Plaid → Google BigQuery
AI-first ETL from Plaid into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Plaid into Google BigQuery
Datrise syncs Plaid's records, events, and configuration objects 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
Plaid: SaaS or API data source for analytics and warehouse sync.
Google BigQuery: Serverless analytics warehouse on GCP.
How Plaid entities map to Google BigQuery
| Plaid entity | Google BigQuery object | Notes |
|---|---|---|
| records | plaid_records | id PK · custom fields → JSON or nested/repeated (STRUCT) columns |
| events | plaid_events | TIMESTAMP events |
| configuration objects | plaid_configuration_objects | id PK · linked to plaid_records |
FAQ
How does Datrise handle Plaid'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 Plaid 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.
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Early access
Connect Plaid to Google BigQuery the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.