How to create a data lake for fun and profit

See the original posting on JavaWorld

Most credit James Dixon of the open source BI vendor Pentaho with coining the phrase “data lake.” Think of a data lake as an unstructured data warehouse, a place where you pull in all of your different sources into one large “pool” of data.

In contrast to a data mart, a data lake won’t “wash” the data or try to structure it or limit the use cases. Sure, you should have some use cases in mind, but the architecture of a data lake is simple: a Hadoop File System (HDFS) with lots of directories and files on it.

Why would you want a data lake?
The answers are both technical and political. Usually, when you start up any new project that involves analyzing your company’s data — especially when the data is stored across functional areas — you’re in for trouble. For example, if the business unit that wants the data isn’t part of the unit providing the data, what kind of priority do you think the unit providing the data likely assign to the effort? How is it budgeted? Who does the integration and how much needs to be done? How do you structure the data and for what purposes?

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