DatriseAI-first ETL

Braintree Google BigQuery

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

How Datrise loads Braintree into Google BigQuery

Datrise syncs Braintree's transactions, customers, disputes, and settlement records 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

Braintree: PayPal-owned payment gateway for card and wallet transactions.

Google BigQuery: Serverless analytics warehouse on GCP.

How Braintree entities map to Google BigQuery

Braintree entityGoogle BigQuery objectNotes
transactionsbraintree_transactionsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
customersbraintree_customersid PK · linked to braintree_transactions
disputesbraintree_disputesid PK · linked to braintree_transactions
settlement recordsbraintree_settlement_recordsid PK · linked to braintree_transactions

FAQ

How does Datrise handle Braintree'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 Braintree 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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