# danielbeach/data-engineering-practice

Data Engineering Practice Problems

Repository: https://github.com/danielbeach/data-engineering-practice
Canonical: https://ross.abutalabs.com/products/data-engineering-practice
Language: Python
License Family: other
Last push: 2025-01-08T21:38:03+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1658, "days_push": 602, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2845, forks 867 (observed 2026-08-28T04:07:24.907493+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: etl, data-science, developer-tools
- domain: tutorials, education
- platform: python
- tags: practice-problems, pyspark, postgres, pandas, web-scraping, aws-s3, data-modeling, exercises, data-engineering, docker

## Member repositories
- danielbeach/data-engineering-practice (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:24.907493+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:37:04.721916+00:00, confidence not recorded.
  - readme: https://github.com/danielbeach/data-engineering-practice (fetched 2026-08-28T04:07:24.907493+00:00, sha 42e6344ee018)
- Data as of 2026-08-30T08:39:29.467469+00:00.