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April 2017, Vol. 6, No. 4

Accelerate Apache Spark to boost big data platforms

So, we have data -- lots and lots of data. We have blocks, files and objects in storage. We have tables, key values and graphs in databases. And increasingly, we have media, machine data and event streams flowing in. It must be a fun time to be an enterprise data architect, figuring out how to best take advantage of all this potential intelligence -- without missing or dropping a single byte. Big data platforms such as Spark help process this data quickly and converge traditional transactional data center applications with advanced analytics. If you haven't yet seen Spark show up in the production side of your data center, you will soon. Organizations that don't, or can't, adopt big data platforms to add intelligence to their daily business processes are soon going to find themselves way behind their competition. Spark, with its distributed in-memory processing architecture -- and native libraries providing both expert machine learning and SQL-like data structures -- was expressly designed for performance with large data sets. ...

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