DatriseAI-first ETL

Oracle Database to Looker Integration

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

How Datrise loads Oracle Database into Looker

Datrise syncs Oracle Database's tables, rows, schemas, indexes, sequences, and redo log changes into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Oracle Database: Oracle Database for enterprise transactional workloads.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Oracle Database entities map to Looker

Oracle Database entityLooker objectNotes
tablesoracle_db_tablesid PK · custom fields → flattened columns (nested fields expanded for modeling)
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 Looker?

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

How does the Oracle Database to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

Early access

Connect Oracle Database to Looker the easy way

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