Full End-to-End Data Engineering Project in Databricks

2026-04-21 · Source: Alex The Analyst · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, extended

Summary

This content details the construction of a full data engineering project within Databricks, integrating data ingestion, ETL pipelines, and job orchestration for automated data processing. The project utilizes an Amazon S3 bucket to store transaction files, which are then ingested into Databricks. A streaming table is created to continuously pull new data from S3 every 30 minutes. The core of the project involves building a bronze-to-silver-to-gold ETL pipeline using Databricks' Genie code, which automates the generation of Python scripts for data cleaning (e.g., trimming whitespace, standardizing capitalization, removing duplicates) and aggregation into daily transaction summaries. The entire process is automated by a Databricks job that triggers the ETL pipeline whenever the raw transactions table is updated, demonstrating an end-to-end, self-updating data workflow.

Key takeaway

For MLOps Engineers or Data Engineers building automated data platforms, this approach demonstrates how to establish a resilient, self-updating data pipeline. You should leverage Databricks' streaming tables for continuous ingestion and utilize its job orchestration capabilities with table update triggers to ensure your ETL processes run automatically whenever new source data arrives, minimizing manual intervention and ensuring data freshness.

Key insights

Automating data ingestion and ETL pipelines in Databricks creates a robust, self-updating data engineering workflow.

Principles

• Automate data ingestion from source to raw layer.
• Implement multi-stage ETL (bronze, silver, gold).
• Trigger ETL based on source table updates.

Method

Schedule S3 data ingestion into a Databricks streaming table. Use Genie code to generate a bronze-to-silver-to-gold ETL pipeline. Create a Databricks job triggered by the streaming table's updates to run the ETL.

In practice

• Use Databricks' Genie code for rapid ETL pipeline development.
• Configure S3 data ingestion to a streaming table for continuous updates.
• Set up job triggers based on table updates for automation.

Topics

• Databricks
• ETL Pipelines
• Data Ingestion
• Amazon S3
• Job Orchestration

Best for: Data Engineer, MLOps Engineer, AI Student

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