Looker Expands Big Data Analytics with Presto and Spark SQL Support

Looker, provider of a BI platform, has added support for Presto and Spark SQL as well as updates to its support for Impala and Hive.

Looker allows enterprises to describe, define and analyze the data in place to decrease the burden of moving it.

With the new support of Presto and Spark SQL, Looker says it is expanding its supported data warehouses and ensuring complete compatibility with the Amazon Elastic MapReduce (Amazon EMR) suite of frameworks.

According to the vendor, Looker alleviates the problem of slow data analysis in Hadoop, caused by the need to move subsets of data into data warehouses to perform analysis, which also results in business teams rarely having direct access. With advances in SQL query engines, the company says, big data technologies are now accessible for business analytics and Hadoop is becoming more than a data store since data analysts can now build a data model across all their data in Hadoop or other databases.

AWS customers can access all their organizational data, whether in Amazon Relational Database Service (Amazon RDS), Amazon Redshift, and, with today’s announcement, also in an Amazon Simple Storage Service (Amazon S3) data lake through one of the SQL engines supported by Amazon EMR.

"With Looker on Hadoop, data analysts can create a single source of truth for the entire enterprise, so every business team can quickly ask and answer their own questions," said Frank Bien, CEO at Looker. "Now all decision makers, not just a handful of data scientists, can utilize the valuable data in Hadoop to drive better business decisions."

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