DatriseAI-first ETL

SUMA CRM Google BigQuery

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

How Datrise loads SUMA CRM into Google BigQuery

Datrise syncs SUMA CRM's contacts, accounts, deals, activities, and lifecycle events 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

SUMA CRM: European CRM for SMB and mid-market sales teams.

Google BigQuery: Serverless analytics warehouse on GCP.

How SUMA CRM entities map to Google BigQuery

SUMA CRM entityGoogle BigQuery objectNotes
contactssumacrm_contactsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
accountssumacrm_accountsid PK · linked to sumacrm_contacts
dealssumacrm_dealsid PK · linked to sumacrm_contacts
activitiessumacrm_activitiesTIMESTAMP events

FAQ

How does Datrise handle SUMA CRM'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 SUMA CRM 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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