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 and ../../09-mlops/README.md for stronger end-to-end workflow habits.

What Comes Next

• Continue to ../README.md for the broader practical data science roadmap.
• Continue to ../../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 if you want a more analysis-heavy complement to this infrastructure track.