DatriseAI-first ETL

Partnerstack to Looker Integration

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

How Datrise loads Partnerstack into Looker

Datrise syncs Partnerstack's partners, customers, transactions, rewards, links, and group memberships 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

Partnerstack: Partner, affiliate, and reseller program management.

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

How Partnerstack entities map to Looker

Partnerstack entityLooker objectNotes
partnerspartnerstack_partnersid PK · custom fields → flattened columns (nested fields expanded for modeling)
customerspartnerstack_customersid PK · linked to partnerstack_partners
transactionspartnerstack_transactionsid PK · linked to partnerstack_partners
rewardspartnerstack_rewardsid PK · linked to partnerstack_partners

FAQ

How does Datrise handle Partnerstack'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 Partnerstack 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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