# SQL & Data Engineering

Advanced SQL, query optimization, Airflow pipelines, PySpark, dbt, and streaming.

Use this subtrack when you want stronger data-pipeline and analytics-engineering skills behind ML or AI applications. It is especially useful if your weak point is data movement, transformation quality, or reproducible feature pipelines.

## How To Use This Subtrack Well

- Treat SQL and pipeline design as core modeling infrastructure, not as setup work to skip.
- Build one small end-to-end data flow before trying every tool in the stack.
- Pair this subtrack with [../../02-data-science/README.md](../../02-data-science/README.md) and [../../09-mlops/README.md](../../09-mlops/README.md) for stronger end-to-end workflow habits.

## What Comes Next

- Continue to [../README.md](../README.md) for the broader practical data science roadmap.
- Continue to [../../09-mlops/README.md](../../09-mlops/README.md) if you want deployment, orchestration, and monitoring around those pipelines.
- Continue to [../../28-practical-data-science/python-data-science/README.md](../../28-practical-data-science/python-data-science/README.md) if you want a more analysis-heavy complement to this infrastructure track.
