Vendor File Ingestion to ADLS Gen2 with ADF

Ingest vendor files into ADLS Gen2 using Azure Data Factory, file validation, metadata tracking, duplicate checks, and Synapse SQL validation.
Azure • Foundations • Beginner • Payments

Architecture Diagram

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What this project solves

Vendor data often arrives as files instead of database records.

Banks, payment processors, settlement partners, fraud providers, and external vendors may deliver CSV or similar files on a daily, weekly, or monthly schedule. Before this data can be used by downstream pipelines, each file should be collected, validated, tracked, archived, and stored in a central data lake.

In this pipeline, you will build a vendor file ingestion process on Azure. You will receive vendor files in ADLS Gen2, validate and convert them using Azure Data Factory, track delivery metadata, prevent duplicate processing, and make the ingested data available for validation using Synapse Serverless SQL.

What you’ll build

  • Receive vendor files into Azure landing folders
  • Validate incoming files against expected patterns
  • Archive original files for audit and traceability
  • Track file deliveries using metadata tables
  • Prevent duplicate file processing
  • Maintain vendor file manifests
  • Store parsed vendor data in ADLS Gen2
  • Query ingested data using Synapse Serverless SQL
  • Validate file counts and ingestion results

How you’ll build it

  • Create ADLS Gen2 landing, raw, and archive folders
  • Configure Azure Data Factory connections and pipeline parameters
  • Validate incoming files before processing
  • Convert valid files and write them into the raw zone
  • Update file metadata, prevent duplicates, and validate results using Synapse SQL

Tools you’ll use

Azure Data Lake Storage Gen2 • Azure Data Factory • Azure SQL Database • Synapse Serverless SQL • Azure Key Vault • SQL

What you’ll walk away with

After completing this pipeline, you will be able to:

  1. Explain how file-based ingestion works on Azure
  2. Ingest vendor files into ADLS Gen2 using Azure Data Factory
  3. Validate files before downstream processing
  4. Track file deliveries and prevent duplicate processing
  5. Query and validate ingested data using Synapse Serverless SQL

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