Big Data Analytics Blog Posts
See the most recent Big Data Analytics related items below.
Top Twelve Priorities for Data Warehouse Modernization
By Philip Russom, Senior Research Director for Data Management, TDWI
No matter the vintage or sophistication of your organization’s data warehouse (DW) and the environment around it, it probably needs to be modernized in one or more ways. That’s because DWs and requirements for them continue to evolve. Many users need to get caught up by realigning the DW environment with new business requirements and technology challenges. Once caught up, they need a strategy for continuous modernization.
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Posted on March 24, 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
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
Take a look at 5 new resources that can help you evolve your analytics strategies beyond spreadsheets and dashboards. Create more value from your data when you move beyond simple business intelligence (BI) reporting to data discovery and advanced analytics. Use these recently released resources to develop your competitive advantage.
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Posted by TDWI on May 15, 20150 comments
By Philip Russom, Research Director for Data Management, TDWI
To help you better understand Hadoop’s evolution into mainstream enterprise usage—and why you should care—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 enterprise Hadoop and its best practices in a form that’s compact, yet amazingly comprehensive.
Every tweet I wrote was a short sound bite or stat bite drawn from my recent TDWI report
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Posted by Philip Russom, Ph.D. on April 27, 20150 comments
Attendees of a recent TDWI Webinar asked excellent questions.
By Philip Russom, TDWI Research Director for Data Management
Recently, on April 14, I broadcast a TDWI Webinar in which I presented some of the findings from my new TDWI report on "Hadoop for the Enterprise." You can download a free copy of the report in a PDF, and you can replay the Webinar. With each link, you may need to scroll down to find what you want. If you’re new to Hadoop, you may wish to first read the 2013 TDWI Best Practices Report Integrating Hadoop into Business Intelligence and Data Warehousing.
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Posted by Philip Russom, Ph.D. on April 15, 20150 comments
Evolving Best Practices for Data Management
By Philip Russom, TDWI Research Director for Data Management
I recently broadcast a really interesting Webinar with David Lyle, a vice president of product strategy at Informatica Corporation. David and I had a “fireside chat” where we discussed one of the most pressing questions in data management today, namely: How can we prepare great data for great analytics, while still leveraging older best practices in data management? Please allow me to summarize our discussion.
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Posted by Philip Russom, Ph.D. on February 2, 20150 comments
Attendees of a recent TDWI Webinar asked excellent questions.
By Philip Russom, TDWI Research Director for Data Management
Recently, on Tuesday April 15, 2014, I broadcasted a TDWI Webinar in which I presented some of the findings from my new TDWI report, Evolving Data Warehouse Architectures in the Age of Big Data. You can download a free copy of the report in a PDF file. And you can replay the Webinar.
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Posted by Philip Russom, Ph.D. on April 30, 20140 comments
By Philip Russom
Research Director for Data Management, TDWI
To help you better understand the ongoing evolution of data warehouse architectures and why you should care, I’d like to share with you the series of 35 tweets I recently issued on the topic. I think you’ll find the tweets interesting because they provide an overview of big data management and its best practices in a form that’s compact, yet amazingly comprehensive.
Every tweet I wrote was a short sound bite or stat bite drawn from my recent TDWI report Evolving Data Warehouse Architectures in the Age of Big Data. Many of the tweets focus on a statistic cited in the report, while other tweets are definitions stated in the report.
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Posted by Philip Russom, Ph.D. on April 15, 20140 comments