DatriseAI-first ETL

MoEngage Google BigQuery

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

How Datrise loads MoEngage into Google BigQuery

Datrise syncs MoEngage's engagement events, campaign performance, and retention behavior signals into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

MoEngage: Customer engagement source for campaigns and retention metrics.

Google BigQuery: Serverless analytics warehouse on GCP.

How MoEngage entities map to Google BigQuery

MoEngage entityGoogle BigQuery 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 Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the MoEngage to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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