Business intelligence platform, Looker, 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.

The 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. With Looker users can analyse  data while it still resides on the big data platform, allowing data analysts to build a data model across all their data in Hadoop databases no matter if they’re in the cloud or on premise.

Looker now supports Presto and Spark SQL and allows access to data in Amazon Relational Database Service (Amazon RDS), Amazon Redshift, or, with this announcement, in an Amazon Simple Storage Service (Amazon S3) data lake and accessed through any of the SQL engines supported by Amazon EMR.