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.
“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, CHIEF AI OFFICE, COGNOA
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.
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.
Results for Data Teams
40% Increase in data engineering productivity when managing
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.