Building ETL Pipelines in Databricks | Data Engineering in Databricks

2026-04-07 · Source: Alex The Analyst · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

This content details the construction of an Extract, Transform, Load (ETL) pipeline within Databricks, focusing on transforming raw data into production-ready formats using the Medallion Architecture (Bronze, Silver, Gold layers). It begins with ingesting raw data (Bronze) from sources like AWS S3, then demonstrates cleaning and standardizing this data (Silver) by addressing issues such as incorrect date formats and duplicate user IDs. The process leverages Databricks' AI Assistant for code generation and refinement, specifically using Python and Pandas. Finally, the cleaned data is further transformed into a "Gold" layer for specific business insights, such as identifying popular ad click days and referral sources. The content differentiates between simple job execution and full ETL pipelines in Databricks, highlighting the latter's advantages like built-in data quality checks, failure recovery, and incremental processing, which rely on Spark declarative pipelines (STP) and materialized views.

Key takeaway

For Data Engineers building robust data workflows in Databricks, prioritize using dedicated ETL pipelines over simple notebook jobs for complex transformations. This approach provides critical features like automatic incremental processing, built-in data quality checks, and failure recovery, which are essential for maintaining data integrity and operational efficiency in production environments. Ensure your transformations define materialized views to fully leverage the declarative pipeline framework.

Key insights

Databricks ETL pipelines transform raw data into production-ready insights using Medallion Architecture and AI-assisted coding.

Principles

• Separate raw, cleaned, and production data layers.
• ETL pipelines offer built-in data quality and recovery.
• Materialized views are key for declarative pipelines.

Method

Ingest raw data (Bronze), clean and standardize it (Silver) using Python/Pandas, then create aggregated insights (Gold). Utilize Databricks' AI Assistant for code generation and define materialized views for pipeline execution.

In practice

• Use Databricks AI Assistant for rapid ETL code generation.
• Implement Bronze, Silver, Gold architecture for data governance.
• Define materialized views for robust ETL pipelines.

Topics

• ETL Pipelines
• Databricks
• Medallion Architecture
• Data Transformation
• AI Assistant

Best for: Data Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Alex The Analyst.