As data teams modernize their data stacks for the cloud and adopt multiple cloud data platforms, it’s becoming harder to provide consistent, secure data access across each enterprise. Each platform has different access and privacy controls, which creates a challenging architecture for managing security and risk while preserving data utility and value.
To manage data access and security now and in the future, across multiple cloud data platforms, data teams need a modern “control center” to balance data access and utility consistently across platforms — in other words, a modern, cloud-native approach to access control, governance, and data privacy. With our latest release, available today to prospects and customers, Immuta can deliver on this promise for organizations so they can confidently leverage the benefits of the cloud data ecosystem.
This release includes expanded and enhanced capabilities for data teams, including:
- Native integration for Starburst
- Expanded Databricks access control for R and Scala
- Native integration for Trino, formerly known as Presto SQL
- Regulatory policies in Snowflake for HIPAA Safe Harbor and CCPA
With these new product features, data engineers and architects will be better able to enable analytics on sensitive data across multiple cloud compute solutions from a single, centralized platform.
Unlocking More Cross-Cloud Data Capabilities
Our new features and native integrations expand data teams’ ability to easily manage cloud data ecosystems while simplifying data governance, access control, privacy management, and cross-platform auditing.
Native integration for Starburst
We recently announced a strategic alliance with Starburst to give data architects and engineers automated sensitive data discovery, flexible access control policies, privacy-enhancing technologies (PETs), and a self-service, active data catalog. These features are available in Immuta’s latest release, enabling data teams to put sensitive data to use and achieve consistent data access governance using Starburst.
Expanded Databricks access control for R and Scala
Databricks recently named Immuta a 2020 ISV Rising Star Award winner for the accomplishments we have jointly achieved in the first year of our strategic partnership. Our latest release enables Databricks customers to use their programming language of choice — R, Scala, Python, or SQL — to analyze protected data sets. Immuta is the first solution in the industry to provide this breadth of language support, allowing data engineers and architects to operationalize sensitive data more quickly and for more data science teams than ever before.
Regulatory policies for HIPAA Safe Harbor and CCPA
Immuta’s Data Engineering Survey: 2021 Impact Report indicates that more than 90% of organizations adhere to one or more regulatory laws or set of rules. Specifically, 26% and 23% of respondents indicated their data teams must comply with HIPAA and CCPA, respectively. In our latest release, we’ve extended our privacy Starter Policies to all our native cloud data platform integrations, including Databricks, Snowflake, and Starburst. Data teams can use Immuta’s Starter Policies to meet the requirements of HIPAA’s Safe Harbor method and the CCPA, while eliminating many of the manual steps required to support these complex regulatory policies.
Native integration for Trino
Also included in our latest release is native support for Trino, formerly known as Presto SQL, providing fine-grained access control at the column-, row-, and, cell-levels. Data teams can now leverage Immuta as an active data catalog for Trino data sources with the option to create policies using existing metadata and to apply PETs at query time without having to develop custom functions or move data.
Interested in exploring all these great new capabilities in Immuta? Sign up for our 14-Day Free Trial and get started, or request a demo from one of our solution architects. And don’t forget to download your copy of Immuta’s Data Engineering Survey: 2021 Impact Report to learn more about the current and future state of data engineering and DataOps.