Title

AWS re:Invent 2023 - Elevate your data and AI governance with Databricks Data Intelligence Platform

Summary

• Databricks solution architects Pamela and Jim presented a session on improving data and AI governance using the Databricks Data Intelligence Platform.
• They highlighted the complexity of data and AI governance due to different types of users, assets, and tools, each with its own governance framework.
• The session introduced Unity Catalog, a unified governance model for all data and AI assets, integrating with external catalogs and compute platforms.
• Unity Catalog offers a single permissioning model, automated data lineage, centralized auditing, cost reporting, and facilitates open data sharing.
• Demonstrations included how Unity Catalog aids data engineers, data scientists, BI analysts, and governance administrators in their respective roles.
• The platform allows for secure interaction with files and tables, ingestion and transformation of data, model registration and deployment, and monitoring of model predictions.
• Unity Catalog's features also include AI-generated comments, Databricks Assistant, Genie Data Rooms, and fine-grained access control with row and column security.
• The platform supports semantic search, data browsing, and tagging for better data management and governance.
• Delta sharing was introduced as a method for sharing lakehouse data with partners and across regions or clouds.
• System tables in Unity Catalog provide a means to monitor costs, audit user access, and understand data and AI asset usage.
• The session concluded with a Q&A session and a reminder of the capabilities of Unity Catalog for data and AI governance.

Insights

• The complexity of data and AI governance is a significant challenge for organizations, often leading to fragmented views of data assets and increased risk of data breaches.
• A unified governance model like Unity Catalog can simplify governance by providing a single layer of permissions and visibility across all data and AI assets.
• The integration of Unity Catalog with external data sources and compute platforms indicates a trend towards interoperability and open governance solutions in the data and AI space.
• The ability to monitor data and model drift is crucial for maintaining the accuracy and reliability of machine learning models, and Unity Catalog provides tools for this purpose.
• The use of AI-generated comments and semantic search can enhance data discoverability and user self-sufficiency, reducing the burden on data stewards and administrators.
• Delta sharing represents a shift towards more efficient and secure methods of data sharing, eliminating the need for traditional data transfer methods like FTP servers.
• The use of system tables for cost monitoring and auditing suggests that organizations are increasingly looking for ways to optimize their data infrastructure spending and ensure compliance with governance policies.
• The session's focus on practical demonstrations of Unity Catalog's features underscores the importance of hands-on experience and real-world applications in understanding and adopting new technologies in data governance.