Databricks Data Access Control

Immuta’s native integration with Databricks helps organizations automatically secure and govern sensitive production analytics and data science projects. For organizations that work with highly sensitive data — from global banks, to healthcare and life sciences, to government agencies, to tech and consumer brands — Immuta plus Databricks is the modern solution for maximizing data utility and data security.

Access Control

Apply dynamic row level security, column-level security, and even cell-level protection on every Spark job and query across SQL Analytics and Data Science workspaces.

Data Access

Understand who accessed what data, when, and for what purpose.

Data Catalog

Enable democratized, subscription-level data access with always-on security and access control.

“We needed to expedite our data processing, while also finding a way to dynamically anonymize sensitive information for reporting. We therefore required a solution that could help us enforce data access roles, permissions and policies beyond the standard resource- or table-based control levels.”

Halim Abbas



Fine-Grained Access Control

Securing analytics data is often a manual effort requiring data copies, manually stripping out or anonymizing sensitive information and provisioning role-based access to specific tables. With Immuta, you can now dynamically apply row-level security, column-level security and data masking, and cell-level data protection to secure sensitive data without copying it, manually preparing it or managing role-based access. Immuta’s modern, attribute-based access controls (ABAC) are dynamically enforced on Spark jobs and queries across SQL Analytics and Data Science workspaces, providing fine-grained security over sensitive analytics data while vastly improving data engineers’ productivity.


Data Access Auditing

Data engineers today must ensure all analytics data and data use is compliant with a complex, growing set of regulatory and business rules. Immuta and Databricks have greatly simplified this process by ensuring all Spark jobs are automatically logged in Immuta. Legal teams can view detailed audit logs and reports that show data consumers’ access levels, intended purposes and query history — all in plain English.


Self-Service Data Catalog

While the market is filled with offerings that claim to provide a unified data catalog, most do not enable true self-service, subscription-based access to live data due to the inherent security risks. Immuta’s active data catalog is built on a strong security foundation with always-on governance and access control. As a result, Databricks analysts and data scientists can use Immuta to search, explore and subscribe to data sources. Immuta is always working in the background to ensure local and global data policies are applied dynamically to Spark workloads and queries across SQL Analytics and Data Science workspaces.


Immuta’s Automated Data Access Control platform – now natively integrated with Databricks – enables organizations to perform data science faster and more securely by dynamically protecting and anonymizing data. Databricks customers can enforce fine-grained data access controls directly within Databricks’ Apache Spark™ unified analytics engine for Big Data and machine learning, and Delta Lake, its open-source storage layer for Big Data workloads.

Results for Data Teams

40% Increase in data engineering productivity when managing
sensitive data.

25%-90% Increase in permitted use cases for cloud analytics by safely unlocking sensitive data.

Reduce to seconds what can be a months-long process to provide self-service data access.

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Reference Materials