DatriseAI-first ETL

Amazon Rds Google BigQuery

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

How Datrise loads Amazon Rds into Google BigQuery

Datrise syncs Amazon Rds'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

Amazon Rds: SaaS or API data source for analytics and warehouse sync.

Google BigQuery: Serverless analytics warehouse on GCP.

How Amazon Rds entities map to Google BigQuery

Amazon Rds entityGoogle BigQuery objectNotes
recordsamazon_rds_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventsamazon_rds_eventsTIMESTAMP events
configuration objectsamazon_rds_configuration_objectsid PK · linked to amazon_rds_records

FAQ

How does Datrise handle Amazon Rds'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 Amazon Rds 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

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