Managing Data — Databricks Certified Data Analyst Associate

The official DATABRICKS documentation our Managing Data practice questions are cited to. Review the primary sources, then practise.

Official references for this objective

Databricks — A Simple, Managed Platform - Modern Feature Engineering on Databricks | Databricks

Define what a feature is (source, entity, aggregation, time window) and let the platform compute it correctly in every context
Databricks — Building Intelligent Data Discovery with Unity Catalog: Domains and Discover | Databricks

how Domains provide a business-aligned governance layer, how Discover Page curates trusted data and insights across the account
Databricks — Cost Governance at Scale @ Databricks | Databricks

we use System Tables, budgets and tagging policies to attribute costs to hundreds of internal teams, identify waste and enforce accountability
Databricks — Data Interoperability with Unity Catalog | Databricks

Unity Catalog as a unified governance layer, using both Delta and Iceberg open table formats
Databricks — Data Interoperability with Unity Catalog | Databricks

tables can be accessed by both Delta and Iceberg clients, enabling cross platform analytics and eliminating data silos
Databricks — Introducing Databricks Managed Disaster Recovery | Databricks

work together under one unified API to deliver 15-minute RPO and RTO targets
Databricks — Sponsored by: MinIO | On-Prem to Databricks: Zero-Copy Analytics with Native Delta Sharing for Iceberg and Delta Lake | Databricks

AIStor Table Sharing is MinIO's native Delta Sharing protocol implementation embedded in the AIStor binary, delivering streamlined administration, consistency and full governance