DatriseAI-first ETL

LinkedIn Ads Amazon Athena

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

How Datrise loads LinkedIn Ads into Amazon Athena

Datrise syncs LinkedIn Ads's B2B campaign metrics, spend, clicks, leads, and audience performance into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

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

Amazon Athena: Serverless SQL over S3 data lake tables.

How LinkedIn Ads entities map to Amazon Athena

LinkedIn Ads entityAmazon Athena objectNotes
B2B campaign metricslinkedin_ads_b2b_campaign_metricsid PK · custom fields → struct/map columns in Parquet
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 Amazon Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

How does the LinkedIn Ads to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

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