DatriseAI-first ETL

Creatio Google BigQuery

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

How Datrise loads Creatio into Google BigQuery

Datrise syncs Creatio's no-code CRM processes, entities, and cross-team workflow orchestration 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

Creatio: No-code CRM and process automation platform for enterprise teams.

Google BigQuery: Serverless analytics warehouse on GCP.

How Creatio entities map to Google BigQuery

Creatio entityGoogle BigQuery objectNotes
no-code CRM processescreatio_no_code_crm_processesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
entitiescreatio_entitiesid PK · linked to creatio_no_code_crm_processes
cross-team workflow orchestrationcreatio_cross_team_workflow_orchestrationid PK · linked to creatio_no_code_crm_processes

FAQ

How does Datrise handle Creatio'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 Creatio 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

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