DatriseAI-first ETL

Braintree Amazon Athena

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

How Datrise loads Braintree into Amazon Athena

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

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

Amazon Athena: Serverless SQL over S3 data lake tables.

How Braintree entities map to Amazon Athena

Braintree entityAmazon Athena objectNotes
transactionsbraintree_transactionsid PK · custom fields → struct/map columns in Parquet
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 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 Braintree 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

Early access

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