DatriseAI-first ETL

Oracle Database to Google BigQuery Integration

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

How Datrise loads Oracle Database into Google BigQuery

Datrise syncs Oracle Database's tables, rows, schemas, indexes, sequences, and redo log changes 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 Database: Oracle Database for enterprise transactional workloads.

Google BigQuery: Serverless analytics warehouse on GCP.

How Oracle Database entities map to Google BigQuery

Oracle Database entityGoogle BigQuery objectNotes
tablesoracle_db_tablesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
rowsoracle_db_rowsid PK · linked to oracle_db_tables
schemasoracle_db_schemasid PK · linked to oracle_db_tables
indexesoracle_db_indexesid PK · linked to oracle_db_tables

FAQ

How does Datrise handle Oracle Database'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 Database 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

Early access

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