Building Real-Time Data Pipelines with Kafka and Spring Boot

15 Mar 2024

Over the past two years at Infosys, I’ve had the opportunity to work on some fascinating real-time data integration challenges for a global enterprise client. One of the most impactful projects was building an event-driven pipeline that processes over 1 million records daily using Apache Kafka and Spring Boot.

The Challenge

Our client needed to stream shipment, trailer, and load events from their Supply Chain Hub systems to external APIs in near real-time. The system had to be:

• Highly available - No data loss even during failures
• Scalable - Handle millions of events per day
• Fault-tolerant - Gracefully handle downstream API failures
• Low latency - Process events within seconds

The Solution

We architected an event-driven microservices system using:

Change Data Capture (CDC)

We implemented CDC to capture changes from source databases and publish them to Kafka topics. This gave us a reliable, ordered stream of events without impacting the source systems.

Kafka as the Backbone

Apache Kafka served as our distributed event streaming platform. We designed topic partitioning strategies to ensure:

• Load balancing across consumers
• Ordered processing within partitions
• Fault tolerance through replication

Spring Boot Microservices

We built stateless microservices that:

• Consumed events from Kafka topics
• Transformed and validated data against business rules
• Enriched events with additional context
• Published to downstream APIs with retry logic and idempotency

Kubernetes Deployment

All services were containerized with Docker and deployed on Azure Kubernetes Service (AKS), with automated CI/CD pipelines in Azure DevOps.

Key Learnings

1. Design for Failure
We implemented Dead Letter Queues (DLQ) for failed events, comprehensive retry mechanisms, and circuit breakers for downstream APIs.

2. Monitoring is Critical
We set up extensive monitoring for Kafka lag, consumer group health, API response times, and data quality metrics. This helped us proactively identify and resolve issues.

3. Data Quality Matters
We built validation frameworks to ensure data consistency and accuracy before publishing to external systems. This saved countless hours of debugging downstream issues.

4. Idempotency is Essential
With distributed systems, you can’t avoid duplicate processing. We designed all our APIs to be idempotent, using unique identifiers to prevent duplicate records.

Impact

The system now reliably processes 1M+ records daily, enabling real-time visibility across the supply chain. It powers multiple downstream consumers and has become a critical piece of infrastructure for the client’s operations.

Building this system taught me invaluable lessons about distributed systems, event-driven architecture, and the importance of operational excellence in production systems.