Hadoop is used for many different purposes, especially as a runtime environment for new types of advanced analytics (65 percent), as memory for raw/detailed data (60 percent), and for data processing and integration (57 percent). A variety of other scenarios have also been implemented.

Use Cases for Hadoop

Use cases for Hadoop (n=144)

A breakdown by region paints an interesting picture in which North America uses Hadoop more intensively as a runtime environment for BI.

Using Hadoop as memory for raw/detailed data varies greatly between North America and Europe. 76 percent of respondents in Europe use it for this purpose in comparison to only 15 percent in North America.

One possible explanation is that with growing experience and maturity, Hadoop usage is shifting from simple data storage more towards an analytic engine as a runtime environment for BI. This would also mean that North America has more experience in analytic Hadoop usage as a whole and, therefore, focuses on other use cases.

Furthermore, using Hadoop as a runtime environment for advanced analytics is more popular in Europe (an 11 percent difference) than in North America. It appears that Hadoop is not “just” used in North America for advanced analytics, exploration or other new forms of analysis but rather as the technology itself to support both old and new analytic tasks. One could assume, therefore, broader usage and, in certain cases, better utilization of potential opportunities in this market as well.


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Use Cases, Benefits and Limitations

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