chore(pipeline): generate main dag from dbt, using cosmos
#289
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The trick is to leverage plpython to do the geocoding inside the database. Doing so, geocoding can now be modelled as a dbt model and orchestrate as such. The geocoding implementation has been moved to an actual package maintained next to the datawarehouse image. The plpython udf simply wraps the call. Is it worth it ? * it heavely simplifies the flow and set clearer concerns between airflow and dbt. Dbt does the transformation, airflow orchestrate it. * less error prone since we do not have to pull data from the db and map it to python ourselves. * geocoding now can simply be done per source. This would have been terribly cumbersome. * we can even leverage dbt to to the geocoding incrementally on inputs that have not been seen before. This will drastically reduce our carbon footprint... There are a few enhancements we would probably want : * obviously clean up * define a macro with the geocoding model that can be used for all sources * re-do geocodings for relatively old inputs
vmttn
changed the title
cosmoscosmos
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https://github.com/astronomer/astronomer-cosmos
depends on #270