Mastering Kafka Concept: High Throughput, Low Latency, Deduplication and Data Integrity

Apache Kafka has emerged as a go-to solution for handling large-scale, high-throughput transaction processing and event-driven architectures. But with challenges such as high traffic, low-latency requirements, and fault tolerance, it is crucial to configure Kafka optimally to ensure seamless operations.

In this article, we will explore how Kafka’s features — such as horizontal scaling, partitioning, replication, and acknowledgment mechanisms — can be leveraged to build a resilient financial transaction processing system. Whether you’re a fintech engineer, a system architect, or a developer working in a transaction-heavy environment, this guide will equip you with the strategies to fine-tune Kafka for your specific use case.

1. Low Latency:

Scenario:

Your fraud detection system must process financial transactions in real-time to identify potential fraud. As transactions are sent to Kafka, the fraud detection service must consume and analyze the messages with minimal delay to flag fraudulent transactions before they are completed.