DatriseAI-first ETL

Responsys to Google BigQuery Integration

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

How Datrise loads Responsys into Google BigQuery

Datrise syncs Responsys's contacts, campaigns, launches, message events, lists, and profile extensions 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

Responsys: Enterprise cross-channel campaign orchestration.

Google BigQuery: Serverless analytics warehouse on GCP.

How Responsys entities map to Google BigQuery

Responsys entityGoogle BigQuery objectNotes
contactsresponsys_contactsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
campaignsresponsys_campaignsid PK · linked to responsys_contacts
launchesresponsys_launchesid PK · linked to responsys_contacts
message eventsresponsys_message_eventsTIMESTAMP events

FAQ

How does Datrise handle Responsys'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 Responsys 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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