DatriseAI-first ETL

MoEngage Amazon Athena

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

How Datrise loads MoEngage into Amazon Athena

Datrise syncs MoEngage's engagement events, campaign performance, and retention behavior signals 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

MoEngage: Customer engagement source for campaigns and retention metrics.

Amazon Athena: Serverless SQL over S3 data lake tables.

How MoEngage entities map to Amazon Athena

MoEngage entityAmazon Athena objectNotes
engagement eventsmoengage_engagement_eventstimestamp events
campaign performancemoengage_campaign_performanceid PK · linked to moengage_engagement_events
retention behavior signalsmoengage_retention_behavior_signalsid PK · linked to moengage_engagement_events

FAQ

How does Datrise handle MoEngage'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 MoEngage 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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