DatriseAI-first ETL

Autopilot to Amazon Athena Integration

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

How Datrise loads Autopilot into Amazon Athena

Datrise syncs Autopilot's contacts, journeys, lists, email events, triggers, and custom fields 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

Autopilot: Visual marketing automation and customer journeys.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Autopilot entities map to Amazon Athena

Autopilot entityAmazon Athena objectNotes
contactsautopilot_contactsid PK · custom fields → struct/map columns in Parquet
journeysautopilot_journeysid PK · linked to autopilot_contacts
listsautopilot_listsid PK · linked to autopilot_contacts
email eventsautopilot_email_eventstimestamp events

FAQ

How does Datrise handle Autopilot'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 Autopilot 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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