DatriseAI-first ETL

LinkedIn Ads Looker

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

How Datrise loads LinkedIn Ads into Looker

Datrise syncs LinkedIn Ads's B2B campaign metrics, spend, clicks, leads, and audience performance 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

LinkedIn Ads: B2B advertising metrics for pipeline and attribution reporting.

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

How LinkedIn Ads entities map to Looker

LinkedIn Ads entityLooker objectNotes
B2B campaign metricslinkedin_ads_b2b_campaign_metricsid PK · custom fields → flattened columns (nested fields expanded for modeling)
spendlinkedin_ads_spendid PK · linked to linkedin_ads_b2b_campaign_metrics
clickslinkedin_ads_clicksid PK · linked to linkedin_ads_b2b_campaign_metrics
leadslinkedin_ads_leadsid PK · linked to linkedin_ads_b2b_campaign_metrics

FAQ

How does Datrise handle LinkedIn Ads'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 LinkedIn Ads 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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