Microsoft Fabric Project: Payment Switch Transaction Analytics

This sample Microsoft Fabric project showcases how to ingest, transform, and analyze payment switch transaction data using Fabric’s powerful data engineering and analytics capabilities.

📚 Project Overview

This project simulates a real-world scenario where financial transaction data from a payment switch is processed and prepared for reporting and analysis. It includes ingestion via pipelines, transformations using dataflows, and visualizations through Power BI.

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🔧 Components

🧩 Pipelines (Data Factory in Fabric)

• PL_Ingest_PaymentTransactions
  Ingests raw payment switch transaction files (e.g., CSV or JSON) from OneLake or Blob Storage into a Fabric Lakehouse or SQL endpoint.

• PL_Load_Staging
  Loads and prepares the data for transformation by saving it into a staging area for further analysis.

💧 Data Flow (Dataflow Gen2)

• DF_Clean_Transform_Transactions
  Applies key transformations, including:
  • Mapping and renaming fields
  • Parsing and standardizing timestamps
  • Handling null or malformed data
  • Data enrichment if needed

📊 Power BI Report

• Payment Switch Dashboard
  Visualizes transaction trends, failed transactions, volume per card type, and more.

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🔗 Author

Ali Parva
💼 Data Engineer | Expert in ETL, SSIS, Azure Data Factory, Microsoft Fabric
🌍 LinkedIn | GitHub

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📝 License

This project is for demonstration and learning purposes.