danielbeach/data-engineering-practice resource

Health v2 · maintenance only

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2845 stars · 867 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of hands-on data engineering practice problems covering Python data processing, file formats, SQL, Postgres, PySpark, and data cleansing. Each exercise runs in Docker with instructions provided per exercise.

Use cases

• practice data engineering skills with exercises
• learn to ingest data into Postgres with Python
• practice converting JSON to CSV files
• learn PySpark through hands-on problems
• practice web scraping and downloading files with Pandas
• work with AWS S3 using boto3
• practice designing SQL schemas for CSV datasets

When to choose

• you are learning data engineering fundamentals
• you want hands-on exercises with Docker-based environments
• you are preparing for data engineering interviews
• you want practice with Python, Pandas, PySpark, and Postgres

When to avoid

• you need production-ready data engineering tooling
• you want a course or structured curriculum rather than exercises
• you need a library or framework to include in your project

Facets

learning-resource · maturity active

etl data-science developer-tools tutorials education python practice-problems pyspark postgres pandas web-scraping aws-s3 data-modeling exercises data-engineering docker

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