DatriseAI-first ETL

Airtable to Google BigQuery Integration

AI-first ETL from Airtable into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Airtable into Google BigQuery

Datrise syncs Airtable's bases, tables, records, fields, views, and linked records into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Airtable: Spreadsheet-database hybrid for structured team data.

Google BigQuery: Serverless analytics warehouse on GCP.

How Airtable entities map to Google BigQuery

Airtable entityGoogle BigQuery objectNotes
basesairtable_basesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
tablesairtable_tablesid PK · linked to airtable_bases
recordsairtable_recordsid PK · linked to airtable_bases
fieldsairtable_fieldsid PK · linked to airtable_bases

FAQ

How does Datrise handle Airtable's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Airtable to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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