Data analytics contender Databricks[1] offers a platform that, along with the open source Apache Spark[2] technology on which its core is based, has long been a favorite for attacking streaming data, data engineering and machine learning workloads. It has also "moonlighted" as a SQL analytics platform, able to accommodate popular business intelligence (BI) tools, and their queries, in a pinch. Today, Databricks is announcing SQL Analytics, a set of interface and infrastructure capabilities that transform SQL analytics on the platform from mere sideline to first-class use case.
The big ticket items are the addition of a SQL Analytics Workspace user interface to the Databricks platform, as well as the ability to create dedicated SQL Analytics Endpoints. The former leverages technology derived from Databricks' acquisition of Redash[3], announced in June. The latter are clusters dedicated to ad hoc analytics/BI workloads, and allow customers to leverage more fully the Delta Engine[4] capabilities added to the core Databricks platform, also in June.
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Users of the Databricks platform -- including both Azure Databricks[5] and the Unified Data Analytics Platform[6] service hosted on Amazon Web Services -- already had the ability to create SQL-based notebooks. Cells in those notebooks can accommodate SQL queries and present the results in tabular form or as relatively simple visualizations which, in turn, can be combined into a special dashboard view of the notebook. These capabilities accommodate rudimentary analysis and BI workloads, but in reality function more as a convenience feature in service of the data engineering and machine learning workloads at which Databricks has excelled.
The new SQL Analytics Workspaces, meanwhile, are available in