Databricks acquires data visualisation firm Redash
Move will help the firm to create a "larger and more thriving" open source system
The move means that Databricks can now offer its own visualisation capabilities instead of relying on partners, creating a more complete set of tools for data scientists and analysts.
The transition will see a hosted version of Redash integrated into the Databricks platform to create a "rich visualisation and dashboarding experience", the company says, which is now available to users signed up to its private preview program.
Databricks announced the acquisition at this years' virtual Spark + AI Summit 2020, but financial terms of the deal were not revealed.
"Our acquisition of Redash was driven not only by the great community and product they’ve developed, but also the same core values we share,” commented Reynold Xin, Chief Architect and Founder of Databricks, in a company post.
"Both our organisations have sought to make it easy for data practitioners to collaborate around data, and democratise its access for all teams."
Collaborative virtualisation and dashboarding platform Redash is designed to enable users to share insights across teams, as well as explore, query and visualise their data.
The platform includes a number of tools and functions, including a query editor for composing SQL and NoSQL queries, visualisation and dashboards, scheduled refreshes, alerts, REST API, as well as broad support for data sources.
"We're excited to welcome Arik and the Redash team to Databricks, and to further develop Redash together and deliver a seamless and more powerful experience for our customers and the broader open source communities," Xin said.
Alongside the Redash acquisition, Databricks has also introduced Delta Engine, a high-performance query engine built on Spark 3.0. Built from the ground up, the solution has been designed to boost the performance of its Delta Lake transaction layer.
The company says the addition will enable customers to leverage a unified data analytics platform that can support "any data use case" and result in "meaningful operational efficiencies and cost savings".
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