DatriseAI-first ETL

Meta Ads Amazon Athena

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

How Datrise loads Meta Ads into Amazon Athena

Datrise syncs Meta Ads's campaign spend, impressions, clicks, conversions, and audience segments 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

Meta Ads: Facebook and Instagram ad source for spend and engagement.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Meta Ads entities map to Amazon Athena

Meta Ads entityAmazon Athena objectNotes
campaign spendmeta_ads_campaign_spendid PK · custom fields → struct/map columns in Parquet
impressionsmeta_ads_impressionsid PK · linked to meta_ads_campaign_spend
clicksmeta_ads_clicksid PK · linked to meta_ads_campaign_spend
conversionsmeta_ads_conversionsid PK · linked to meta_ads_campaign_spend

FAQ

How does Datrise handle Meta 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 Meta 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

Early access

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