DatriseAI-first ETL

Selligent to Google BigQuery Integration

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

How Datrise loads Selligent into Google BigQuery

Datrise syncs Selligent's contacts, journeys, campaigns, message events, lists, and real-time triggers 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

Selligent: Enterprise cross-channel campaigns with real-time triggers.

Google BigQuery: Serverless analytics warehouse on GCP.

How Selligent entities map to Google BigQuery

Selligent entityGoogle BigQuery objectNotes
contactsselligent_contactsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
journeysselligent_journeysid PK · linked to selligent_contacts
campaignsselligent_campaignsid PK · linked to selligent_contacts
message eventsselligent_message_eventsTIMESTAMP events

FAQ

How does Datrise handle Selligent'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 Selligent 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.

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