Comments (1)
MattTriano
commented on May 27, 2024
I also updated the version of the dbt image (which is only used for serving dbt docs) to keep it in line with the version running in the airflow scheduler. Maybe if I adopt a CeleryExecutor or K8sExecutor setup, I'll just fold dbt doc-serving into worker or executor image and eliminate the dbt_proj service.
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Related Issues (20)
- Develop tooling to ingest Census geographic data from the TIGER data offerings
- Add pipelines to collect TIGER geospatial features distributed in spanning different geometries
- Extend TIGER taskflow to include validation and ingestion into a persistent data_raw table
- Extend Census API Caller taskflow to include validation and ingestion into a persistent data_raw table
- Upgrade Airflow images to 2.6.2
- Rewrite great_expectations sections in the docs
- Implement success/failure notifiers for Airflow DAGs HOT 1
- Implement functionality to provide more meaningful variable names for census dataset features
- Upgrade airflow images to 2.6.3 along with package versions
- Update Superset Version to 2.1.0
- Add new datasets from this year's CCSO Open Data Refresh
- Upgrade Superset to version 2.1.0
- Remove references to the deprecated and obsoleted py-utils container
- Add instructions for backing up and restoring a database
- Change date format in backup archive names
- Upgrade Airflow to v2.7.2 and Airflow image package versions as appropriate
- Redact Superset credentials from init script
- A failed typecasting prevents CTA bus stop data standardization and cleaning
- Update startup script and recipes to also work on a fresh Ubuntu/Debian instance
- Modify system to work when run by Docker in rootless mode
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