Immuta and Databricks partner to offer cloud-native, automated data governance for Apache Spark and Delta Lake

Immuta announced a partnership with Databricks that empowers data teams to perform analytics on sensitive data in the cloud in a compliant, policy-driven, and automated way. Through a new, native integration, the two companies help customers make data secure, anonymized and compliant for cloud analytics and machine learning.

With Databricks and Immuta, customers can enforce fine-grained access controls and dynamically apply anonymization techniques from directly within Databricks and Delta Lake, the open source storage layer that brings reliability to data lakes. As a result, Databricks users see only what they’re allowed to see, based on who they are, and their approved purpose. This enables organizations to securely access data and build machine learning models.

With Immuta natively integrated within Databricks, the customer now has the power to create automatically-enforced data policies, combining ABAC (or attribute-based access control) and purpose. 

Now, the customer’s analysts enjoy dynamic access to data, with Immuta ensuring proper security and privacy protection – down to the row, column, and cell-level. Immuta’s integrated data governance made possible the migration to Databricks. The customer now benefits from analytics on a modern platform, without sacrificing governance and security.

SnapLogic extends alliance with Databricks to boost data lake reliability; analyze big data workloads in the cloud

SnapLogic has expanded its partnership with Databricks with new support for Delta Lake, the open source storage layer created by Databricks that brings reliability to traditional data lakes. 

Together, the joint solution helps customers accelerate the integration, transformation, and processing of big data workloads into Delta Lake, increasing data quality and accelerating the time to value of advanced analytics and machine learning initiatives.

Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of existing data lake and is fully compatible with Apache Spark APIs. Delta Lake on Databricks allows users to configure Delta Lake based on their workload patterns and provides optimized layouts and indexes for fast interactive queries.

Delta Lake sits on top of Apache Spark. The format and the compute layer helps to simplify building big data pipelines and increase the overall efficiency of the pipelines.

Organizations are increasingly investing in data lakes to gain actionable insights into their growing data assets. However, the high volume and complexity of data often results in data quality, reliability and performance issues. 

Together, SnapLogic and Databricks are removing these roadblocks by providing a low-code, visual paradigm for data engineers to create and process data pipelines that leverage the full power of Delta Lake — including features like ACID transactions, scalable metadata handling, schema enforcement, and batch and streaming support.

“Databricks and SnapLogic are committed to delivering product innovations that help organizations reduce the time, effort, and skills needed to manage their big data initiatives so they can quickly turn their data into meaningful insights that drive the business forward,” said Craig Stewart, chief technology officer, SnapLogic. “By teaming up with Databricks, we aim to remove the key technical barriers to data lake and big data management so our customers can accelerate their analytics and machine learning initiatives and focus on delivering real business value.”