# gtoonstra/etl-with-airflow

ETL best practices with airflow, with examples

Repository: https://github.com/gtoonstra/etl-with-airflow
Canonical: https://ross.abutalabs.com/products/etl-with-airflow
Language: Shell
License Family: other
Last push: 2024-09-25T14:42:06+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": 3608, "days_push": 707, "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 1356, forks 260 (observed 2026-08-28T04:04:29.338737+00:00)

## What it is
A community-maintained collection of ETL best practices, usage patterns, and examples for Apache Airflow, published as documentation with accompanying source code. It is not affiliated with the official Apache Airflow project.

## Use cases
- learn how to build ETL pipelines with airflow
- find airflow DAG examples and patterns
- understand ETL best practices for data engineering
- get guidance on structuring airflow projects
- study real-world airflow workflow examples

## When to choose
- you are learning Apache Airflow and want practical ETL examples
- you need guidance on ETL design patterns and principles
- you want reference material beyond official Airflow docs

## When to avoid
- you need official, up-to-date Airflow documentation
- you need a production ETL tool or library rather than educational material
- you require a maintained, licensed dependency in your project

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: etl, workflow-automation, documentation, scheduling
- domain: tutorials, developer-tools
- platform: python, cross-platform
- tags: apache-airflow, etl-patterns, best-practices, examples, data-pipelines, data-engineering, automation

## Member repositories
- gtoonstra/etl-with-airflow (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:29.338737+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 2026-08-30T04:41:54.596256+00:00, confidence not recorded.
  - readme: https://github.com/gtoonstra/etl-with-airflow (fetched 2026-08-28T04:04:29.338737+00:00, sha b9153be10d54)
- Data as of 2026-08-30T08:39:29.467469+00:00.