We are excited to announce that Immuta’s latest product release is here, offering deeper native integrations with Databricks and Snowflake alongside overall performance enhancements. In this blog, we’ll highlight our new key capabilities:
- Databricks Spark Integration with Unity Catalog – Customers can use Immuta’s Databricks Spark integration to enforce fine-grained access control on clusters in Databricks Unity Catalog-enabled workspaces.
- Support for Data Sources with More Than 1600 Columns – Immuta can now handle data sets and sources with tables that contain more than 1600 columns.
- Snowflake Lineage Tag Propagation – Snowflake customers using Immuta are now able to have upstream data tags propagate automatically whenever they add new data sources or new tags.
- Enhanced Schema Monitoring Scale for Snowflake – Immuta provides customers with enhanced schema monitoring on Snowflake.
- Support for Databricks PrivateLink – Immuta provides AWS PrivateLink support so customers can leverage private networking to connect to Databricks for enhanced data security.
- Handle Unnecessary Row Access Policies in Snowflake Integration – Immuta enhances users’ performance by making it easier to incrementally onboard a subset of Snowflake users into the Immuta platform.
Let’s explore these features in greater detail.
The following features are available to the general public.
Databricks Spark Integration with Unity Catalog Support
Immuta continues to work alongside Databricks as Unity Catalog builds towards full native integration. Customers can now use Immuta’s Databricks Spark integration on Databricks clusters to enforce fine-grained access controls (table-, row-, and column-level support) in a workspace with Unity Catalog enabled.
Databricks Runtime 11.2 Support
Furthering our deep strategic partnership with Databricks, Immuta is one of the first data security platforms to support Databricks Runtime version 11.2. Our customers will find this support to be an extremely valuable option because it allows them to use the most recent version of the Databricks runtime with confidence that data security controls are in place.
Support for Data Sources with More Than 1600 Columns
Because of limitations imposed by the underlying Postgres database, Immuta historically has been able to support up to 1,600 columns at a time. However, data volumes continue to grow rapidly and we understand that some customers have specific use cases for data sources with tables exceeding these limits. That is why we enhanced Immuta to now support data sources and use cases with more than 1600 columns.
The following features are available in private preview.
Snowflake Lineage Tag Propagation
Customers need their data inventory in Immuta to stay updated so that their policies are effectively protecting their data assets. But an up-to-date inventory requires more than just a list of data sources; customers place tags on those data sources to capture business and semantic context about their data so that policies can be built around them. While Immuta already solves automatic data discovery (through schema monitoring), applying tags to those new data sources is often manual. Snowflake customers can now have upstream data tags propagate automatically in Immuta whenever they add new data sources or tags.
Below is a model of Snowflake Lineage Tag Propagation with Immuta.
Enhanced Schema Monitoring Scale for Snowflake
Customers rely on Immuta’s schema monitoring to find new data sources, new columns, and make modifications to existing columns. To ensure data is easily and quickly accessible, schema monitoring performance needs to be efficient at all times. Snowflake schema monitoring performance has been improved with this release, so that there is never a need to reduce how frequently it is run or even turn it off. Customers always remain completely aware of their data surface area without any manual intervention.
Databricks PrivateLink (SaaS Only)
As Databricks introduced the ability to set up an AWS PrivateLink between the Databricks instance and the clients that need to access it, we see more customers begin to leverage private networking options where Immuta SaaS interacts with Databricks as a data source. Immuta now supports AWS PrivateLink so customers are able to leverage private networking to connect to Databricks for enhanced security.
Handle Unnecessary Row Access Policies in Snowflake Integration
Before Snowflake table grants were available, Immuta used row access policies to filter out all rows when a user did not have privileges to query the table. Immuta can now use SELECT privileges via Snowflake table grants to enable a user to query a table. This feature dramatically enhances users’ performance since it removes unnecessary row access policies and makes it easier for Snowflake users to onboard Immuta’s data security platform.
Getting Started with Immuta
At Immuta, we continue innovating our data security platform with the goal of removing the most challenging parts of the data access control and governance management. These latest key new features help data teams simplify and accelerate processes while maximizing ROI on leading cloud data platforms like Snowflake and Databricks.
Ready to try it yourself?
Check out Immuta’s self-guided demo to start connecting data sources and building policies.Self-Guided Demo