DatriseAI-first ETL

Bigin by Zoho Google BigQuery

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

How Datrise loads Bigin by Zoho into Google BigQuery

Datrise syncs Bigin by Zoho's SMB leads, contacts, deals, and day-to-day activity tracking 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

Bigin by Zoho: SMB CRM source for leads, deals, and activity tracking.

Google BigQuery: Serverless analytics warehouse on GCP.

How Bigin by Zoho entities map to Google BigQuery

Bigin by Zoho entityGoogle BigQuery objectNotes
SMB leadsbigin_by_zoho_smb_leadsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
contactsbigin_by_zoho_contactsid PK · linked to bigin_by_zoho_smb_leads
dealsbigin_by_zoho_dealsid PK · linked to bigin_by_zoho_smb_leads
day-to-day activity trackingbigin_by_zoho_day_to_day_activity_trackingTIMESTAMP events

FAQ

How does Datrise handle Bigin by Zoho'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 Bigin by Zoho 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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