DatriseAI-first ETL

NetSuite Amazon Athena

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

How Datrise loads NetSuite into Amazon Athena

Datrise syncs NetSuite's transactions, customers, items, subsidiaries, and GL activity into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

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

Amazon Athena: Serverless SQL over S3 data lake tables.

How NetSuite entities map to Amazon Athena

NetSuite entityAmazon Athena objectNotes
transactionsnetsuite_transactionsid PK · custom fields → struct/map columns in Parquet
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 Amazon Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

How does the NetSuite to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

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