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Airtable ETL

This repository contains DAG generators for:

  • Putting data that meets certain criteria into an existing Airtable base and table
  • Putting Airtable data that meets certain criteria into BigQuery

If your use case fits one of these templates, you can simply add a new configuration json (see below) and the appropriate DAG generator will create a pipeline for you. Otherwise, hopefully these pipelines are a useful starting point for further development.

Adding a new BQ -> Airtable pipeline

To create a new BQ to Airtable pipeline, create a json with the following fields:

  • name - Name of your pipeline (matching [A-Za-z0-9_]+)
  • schedule_interval - Cron-style string indicating when your pipeline should run. Specify null if you will trigger your pipeline another way.
  • input_data - Name of query (no file extension) that can be found in gs://<your dag dir>/dags/sql/bq_to_airtable/<name>/. This query should be used to retrieve the data you want to put in Airtable
  • airtable_table - Name of the Airtable table that should receive the data
  • airtable_base - Airtable Base ID containing airtable_table. To find the Base ID, open the table you want to add data to in Airtable, then look at the url. The Base ID appears directly after the domain name, i.e. https://airtable.com/ appvnA46jraScMMth /tblfDL6s8f3LWb0C2/viwBnWFt0W8D5UcuB?blocks=hide
  • [Optional] column_map - A dict mapping BigQuery column names to the column names you want to appear in Airtable. Mapping does not have to cover all columns; unspecified columns will use BigQuery names

Put your JSON in gs://<your dag dir>/dags/bq_to_airtable_config/ and bq_to_airtable.py will generate a DAG from it.

Adding a new Airtable -> BQ pipeline

To create a new Airtable to BQ pipeline, create a json with the following fields:

  • name - Name of your pipeline (matching [A-Za-z0-9_]+)
  • schedule_interval - Cron-style string indicating when your pipeline should run. Specify null if you will trigger your pipeline another way.
  • airtable_table - Name of the Airtable table that you want to retrieve data from
  • airtable_base - Airtable Base ID containing airtable_table. To find the Base ID, open the table you want to add data to in Airtable, then look at the url. The Base ID appears directly after the domain name, i.e. https://airtable.com/ appvnA46jraScMMth /tblfDL6s8f3LWb0C2/viwBnWFt0W8D5UcuB?blocks=hide
  • schema_name - Name of schema (no file extension) that can be found in gs://airflow-data-exchange(development)?/schemas/airtable_to_bq/<name>/
  • [Optional] new_query - Name of query (no file extension) that can be found in gs://<your dag dir>/dags/sql/airtable_to_bq/<name>/. This query will be used to filter only the new rows from the contents of the full Airtable table and save them.
  • merge_query - Name of query (no file extension) that can be found in gs://<your dag dir>/dags/sql/airtable_to_bq/<name>/. This query will be used to generate the updated production table from the contents of the full Airtable table and the current production table.
  • production_dataset - BQ dataset containing production table
  • production_table - Production BQ table we are updating within production_dataset
  • [Optional] column_map - A dict mapping Airtable column names to the column names you want to appear in BigQuery. Mapping does not have to cover all columns; unspecified columns will use Airtable names. To exclude columns from BigQuery, map them to the string "EXCLUDE"

Put your JSON in gs://<your dag dir>/dags/airtable_to_bq_config/ and airtable_to_bq.py will generate a DAG from it.

Adding multiple imports/exports per pipeline

You can now specify both single or multiple imports/exports per pipeline. As you can see in the examples directory, single exports should be specified in a single config file within gs://<your dag dir>/dags/{airtable_to_bq or bq_to_airtable, as appropriate}_config/. Multiple exports can be defined with a directory under gs://<your dag dir>/dags/. The pipeline will be named after the directory name. Shared configuration should go in a config.json, while table-specific configuration should go in individual config files.