06:00 UTC
Airtable
Selected accounts
Configured job
BigQuery
Destination table
Join marketing, finance, CRM, and product data inside the same warehouse.
Metrics availables
Dimensions availables
Airtable
3 advertising accounts
Connected
Google Ads
2 advertising accounts
Connected
Google Analytics 4
1 web property
Connected
HubSpot
1 CRM portal
Connected
Select your Google Cloud project and BigQuery dataset. Catchr automatically prepares the destination and maps your data into analytics-ready tables.
Catchr continuously imports new and updated data into BigQuery.
Your warehouse stays fresh automatically, ready for SQL, dbt, BI tools, and AI workloads.
Open the dedicated route for each platform. Its field catalog, reporting grain, historical constraints, and warehouse use cases should be specific to that source.
Answers about available Meta Ads data, reporting cadence, history, table settings, attribution changes, and downstream joins.
Add Airtable as a source in Catchr, provide an Airtable personal access token, choose a discovered Base / Table pair and the fields to export, then authorize the target Google Cloud project and dataset.
You can select fields discovered dynamically from your Airtable tables, including scalar values and complex values such as linked records, attachments, and multi-select arrays.
Catchr maps discovered fields using stable Airtable field IDs, helping existing mappings continue to work when an Airtable field's display name changes.
Catchr preserves linked records, attachments, multi-select arrays, and other complex Airtable values as JSON strings instead of flattening or discarding their contents.
Catchr discovers available sources as Base / Table pairs from the Airtable bases and tables accessible with the personal access token you provide.
If your selected schema contains an eligible date field, you can configure that field as the optional date partition for the destination table. Catchr does not configure table clustering or design your downstream analytical models.
Yes. Configure the historical period supported by the source for the initial import, then keep the table updated with recurring exports. The available range is determined by the source API and the settings of your job, not an unlimited-history promise.
Our teams is always here to responds to any question you could have about our data connector.
Connect your accounts, choose the fields then send the configured export to your BigQuery project.
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