DatriseAI-first ETL

Oracle Db Google BigQuery

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

How Datrise loads Oracle Db into Google BigQuery

Datrise syncs Oracle Db'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

Oracle Db: SaaS or API data source for analytics and warehouse sync.

Google BigQuery: Serverless analytics warehouse on GCP.

How Oracle Db entities map to Google BigQuery

Oracle Db entityGoogle BigQuery objectNotes
recordsoracle_db_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventsoracle_db_eventsTIMESTAMP events
configuration objectsoracle_db_configuration_objectsid PK · linked to oracle_db_records

FAQ

How does Datrise handle Oracle Db'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 Oracle Db 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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