DatriseAI-first ETL

NetSuite Google BigQuery

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

How Datrise loads NetSuite into Google BigQuery

Datrise syncs NetSuite's transactions, customers, items, subsidiaries, and GL activity 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

NetSuite: Cloud ERP for finance, inventory, and operations.

Google BigQuery: Serverless analytics warehouse on GCP.

How NetSuite entities map to Google BigQuery

NetSuite entityGoogle BigQuery objectNotes
transactionsnetsuite_transactionsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
customersnetsuite_customersid PK · linked to netsuite_transactions
itemsnetsuite_itemsid PK · linked to netsuite_transactions
subsidiariesnetsuite_subsidiariesid PK · linked to netsuite_transactions

FAQ

How does Datrise handle NetSuite'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 NetSuite 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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