Zachary is a PM at Immuta. His team builds a common security layer that governs all data access directly in the warehouse and as a proxy, intercepting all ODBC and JDBC requests and applying a security model on top of them. In a past life, he was a data engineer, working on advertising systems and data science platform for major mobile games at Fox and Disney. In his spare time, Zachary goofs around with his daughter and attempts to get on his bike.
May 27, 2021 11:35 AM PT
Organizations are increasingly exploring lakehouse architectures with Databricks to combine the best of data lakes and data warehouses. Databricks SQL Analytics introduces new innovation on the “house” to deliver data warehousing performance with the flexibility of data lakes. The lakehouse supports a diverse set of use cases and workloads that require distinct considerations for data access. On the lake side, tables with sensitive data require fine-grained access control that are enforced across the raw data and derivative data products via feature engineering or transformations. Whereas on the house side, tables can require fine-grained data access such as row level segmentation for data sharing, and additional transformations using analytics engineering tools. On the consumption side, there are additional considerations for managing access from popular BI tools such as Tableau, Power BI or Looker.
The product team at Immuta, a Databricks partner, will share their experience building data access governance solutions for lakehouse architectures across different data lake and warehouse platforms to show how to set up data access for common scenarios for Databricks teams new to SQL Analytics.