DatriseAI-first ETL

3PL Central to Azure Data Lake Storage Integration

AI-first ETL from 3PL Central into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.

How Datrise loads 3PL Central into Azure Data Lake Storage

Datrise syncs 3PL Central's orders, receipts, inventory levels, items, customers, and warehouses into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.

Ideal for Azure lakehouse storage shared across Synapse and Databricks.

Endpoints

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

Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.

How 3PL Central entities map to Azure Data Lake Storage

3PL Central entityAzure Data Lake Storage objectNotes
orderstplcentral_ordersid PK · custom fields → nested struct/map fields 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 Azure Data Lake Storage?

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

How does the 3PL Central to Azure Data Lake Storage sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.

Related pipelines

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