DatriseAI-first ETL

Oracle CX Google BigQuery

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

How Datrise loads Oracle CX into Google BigQuery

Datrise syncs Oracle CX's enterprise CX entities across sales, service, and customer operations 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 CX: Enterprise customer experience suite with sales and service data.

Google BigQuery: Serverless analytics warehouse on GCP.

How Oracle CX entities map to Google BigQuery

Oracle CX entityGoogle BigQuery objectNotes
enterprise CX entities across salesoracle_cx_enterprise_cx_entities_across_salesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
serviceoracle_cx_serviceid PK · linked to oracle_cx_enterprise_cx_entities_across_sales
customer operationsoracle_cx_customer_operationsid PK · linked to oracle_cx_enterprise_cx_entities_across_sales

FAQ

How does Datrise handle Oracle CX'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 CX 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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