DatriseAI-first ETL

3PL Central to Amazon Athena Integration

AI-first ETL from 3PL Central into Amazon Athena. Governed entities, incremental sync, typed landing tables.

How Datrise loads 3PL Central into Amazon Athena

Datrise syncs 3PL Central's orders, receipts, inventory levels, items, customers, and warehouses 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

3PL Central: Third-party logistics warehouse and order operations.

Amazon Athena: Serverless SQL over S3 data lake tables.

How 3PL Central entities map to Amazon Athena

3PL Central entityAmazon Athena objectNotes
orderstplcentral_ordersid PK · custom fields → struct/map columns in Parquet
receiptstplcentral_receiptsid PK · linked to tplcentral_orders
inventory levelstplcentral_inventory_levelsid PK · linked to tplcentral_orders
itemstplcentral_itemsid PK · linked to tplcentral_orders

FAQ

How does Datrise handle 3PL Central'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 3PL Central 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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