NextGenLakehouse learning paths sequence the free Databricks video tutorials and written guides into a clear progression — from lakehouse fundamentals to production AI — so you can learn the Databricks Data and AI Platform in the order concepts build on each other.
What order should you learn Databricks in?
Start with data engineering: ingest with Auto Loader and Lakeflow Connect, build declarative pipelines with Lakeflow Declarative Pipelines, and model reliable tables with Delta Lake. Move to governance with Unity Catalog — catalogs and schemas, fine-grained access control, data lineage, and auditing across every workspace.
Next, analytics: query with Databricks SQL, build AI/BI dashboards, and ask questions in natural language with Genie. Then AI and machine learning: track experiments with MLflow, deploy with Databricks Model Serving, build retrieval with Vector Search, and ship agents with Agent Bricks. Finally, operational data with Lakebase, the serverless Postgres for transactional and agent workloads.
Who are these Databricks learning paths for?
The paths are designed for data engineers, analysts, data scientists, and architects who want a structured route through the Databricks Data and AI Platform rather than a random playlist. Every lesson is free and practical: concepts are introduced with a short explanation, then demonstrated end to end so you can reproduce them in your own workspace. If you are brand new, the platform and getting-started answers explain core ideas — workspaces, compute, catalogs, and pricing — before you dive into a track.
How to follow the paths (and prep for certification)
You can follow a path top to bottom to build depth in one area, or mix tracks to match a specific project — for example, pairing the governance track with the analytics track to ship a well-governed Databricks SQL and AI/BI deployment, or combining data engineering with AI and machine learning to move from raw ingestion all the way to a served model or agent. Many lessons map directly to topics on the Databricks certification exams, so the paths double as exam preparation for the Data Engineer, Data Analyst, and Machine Learning Associate and Professional credentials.
Each path links to hands-on videos and articles. For source-backed answers organized by topic, see the Databricks Answers hub; for timestamped video write-ups, see the Learn library; and browse everything on the Tutorials hub.