DatriseAI-first ETL

AppsFlyer Amazon Athena

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

How Datrise loads AppsFlyer into Amazon Athena

Datrise syncs AppsFlyer's installs, in-app events, campaigns, and attribution touchpoints 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

AppsFlyer: Mobile attribution and marketing analytics platform.

Amazon Athena: Serverless SQL over S3 data lake tables.

How AppsFlyer entities map to Amazon Athena

AppsFlyer entityAmazon Athena objectNotes
installsappsflyer_installsid PK · custom fields → struct/map columns in Parquet
in-app eventsappsflyer_in_app_eventstimestamp events
campaignsappsflyer_campaignsid PK · linked to appsflyer_installs
attribution touchpointsappsflyer_attribution_touchpointsid PK · linked to appsflyer_installs

FAQ

How does Datrise handle AppsFlyer'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 AppsFlyer 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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