Set-up Notes

1. Ran Meltano commands

meltano init
meltano add --custom extractor tap-postgres
meltano add --custom loader target-postgres

2. Manually set config vars in meltano.yml for tap and target.
3. Ran meltano install
4. Created .env_template file with default values.
5. Copied .env_template to .env
6. Ran docker-compose up -d warehouse_db from separate docker-compose virtual env.
7. Added robmoore/adventureworks-for-postgres to docker-compose.yml and then ran docker-compose up -d source_db from docker-compose virtual env.
8. Added tables for tap

meltano invoke tap-postgres --discover
meltano select tap-postgres --list --all
meltano select tap-postgres "salesorderdetail" "*"

9. Tested tap is working

meltano invoke tap-postgres --properties .meltano/run/tap-postgres/tap.properties.json

10. Added dbt

meltano add transformer dbt

11. Added airflow

meltano add orchestrator airflow

And then manually edited sql_alchemy_conn value to point to postgres with sql_alchemy_conn: postgresql+psycopg2://$PG_USERNAME:$PG_PASSWORD@$PG_ADDRESS:$PG_PORT/$PG_DATABASE and load_examples: 'False'.

12. Initialize airflow db

meltano invoke airflow initdb

13. Start the airflow ui

meltano invoke airflow webserver -D

14. Start the airflow scheduler, enabling background job processing

meltano invoke airflow scheduler -D

15. Scheduled job

meltano schedule adventureworks tap-postgres target-postgres @hourly --transform run

16. Unpause DAG

Log into Airflow at http://localhost:8080 and click on the 'off' control to the left.

Miscellaneous

See https://www.sqldatadictionary.com/AdventureWorks2014/ for data dictionary. Generated bootstrap model using dbt-helper from the transform directory: dbt-helper bootstrap --profiles-dir ./profile/ --schemas raw

See also Running Airflow on Heroku.