Real-Time Payment Monitoring with Event Hubs and Databricks

Process payment events in real time using Event Hubs and Databricks Structured Streaming, then create curated tables and live KPI outputs for monitoring.
Azure • Streaming • Advanced • Payments

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What this project solves

Capturing events is only the first step in a streaming architecture.

Operations, risk, and support teams need to monitor payment activity as it happens. They may need to track success and failure rates, processing delays, retry activity, high-risk transactions, and data quality issues before they affect customers.

In this pipeline, you will build a real-time payment monitoring layer on Azure. You will process events from Azure Event Hubs using Databricks Structured Streaming, archive raw events, separate invalid records, create curated streaming tables, generate live KPI tables, and make the results available for analysis using Synapse Serverless SQL.

What you’ll build

  • Capture payment events as they happen
  • Store raw events for future analysis and troubleshooting
  • Organize streaming data into trusted business tables
  • Separate bad records from valid events
  • Maintain the latest view of transactions
  • Track transaction success and failure trends
  • Monitor processing delays and retry activity
  • Identify high-risk transactions as they occur
  • Monitor the quality of incoming event data
  • Explore real-time metrics using Synapse

How you’ll build it

  • Publish payment events into Azure Event Hubs
  • Use Databricks Structured Streaming to consume event streams
  • Validate events and separate bad records from good records
  • Write raw, curated, and KPI outputs as Delta tables on ADLS Gen2
  • Query and validate monitoring outputs using Synapse Serverless SQL

Tools you’ll use

Azure Event Hubs • Azure Databricks Structured Streaming • Delta Lake • ADLS Gen2 • Synapse Serverless SQL • Python

What you’ll walk away with

After completing this pipeline, you will be able to:

  1. Explain how real-time monitoring pipelines work on Azure
  2. Process Event Hubs streams using Databricks Structured Streaming
  3. Handle invalid events without stopping the pipeline
  4. Build curated Delta tables and live KPI tables from streaming data
  5. Query and validate real-time monitoring outputs using Synapse

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