Amazon RDS to Google BigQuery Integration
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 tables, rows, schemas, indexes, primary keys, and change streams 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: Managed relational database service on AWS.
Google BigQuery: Serverless analytics warehouse on GCP.
How Amazon RDS entities map to Google BigQuery
| Amazon RDS entity | Google BigQuery object | Notes |
|---|---|---|
| tables | amazon_rds_tables | id PK · custom fields → JSON or nested/repeated (STRUCT) columns |
| rows | amazon_rds_rows | id PK · linked to amazon_rds_tables |
| schemas | amazon_rds_schemas | id PK · linked to amazon_rds_tables |
| indexes | amazon_rds_indexes | id PK · linked to amazon_rds_tables |
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.
Related pipelines
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Early access
Connect Amazon RDS to Google BigQuery the easy way
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