DatriseAI-first ETL

Import API to Google BigQuery Integration

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

How Datrise loads Import API into Google BigQuery

Datrise syncs Import API's records, payloads, batches, schemas, ingest timestamps, and source keys 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

Import API: Direct API push of custom records into a pipeline.

Google BigQuery: Serverless analytics warehouse on GCP.

How Import API entities map to Google BigQuery

Import API entityGoogle BigQuery objectNotes
recordsimport_api_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
payloadsimport_api_payloadsid PK · linked to import_api_records
batchesimport_api_batchesid PK · linked to import_api_records
schemasimport_api_schemasid PK · linked to import_api_records

FAQ

How does Datrise handle Import API'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 Import API 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

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