LinkedIn Ads → Azure Data Lake Storage
AI-first ETL from LinkedIn Ads into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads LinkedIn Ads into Azure Data Lake Storage
Datrise syncs LinkedIn Ads's B2B campaign metrics, spend, clicks, leads, and audience performance into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes new date partitions to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.
Ideal for Azure lakehouse storage shared across Synapse and Databricks.
Endpoints
LinkedIn Ads: B2B advertising metrics for pipeline and attribution reporting.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How LinkedIn Ads entities map to Azure Data Lake Storage
| LinkedIn Ads entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| B2B campaign metrics | linkedin_ads_b2b_campaign_metrics | id PK · custom fields → nested struct/map fields in Parquet |
| 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 Azure Data Lake Storage?
Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Data Lake Storage types.
How does the LinkedIn Ads to Azure Data Lake Storage sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.
Related pipelines
More destinations for LinkedIn Ads
- LinkedIn Ads → Azure Synapse
- LinkedIn Ads → Spreadsheets
- LinkedIn Ads → Airtable
- LinkedIn Ads → CSV Files
- LinkedIn Ads → MongoDB
- LinkedIn Ads → Supabase
- LinkedIn Ads → Neon
- LinkedIn Ads → PlanetScale
- LinkedIn Ads → Amazon DynamoDB
- LinkedIn Ads → Looker
- LinkedIn Ads → Looker Studio
- LinkedIn Ads → Microsoft Power BI
More sources for Azure Data Lake Storage
- Meta Ads → Azure Data Lake Storage
- SAP → Azure Data Lake Storage
- Amplitude → Azure Data Lake Storage
- MoEngage → Azure Data Lake Storage
- Auth0 → Azure Data Lake Storage
- Attio → Azure Data Lake Storage
- Bigin by Zoho → Azure Data Lake Storage
- BambooHR → Azure Data Lake Storage
- Workday → Azure Data Lake Storage
- Pipeliner CRM → Azure Data Lake Storage
- Kommo → Azure Data Lake Storage
- HighLevel → Azure Data Lake Storage
Early access
Connect LinkedIn Ads to Azure Data Lake Storage the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.