DatriseAI-first ETL

MoEngage Amazon S3 Data Lake

AI-first ETL from MoEngage into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads MoEngage into Amazon S3 Data Lake

Datrise syncs MoEngage's engagement events, campaign performance, and retention behavior signals into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.

Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.

Endpoints

MoEngage: Customer engagement source for campaigns and retention metrics.

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How MoEngage entities map to Amazon S3 Data Lake

MoEngage entityAmazon S3 Data Lake objectNotes
engagement eventsmoengage_engagement_eventsISO-8601 timestamp columns 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 S3 Data Lake?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.

How does the MoEngage to Amazon S3 Data Lake sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.

Related pipelines

Early access

Connect MoEngage to Amazon S3 Data Lake the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.