DatriseAI-first ETL

Zuora to Google BigQuery Integration

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

How Datrise loads Zuora into Google BigQuery

Datrise syncs Zuora's subscriptions, accounts, invoices, rate plans, payments, and amendments 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

Zuora: Subscription billing, revenue, and quoting.

Google BigQuery: Serverless analytics warehouse on GCP.

How Zuora entities map to Google BigQuery

Zuora entityGoogle BigQuery objectNotes
subscriptionszuora_subscriptionsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
accountszuora_accountsid PK · linked to zuora_subscriptions
invoiceszuora_invoicesid PK · linked to zuora_subscriptions
rate planszuora_rate_plansid PK · linked to zuora_subscriptions

FAQ

How does Datrise handle Zuora'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 Zuora 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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