# adilkhash/Data-Engineering-HowTo

A list of useful resources to learn Data Engineering from scratch

Repository: https://github.com/adilkhash/Data-Engineering-HowTo
Canonical: https://ross.abutalabs.com/products/data-engineering-howto
License Family: other
Topics: distributed-systems, data-engineering, data-pipeline, cloud-providers, scala
Last push: 2024-06-19T08:49:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2715, "days_push": 805, "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 4010, forks 569 (observed 2026-08-28T04:08:32.289566+00:00)

## What it is
A curated list of articles, talks, courses, and books for learning data engineering from scratch. It covers topics like data pipelines, SQL, algorithms, programming languages, and tools such as Apache Airflow.

## Use cases
- learn data engineering from scratch
- find resources to become a data engineer
- curated reading list for data engineering
- learn SQL and data pipelines
- study materials for data engineering interviews
- find Apache Airflow tutorials and talks

## When to choose
- you are starting a career in data engineering and want a structured path
- you want a single curated collection of articles, courses, and talks
- you prefer free, community-vetted learning resources

## When to avoid
- you need hands-on code, tools, or a runnable framework rather than links
- you want an up-to-date course with instructor support
- you need production data engineering tooling

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, developer-tools
- domain: tutorials, big-data, awesome-lists
- platform: cross-platform
- tags: curated-list, data-pipelines, career-learning, sql, apache-airflow, data-engineering

## Member repositories
- adilkhash/Data-Engineering-HowTo (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:32.289566+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 2026-08-29T18:24:01.796632+00:00, confidence not recorded.
  - readme: https://github.com/adilkhash/Data-Engineering-HowTo (fetched 2026-08-28T04:08:32.289566+00:00, sha f70620cafdf2)
- Data as of 2026-08-30T08:39:29.467469+00:00.