DatriseAI-first ETL

Real Geeks Google BigQuery

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

How Datrise loads Real Geeks into Google BigQuery

Datrise syncs Real Geeks'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

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Real Geeks entities map to Google BigQuery

Real Geeks entityGoogle BigQuery objectNotes
contactsreal_geeks_contactsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
accountsreal_geeks_accountsid PK · linked to real_geeks_contacts
dealsreal_geeks_dealsid PK · linked to real_geeks_contacts
activitiesreal_geeks_activitiesTIMESTAMP events

FAQ

How does Datrise handle Real Geeks'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 Real Geeks 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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