DatriseAI-first ETL

Public API to Looker Integration

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

How Datrise loads Public API into Looker

Datrise syncs Public API's records, response payloads, pagination cursors, request parameters, schemas, and fetch timestamps 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

Public API: Generic REST endpoint pulled on a schedule.

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

How Public API entities map to Looker

Public API entityLooker objectNotes
recordspublic_api_recordsid PK · custom fields → flattened columns (nested fields expanded for modeling)
response payloadspublic_api_response_payloadsid PK · linked to public_api_records
pagination cursorspublic_api_pagination_cursorsid PK · linked to public_api_records
request parameterspublic_api_request_parametersid PK · linked to public_api_records

FAQ

How does Datrise handle Public API'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 Public API to Looker sync stay up to date?

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

Related pipelines

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