Explore the advanced analytics platform, Part 2, Explore use cases that cross multiple industries using the advanced analytics platform

Discover how the advanced analytics platform supports multiple big data use cases

From the developerWorks archives

Dr Arvind Sathi, Mathews Thomas, Mr Jinesh Radadia, Mr Ken Kralick, and Mr Richard Lanahan

Date archived: January 13, 2017 | First published: September 24, 2013

In the first article of this series, you learned about big data and the “four Vs” that characterize this data: Volume, Velocity, Variety, and Veracity and how an integrated analytics platform supports their diverse requirements. You also saw a high-level overview of the Advanced Analytics Platforms (AAP) architecture components and how they support various aspects of big data.

The analytics platform that is created requires investment of products and sourcing of data from multiple sources. By its very nature, big data leads to extreme requirements in data volumes and velocity that make it hard to replicate data across organizational silos. The platform must support multiple use cases for an organizational data lake (a common big data store across many divisions of the organization) to justify such investment. Explore multiple successful big data use cases and the flow of one use case to learn how the architecture supports the use cases.

This content is no longer being updated or maintained. The full article is provided "as is" in a PDF file. Given the rapid evolution of technology, some steps and illustrations may have changed.



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ArticleTitle=Explore the advanced analytics platform, Part 2: Explore use cases that cross multiple industries using the advanced analytics platform
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