- Reliable analytics and AI depend on well-built data foundations, and SQL practitioners are the ones building the pipelines, models, and metrics that power them. - A new learning pathway for SQL practitioners that covers skills to use the full SQL ETL toolkit on databricks - data modeling, declarative SQL pipelines for lightweight transformations or governed end-to-end workflows, consistent semantic layers, and conversational agents. - Courses are available now on Databricks Academy, in self-paced and instructor-led formats so you can start learning today. Also included with any active Databricks Learning Subscription.

databricks

Databricks has launched an Analytics Engineer Learning Pathway on Databricks Academy, targeting SQL practitioners who want to build governed, AI-ready data foundations. The curriculum includes six hands-on courses covering analytics fundamentals, data modeling strategies, SQL ETL pipelines using Materialized Views and Streaming Tables, semantic models with Unity Catalog Metric Views, conversational agents with Genie, and declarative Spark pipelines. Courses are available in self-paced and instructor-led formats and are included with any active Databricks Learning Subscription.

Announcing the Databricks analytics engineer learning pathway

Why analytics engineering is becoming essential