DatriseAI-first ETL

Top Producer to Google BigQuery Integration

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

How Datrise loads Top Producer into Google BigQuery

Datrise syncs Top Producer's contacts, leads, listings, transactions, follow-up plans, and market snapshots 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

Top Producer: Real estate contact management and transaction follow-up.

Google BigQuery: Serverless analytics warehouse on GCP.

How Top Producer entities map to Google BigQuery

Top Producer entityGoogle BigQuery objectNotes
contactstop_producer_contactsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
leadstop_producer_leadsid PK · linked to top_producer_contacts
listingstop_producer_listingsid PK · linked to top_producer_contacts
transactionstop_producer_transactionsid PK · linked to top_producer_contacts

FAQ

How does Datrise handle Top Producer'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 Top Producer 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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