Redis

Reference Architecture: Redis + Kafka for Fast Reads and Event Propagation 1. Purpose This document describes a reference architecture where: Redis is used to accelerate synchronous read paths Kafka is used to propagate state changes asynchronously The application layer is the sole coordinator of state and events This architecture optimises for: Low latency API responses Decoupled downstream processing Independent scaling of consumers Eventual consistency across systems It does not assume microservices, CQRS, or event sourcing by default. ...

Redis + Kafka: Fast Reads with Event-Driven Change Propagation Overview Redis and Kafka are often presented together, yet they solve orthogonal problems: Redis optimises synchronous access to current state Kafka propagates asynchronous state changes to interested consumers Used together, they enable: Low-latency API responses Decoupled distribution of data changes Independent scaling of read paths and consumers This article explains how they complement each other in practice, without implying any direct coupling or “magic integration”. ...

Understanding Redis: The In-Memory Data Platform for Modern Apps By a developer / solution architect perspective 1. Introduction In today’s world of high-scale web applications, low-latency microservices and real-time analytics, the demands on data stores are steadily increasing. Traditional relational databases often struggle when you need ultra-fast lookups, high throughput and flexible data structures. This is where systems like Redis shine. Redis is an open-source, in-memory key-value store that goes beyond simple caching: it offers rich data structures, persistence options, clustering, replication, and a wide variety of use-cases that make it a very attractive component for building modern architectures. According to various sources: ...