DatriseAI-first ETL

Propertybase Google BigQuery

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

How Datrise loads Propertybase into Google BigQuery

Datrise syncs Propertybase'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

Propertybase: Real estate CRM for leads, listings, and agent follow-up.

Google BigQuery: Serverless analytics warehouse on GCP.

How Propertybase entities map to Google BigQuery

Propertybase entityGoogle BigQuery objectNotes
contactspropertybase_contactsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
accountspropertybase_accountsid PK · linked to propertybase_contacts
dealspropertybase_dealsid PK · linked to propertybase_contacts
activitiespropertybase_activitiesTIMESTAMP events

FAQ

How does Datrise handle Propertybase'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 Propertybase 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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