Open Source  ·  v0.7.4  ·  Python 3.12+

The Data Engineering
Framework

Answer three questions. Get a complete, working pipeline — Docker services, dependencies, UIs, and a unified CLI to run it all.

$ pipx install degen-cli
Everything wired. Zero boilerplate.

Everything you'd spend half a day configuring by hand — done in 60 seconds.

Interactive Scaffold

Three questions. DEGEN generates a complete project tailored to your stack.

🐋

Services Out of the Box

Kafka, Postgres, Airflow, Grafana — all in Docker, pre-wired. One degen docker-up.

🖥️

UI for Every Tool

Kafka UI, pgAdmin, Prefect, dbt Docs, Jupyter — all registered and ready, no manual config.

🚀

Unified CLI

degen run, degen stream, degen docs — one consistent interface across all stacks.

From zero to running in 5 commands
Terminal
$ degen init
  Project name: sales_pipeline
  Pattern: Analytics
  Tools: Airflow · dbt · Postgres · Grafana
  ✓ Generated sales_pipeline/

$ degen install
  ✓ Dependencies installed

$ degen docker-up
  ✓ Postgres · pgAdmin · Grafana · Airflow

$ degen run
  ✓ Pipeline executed

$ degen ui
  Airflow UI   →  http://localhost:8080
  pgAdmin     →  http://localhost:5050
  Grafana     →  http://localhost:3001
Every tool. A UI. Always.

No tool without a visual interface. Registered automatically, no manual setup.

Kafka
Kafka UI
:8082
Postgres
pgAdmin 4
:5050
Airflow
Airflow UI
:8080
Prefect
Prefect UI
:4200
dbt
dbt Docs
:8081
DuckDB
Jupyter Lab
:8888
PySpark
Spark UI
:4040
Grafana
Grafana
:3001
Metabase
Metabase
:3000
ClickHouse
Play UI
:8123/play

Learn by doing

The Tutorial walks you through a real pipeline — Airflow + PySpark + dbt + Postgres — writing every file from scratch, running every command, and seeing live results in the UI.

Start the Tutorial →