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Error Reprocessing Tool

A Kafka and Azure platform for monitoring, diagnosing, and replaying millions of cloud-migration records with granular transparency.

Case study

Case Study: Error Reprocessing Tool (ERT) Data Pipeline

An enterprise-grade data observability and recovery pipeline built on Kafka and Azure, orchestrating the zero-loss migration of millions of legacy on-premise database records to Azure Cloud daily.

The Problem

High-volume on-premise cloud migrations frequently stall due to data pipeline errors. Without granular, real-time observability down to the domain level, data engineering teams struggle to isolate root causes, resulting in manual debugging overhead and high migration failure risks.

Engineering Features & Direct Impact

  • Horizontally Scalable ERT Sync Engine: Pairs Spring Boot with Azure Event Hubs (Kafka) inside Azure Container Instances to consume error events, partitioning data streams cleanly by domain into structured Azure SQL instances.

  • Bi-Directional ERT Reprocessing API: Exposes RESTful endpoints using Spring Boot to query, filter, and push failed migration events back to their native Kafka topics for immediate, granular or batch recovery.

  • High-Observability ERT Dashboard UI: Visualizes the live migration status of complex domains and subdomains, giving developers a centralized interface to inspect errors and manually replay records.

  • Automated ERT Retries & Scheduler: Executes background polling against the persistence layer to automatically scan for pending errors, initiating automated retry workflows based on predefined, configurable business logic.

  • Containerized Cloud Architecture: Standardizes the deployment footprint across Azure Container Instances, Azure Event Hubs, and Azure SQL, ensuring reliable multi-market pipeline setups and zero-downtime scalability.

The Bottom Line

  • The System: Established an end-to-end telemetry and automated recovery pipeline handling millions of records daily with absolute data transparency.

  • The Business: Drastically reduced cloud migration risks and engineering overhead by replacing blind manual debugging with self-healing data workflows.

Technical Stack

  • Spring Boot

  • Apache Kafka

  • Azure

  • ReactJs

Link

  • Walmart Inhouse product

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