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Announcing the Databricks analytics engineer learning pathway
Maroua Lazzarou, Pratyarth Rao
- Source
- Databricks
- Published
- Added to Yomu
Summary
Databricks is launching the Analytics Engineer Learning Pathway, a curriculum for SQL practitioners that covers data modeling, pipelines, metrics, and Genie spaces on the lakehouse. The pathway teaches learners to transform raw data into governed, AI-ready semantic models and metric views, the foundation for analytics, dashboards, and AI agents. Courses cover Analytics Fundamentals, production SQL ETL with Materialized Views, Streaming Tables, AUTO CDC, and Lakeflow Jobs, plus data modeling with Delta Lake and Unity Catalog. The curriculum also addresses metric views, Genie spaces, governance with Unity Catalog permissions and ABAC policies, and Spark Declarative Pipelines with expectations, event logs, and metrics. Courses are available in self-paced and instructor-led formats through Databricks Academy, with the full pathway included in active learning subscriptions.
Context
SQL practitioners are increasingly expected to contribute to modeling, pipelines, metrics, and the governed data layers used by dashboards and AI agents. The pathway responds to limited data-engineering capacity and the need for practitioners closer to business questions and metrics to build reliable data foundations.
Approach / What changed
The pathway uses hands-on courses covering the SQL ETL toolkit on Databricks, including Analytics Fundamentals, data modeling with Delta Lake and Unity Catalog, SQL pipelines with Materialized Views, Streaming Tables, AUTO CDC, and Lakeflow Jobs, metric views, Genie spaces, and Spark Declarative Pipelines.
Takeaways
- The curriculum teaches data modeling strategies aligned with business requirements, including data architectures using Delta Lake and Unity Catalog, data product lifecycles, integration, and sharing.
- The SQL ETL coursework covers declarative pipelines with Streaming Tables, Materialized Views, and AUTO CDC, including incremental transformations, SCD Type 1 and Type 2, and Lakeflow Jobs orchestration.
- The Genie course covers Knowledge Store curation, derived expressions, joins, instructions, Unity Catalog permissions, ABAC policies, benchmarks, user feedback, and observed outputs.