How do I learn to build ETL pipelines the right way?

Summary

• Learn the declarative approach. Free self-paced Databricks Academy courses cover Lakeflow Connect for ingestion, Lakeflow Declarative Pipelines for transformation, and Lakeflow Jobs for orchestration.
• Declare, don't hand-code. With Lakeflow Declarative Pipelines you define transformations in SQL or Python, and the framework handles incremental processing, dependencies, data quality, lineage, and recovery.
• Follow the medallion architecture. Land raw data (bronze), clean and join it (silver), and aggregate curated tables (gold).
• Ingest incrementally with Auto Loader. Auto Loader automatically detects new files in cloud object storage and infers schema for reliable incremental ingestion.
• Productionize with source control. Use Declarative Automation Bundles (DABs) and Git folders for version-controlled, automated deployment.

How do I learn to build ETL pipelines the right way?

Building ETL pipelines the right way means using a declarative, incremental approach with built-in data quality, orchestration, and observability instead of hand-maintaining brittle scripts. On Databricks, the recommended path is to learn the Lakeflow components -- Lakeflow Connect for ingestion, Lakeflow Declarative Pipelines for transformation, and Lakeflow Jobs for orchestration -- and to structure data using the medallion architecture. Free self-paced courses and hands-on tutorials take you from your first pipeline to production.

Why build ETL pipelines on Databricks

• Start with free, self-paced training. Databricks Academy offers courses on data ingestion with Lakeflow Connect, deploying workloads with Lakeflow Jobs, and building pipelines with Lakeflow Declarative Pipelines, all available through the Databricks training catalog.
• Declare transformations instead of hand-coding them. With Lakeflow Declarative Pipelines you declare transformations in SQL or Python, and the framework automatically handles incremental processing, dependency management, data-quality expectations, lineage and monitoring, and checkpoint-based recovery.
• Use the right building blocks. Streaming tables ingest incremental, append-only data, while materialized views keep aggregations correct and up to date.
• Ingest incrementally with Auto Loader. Auto Loader automatically detects new files in cloud object storage and infers schema for reliable incremental ingestion.
• Follow the medallion architecture. Lakeflow Connect pulls source data into Unity Catalog tables, Declarative Pipelines transform raw data through bronze, silver, and gold layers, and Lakeflow Jobs run pipelines and tasks on a schedule.
• Productionize with source control. Put pipelines under version control with Declarative Automation Bundles (DABs) and Git folders for automated, repeatable deployment.

Getting started

• Take the free Lakeflow self-paced courses in the Databricks training catalog.
• Work through Tutorial: Build an ETL pipeline with Lakeflow pipelines.
• Explore more patterns -- change data capture, geospatial, and file processing -- in the Lakeflow pipelines tutorials and Build an ETL pipeline using change data capture.
• Learn incremental ingestion with Auto Loader.
• Read Data pipeline architecture on Databricks and What's new in Lakeflow Declarative Pipelines.

FAQs

What is the recommended way to build ETL pipelines on Databricks?

Use Lakeflow Declarative Pipelines to declare transformations in SQL or Python, ingest incrementally with Auto Loader, structure data with the medallion architecture, and orchestrate with Lakeflow Jobs.

What is the medallion architecture?

It is a layered design where raw data lands in a bronze layer, is cleaned and joined into a silver layer, and is aggregated into curated gold tables for analytics and AI.

How do I move ETL pipelines to production?

Put pipelines under source control with Declarative Automation Bundles (DABs) and Git folders, then schedule and monitor them with Lakeflow Jobs.