Considerations for Data Access in Lakehouse Architectures

Explore key considerations for implementing data access controls in lakehouse architectures using Databricks SQL Analytics. Learn about the unique challenges of managing access across both data lake and data warehouse components, including fine-grained controls for sensitive data, row-level segmentation for data sharing, and integration with popular BI tools. Discover best practices for setting up robust data governance in common lakehouse scenarios, drawing on insights from Immuta's product team and their experience across different platforms. Gain a comprehensive understanding of role-based and attribute-based access control, enterprise-grade frameworks, and techniques for managing table-level, row-level, and column-level access to ensure secure and efficient data utilization in your lakehouse environment.

Intro
What is a Lakehouse?
Key Features of the Lakehouse
Role-Based Access Control (RBAC)
Attribute-Based Access Control (ABAC)
Access Control Dimensions
Requirements for Enterprise-Grade Access Controls Framework
Framework for Managing Table-level Access Controls
Solving for ABAC in our Framework
Managing Row-level Access Controls
Column-level Masking
sec.fct sales (for user without the executive attribute)