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Use Semantics to Keep Your Data Lake Clear
December 11, 2015 Blog big data Data Lakes

This article was originally published by data-informed.com and can be viewed in full here

In today’s evolving world of big data, many businesses have been inspired by the potential of the data lake approach. A data lake offers advantages to storing large volumes of heterogeneous data and, for the majority of organizations that need to analyze complex data (structured and unstructured), a data lake delays the need to integrate the data with a data warehouse.

But constructing a usable data lake with native formats presents a number of challenges that must be addressed for the data lake to fulfill its promise of making it easier and less costly to extract actionable information from data.

According to Gartner Research Director Nick Heudecker, “Data lakes typically begin as ungoverned data stores. Meeting the needs of wider audiences requires curated repositories with governance, semantic consistency, and access controls.”

It’s important not to overly constrain the data, but without sensible governance, users soon will find that accessing what they have stored is surprisingly challenging. Idle and overgrown, the data lake quickly will become a stagnant data swamp. But organizations can avoid data swamps by adding semantics to a data lake.

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