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Looker has announced support for Presto and Spark SQL as well as updates to its support for Impala and Hive. Looker allows enterprises to describe, define and analyse the data where it lives, significantly eliminating the time, expertise, and the cost burdens of moving the data. Today’s announcement expands Looker’s list of supported data warehouses, such as Amazon Redshift, and ensures complete compatibility with the Amazon Elastic MapReduce (Amazon EMR) suite of frameworks.
Until today, it was painfully slow to do data analysis in Hadoop. Typically, data analysts had to move subsets of data into data warehouses to perform analysis and, as a result, business teams rarely had direct access. Today, thanks to advances in the SQL query engines, big data technologies are finally accessible for business analytics and the vision of Hadoop as more than a data store is now a reality. Data analysts can now build a data model across all their data in Hadoop or other databases, easily transform raw data into meaningful metrics and allow business teams to utilise years of stored data in Hadoop.
“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 utilise the valuable data in Hadoop to drive better business decisions.”
“Looker makes the data in Hadoop easy for everyone to access and explore in a single platform. With Looker, we can see and respond to the impact of product changes immediately, greatly improving our customer experience,” said Mike Van Kempen, Sr. Business Analyst at Acorns.
“To make meaningful business decisions, all individuals within an organisation must have easy access to tools for performing business analytics with Hadoop,” said Anurag Gupta, Vice President, Database Services, Amazon Web Services, Inc. “Looker's support of Presto and Spark SQL helps AWS customers access all their organisational data, whether in Amazon Relational Database Service (Amazon RDS), Amazon Redshift, or, with today's announcement, in an Amazon Simple Storage Service (Amazon S3) data lake accessed through one of the many SQL engines supported by Amazon EMR.”
