DatriseAI-first ETL

Amplitude Amazon Athena

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

How Datrise loads Amplitude into Amazon Athena

Datrise syncs Amplitude's product events, user properties, funnels, cohorts, and retention curves 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

Amplitude: Product analytics source for events, funnels, and cohorts.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Amplitude entities map to Amazon Athena

Amplitude entityAmazon Athena objectNotes
product eventsamplitude_product_eventstimestamp events
user propertiesamplitude_user_propertiesid PK · linked to amplitude_product_events
funnelsamplitude_funnelsid PK · linked to amplitude_product_events
cohortsamplitude_cohortsid PK · linked to amplitude_product_events

FAQ

How does Datrise handle Amplitude'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 Amplitude 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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