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SPARK is Replacement for MapReduce in Bigdata Real Analytics!

SPARK Vs MapReduce
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Apache Spark is among the Hadoop ecosystem technologies acting as catalysts for broader adoption of big data infrastructure. Now, Looker -- a vendor of business intelligence software -- has announced support for Spark and other Hadoop technologies. The goal? To speed up access to the data that fuels business decision making.

Hadoop's arrival on the scene 10 years ago may have started the big data revolution, but only recently did adoption of this technology begin spreading to a wider audience. Apache Spark is one of the catalysts for the growing adoption rates.

Spark can be used as a replacement for MapReduce, a component of Hadoop implementations, to speed up the processing and analytics of big data by 100x in memory, according to the Apache Software Foundation.

In today's business environment, in which real-time analytics is the goal and organizations don't want to wait for data warehouses and analysts to provide batch intelligence back to business users, Spark has gained momentum.

And here's one case in point: Looker, a business intelligence platform used by Avant, Acorns, and Etsy, this week announced support for Presto and Spark SQL. The company also updated its support for Impala and Hive, other Hadoop ecosystem technologies that speed up analysis on Hadoop.

Looker's announcement of support for these additional Hadoop ecosystem technologies lets organizations "leave data in Hadoop and process it at speed and at scale," said James Haight,

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