LinkedIn Ads → Sisense
AI-first ETL from LinkedIn Ads into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads LinkedIn Ads into Sisense
Datrise syncs LinkedIn Ads's B2B campaign metrics, spend, clicks, leads, and audience performance into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.
Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.
Ideal for embedded analytics on an in-memory engine.
Endpoints
LinkedIn Ads: B2B advertising metrics for pipeline and attribution reporting.
Sisense: Analytics platform with elastic data models and embedded analytics.
How LinkedIn Ads entities map to Sisense
| LinkedIn Ads entity | Sisense object | Notes |
|---|---|---|
| B2B campaign metrics | linkedin_ads_b2b_campaign_metrics | id PK · custom fields → flattened columns for the cube |
| spend | linkedin_ads_spend | id PK · linked to linkedin_ads_b2b_campaign_metrics |
| clicks | linkedin_ads_clicks | id PK · linked to linkedin_ads_b2b_campaign_metrics |
| leads | linkedin_ads_leads | id PK · linked to linkedin_ads_b2b_campaign_metrics |
FAQ
How does Datrise handle LinkedIn Ads's custom fields in Sisense?
Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.
How does the LinkedIn Ads to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
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