DatriseAI-first ETL

Smartengage to Google BigQuery Integration

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

How Datrise loads Smartengage into Google BigQuery

Datrise syncs Smartengage's subscribers, sequences, broadcasts, tags, message events, and custom fields 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

Smartengage: Messenger and email marketing automation.

Google BigQuery: Serverless analytics warehouse on GCP.

How Smartengage entities map to Google BigQuery

Smartengage entityGoogle BigQuery objectNotes
subscriberssmartengage_subscribersid PK · custom fields → JSON or nested/repeated (STRUCT) columns
sequencessmartengage_sequencesid PK · linked to smartengage_subscribers
broadcastssmartengage_broadcastsid PK · linked to smartengage_subscribers
tagssmartengage_tagsid PK · linked to smartengage_subscribers

FAQ

How does Datrise handle Smartengage'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 Smartengage 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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