Integrating Analytic Data Using Data Virtualization

More Data = Better Analysis

The analytic data domain includes all kinds of data, including:

  • traditional enterprise data from sources like the Enterprise Data Warehouse;
  • big data from sources like Hadoop;
  • cloud data from SaaS applications and public providers;
  • social media data from sites like Facebook and Twitter; and
  • personal/desktop data from spreadsheets and flat files.

Data virtualization is an optimal solution to integrate all these sources, thereby improving analysis and business insight.

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Data Challenges in the Analytics Pipeline

Data is the lifeblood of analytics — the more diverse the better.

In their best-selling book, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Mayer-Schonberger and Cukier describe the synergy that occurs when previously unrelated and disparate data is brought together to uncover hidden insights. But these advanced analytics data requirements are a double-edged sword as these more diverse sources complicate data integration and constrain progress.

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Big Data Common Sense

Back to Basics

Velocity, volume, and variety seem to be the 3 V’s that everyone talks about when it comes to Big Data. What happened to the most important V, Value?

So when it comes to Big Data, my advice is don’t get knocked off guard by the Big Data buzzwords. Go back to business and technology basics, and you’ll be fine.

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Data Virtualization Returns to the BBBT

Since 2010, Composite Software has kicked off the Boulder BI Brain Trust’s new year. In 2013 we continue this tradition on Friday January 4.

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Data Virtualization Insights from the Pacific Northwest BI Summit 2012

I am back at Composite Software headquarters after attending my fifth Pacific Northwest BI Summit.

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The Role of Data Virtualization in a World of Big Data

If you are reading this blog entry, you certainly are aware of the rising importance of data virtualization as a key enabler for BI agility, IT cost savings and more.

Big data is an ever bigger trend, solving new classes of business problems with new types of analytics that leverage today’s unprecedented data volumes , varieties and velocities.

How do these trends intercept? And what are the consequences for the business and IT?

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Is it time to Embrace a Hybrid Data Ecosystem?

From a data management strategy point of view, we all understand the critical importance of the enterprise data warehouse. Yet new business imperatives and new technologies have significantly changed the data management landscape, hereafter changing the role of the EDW.

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Data Virtualization Is Hot at the Gartner BI Summit

A Fantastic, Immersive Opportunity

Last week I attended the Gartner BI Summit in Los Angeles.  While I am an avid follower of Gartner’s Information Management, Analytics and BI research, it was great to hear this content directly from the analysts and gauge the reaction of the thousand plus attendees.

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Composite + RainStor = Big Data Management Flexibility

Last week Composite and RainStor announced that Composite has added access to RainStor’s Big Data management database to our long list of popular enterprise applications we support including Oracle, SAP, and salesforce.com as well as relational and multi-dimensional data sources from IBM, IBM Netezza, Microsoft, Oracle, SAP BW, SAP Sybase and Teradata.

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What is Your Strategy for Data Virtualization?

Five Popular Data Virtualization Usage Patterns

Data virtualization is a versatile data integration solution that can be deployed to solve a wide range of data integration challenges.  Based on nearly ten years of successful implementations, several common usage patterns have emerged to help guide your enterprise’s data virtualization adoption strategy.

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