Inspector was created because ETL pipelines never get enough testing.

The main features are:

• Checks - run SQL queries within your database or across two databases

  • supports for any SqlAlchemy compatible database

  • comparing results against:

    • another SQL query
    • constant values (dates, numbers, strings)
    • Python and SQL expressions

• Check history - see how your checks behaved in the past
• Multiple environments - run checks against different instances of the same system
• Tagging - tag checks to easily group them and execute multiple checks at once
• Profiling - rapid profiling of database tables using ydata-profiling

You can use Inspector for:

• monitoring correctness of data in production
• automated testing of ETL processes
• confirming that testing has actually been done ;)
• profiling your data

Some key technical details:

• Built with Django
• Uses Celery for asynchronous tasks
• All functionality is exposed via REST API (Django REST Framework)
• Fully dockerized
• SqlAlchemy for database connections
• ydata-profiling for profiling
• React frontend - available separately, non-open source, contact the author for more details

https://data-inspector.readthedocs.io/

Install local dependencies for all services

pip install -r requirements/local.txt

Run a local django server

export DJANGO_READ_DOT_ENV_FILE=True
export DJANGO_ENV_FILE=.envs/.local/.inspector
docker-compose -f docker-compose-develop.yml up -d postgres redis
./manage.py runserver

You may also use

docker-compose -f docker-compose-develop.yml up -d

Access Inspector at http://localhost:8000

Simply

docker-compose up -d

and Inspector will be available at http://localhost:5000

Obviously, you might want to customize parameters, so check out the files in .envs/example and prepare your own docker-compose.yml with HTTPS reverse proxy in front

• Check list

• Check definition

• Check execution history

• Table list

• Profiling history

• Profiling report (Pandas Profiling)

You are more than welcome to submit a PR