Databricks table ACLs let data engineers programmatically grant and revoke access to tables. With table ACLs enabled, users have no access to data stored in the cluster’s managed tables by default until granted access, thereby providing improved security for sensitive data. With Databricks runtime 3.5 or above, table ACLs allow authorized users to run SQL and Python commands against tables to which they have been granted access. But due to security vulnerabilities, table ACLs do not support R or Scala commands.
In partnership with Databricks, Immuta has engineered a data governance solution for fine-grained access (table-, row-, column- and cell-level) controls and advanced privacy techniques that work in tandem with table ACLs for Python and SQL today. The access and privacy controls are enforced natively in the Databricks platform so users do not have to change their workflows.
Many of our customers are now asking for R and Scala support for fine-grained access controls, so we’re excited to announce an early access program through which you can test the solution and provide input as soon as it’s available.
Immuta for Databricks Architecture
The big data era required separation of compute/storage to scale, which brought the rise of Apache Spark and Databricks. Now, the “personal data” era – where organizations analyze large volumes of sensitive data for analytics and data science – requires separation of platform/policy to scale. Organizations using Databricks and Immuta are adopting this architectural best practice, as it enables scaling access and privacy controls when working with personal or other sensitive data. Today, SQL and Python are supported with table ACLs; the same native architecture will extend to R and Scala while completely removing the need for table ACLs and high concurrency clusters.
Sign up for Early Access
Please register your interest with our product team to get the latest updates on when this feature will be available.