DatriseAI-first ETL

Efficy CRM to Google BigQuery Integration

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

How Datrise loads Efficy CRM into Google BigQuery

Datrise syncs Efficy CRM's contacts, companies, opportunities, actions, campaigns, and service tickets 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

Efficy CRM: European CRM suite for sales, marketing, and service.

Google BigQuery: Serverless analytics warehouse on GCP.

How Efficy CRM entities map to Google BigQuery

Efficy CRM entityGoogle BigQuery objectNotes
contactsefficy_contactsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
companiesefficy_companiesid PK · linked to efficy_contacts
opportunitiesefficy_opportunitiesid PK · linked to efficy_contacts
actionsefficy_actionsid PK · linked to efficy_contacts

FAQ

How does Datrise handle Efficy 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 Efficy 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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