We’re living in a world of big data.

The current generation of line-of-business computer systems generate terabytes of data every year, tracking sales and production through CRM and ERP.
It’s a flood of data that’s only going to get bigger as we add the sensors of the industrial internet of things, and the data that’s needed to deliver even the simplest predictive-maintenance systems.Having that data is one thing, using it as another.

Big data is often unstructured, spread across many servers and databases. You need something to bring it together.

That’s where big data analysis tools like Apache Spark come into play; these distributed analytical tools work across clusters of computers.

Building on techniques developed for the MapReduce algorithms used by tools like Hadoop, today’s big data analysis tools go further to support more database-like behavior, working with in-memory data at scale, using loops to speed up queries, and providing a foundation for machine learning systems.To read this article in full, please click here

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