I Built an AI-Powered Data Pipeline Generator for Databricks — Here Is What Happened

[ShamenParis]

Building robust Medallion architectures takes time. Writing the same boilerplate for Auto Loader, streaming tables, and SCD Type 2 merges across different projects is a bottleneck.

So, I ran an experiment: What happens if you let AI write your Spark Declarative Pipelines?

Over the last few weeks, I’ve been deep in the weeds with Databricks Genie Skills and multi-agent orchestration. I decided to build a custom AI-Powered Data Pipeline Generator to see if we could automate the creation of end-to-end DLT pipelines.

In my latest article, I unpack the entire experiment:
🔹 How I structured the GenAI prompts and context
🔹 The architecture behind the AI Skills generator
🔹 Where the AI excelled (and where it needed guardrails)

Whether you're currently optimizing your Delta Live Tables or just curious about applied AI in data engineering, I’d love to hear your thoughts on this approach.

This is what I generated with the SKILLs:

 Read the full deep dive here:
🔗 https://medium.com/@shamen1209/i-built-an-ai-powered-data-pipeline-generator-for-databricks-here-is-...

GitHub: 🔗 https://github.com/ShamenParis/SDP-Advance-Project

#Databricks #DatabricksMVP #SparkDeclarativePipeline #DLT