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Three incremental, manageable steps to building a “data first” data lake

Jack Norris, Senior VP Data and Applications, MapR Technologies | July 26, 2016
Instead of extracting, transforming and loading data into separate analytic clusters or data warehouses, converge data so all applications can use it in real time

Such transformational operational insight and agility requires the ability to quickly analyze and understand streaming data in context. The context comes from understanding both short- and long-term trends and patterns from both data-at-rest and incoming data.

And this raises an important point: To get maximum benefit from having a data lake, the applications need to be able to work with both data-in-motion and data-at-rest. Many data analysts tend to consider “Big Data” as always being at rest, marveling at its volume and variety, and can lose sight of the fact that all of that data was created one event at a time from a wide range of sources—old and new, batch and transactional.

Indeed, it is this ability to harness many different data flows, and to understand their meaning in context and in real-time, that should be considered the hallmark of a successful data first data lake. And when that ability is achieved, the data lake becomes “enterprise grade” and ready to take on truly transformational applications.

The three incremental steps outlined here can enable any organization to approach a data first data lake the prudent way: feet first. And by helping to build competence and instill confidence, these small first steps will clear the way to diving down deep to discover the operational insights and competitive advantages previously hidden beneath the surface.


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