing and cyclic data flows.
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Posted by Philip Russom, Ph.D. on December 7, 20150 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
To help you better understand what today’s emerging technologies and methods (ETMs) are – especially those related to business intelligence, analytics, and data warehousing – I’d like to share with you the series of 25 tweets I recently issued on the topic. I think you’ll find the tweets interesting, because they provide an overview of ETMs in a form that’s compact, yet amazingly comprehensive.
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Posted by Philip Russom, Ph.D. on November 9, 20150 comments