DatriseAI-first ETL

LinkedIn Ads Snowflake

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

How Datrise loads LinkedIn Ads into Snowflake

Datrise syncs LinkedIn Ads's B2B campaign metrics, spend, clicks, leads, and audience performance into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

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

Snowflake: Cloud data warehouse with separated compute and storage.

How LinkedIn Ads entities map to Snowflake

LinkedIn Ads entitySnowflake objectNotes
B2B campaign metricslinkedin_ads_b2b_campaign_metricsid PK · custom fields → VARIANT columns
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 Snowflake?

Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.

How does the LinkedIn Ads to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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