DatriseAI-first ETL

SugarCRM Google BigQuery

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

How Datrise loads SugarCRM into Google BigQuery

Datrise syncs SugarCRM's enterprise account, opportunity, and customer-service intelligence data 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

SugarCRM: Enterprise CRM platform for sales, service, and account intelligence.

Google BigQuery: Serverless analytics warehouse on GCP.

How SugarCRM entities map to Google BigQuery

SugarCRM entityGoogle BigQuery objectNotes
enterprise accountsugarcrm_enterprise_accountid PK · custom fields → JSON or nested/repeated (STRUCT) columns
opportunitysugarcrm_opportunityid PK · linked to sugarcrm_enterprise_account
customer-service intelligence datasugarcrm_customer_service_intelligence_dataid PK · linked to sugarcrm_enterprise_account

FAQ

How does Datrise handle SugarCRM'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 SugarCRM 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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