What is the difference between message queues and data streams?

Message queues and data streams are both tools for moving data between systems, but they serve distinct purposes. Message queues are designed for discrete, asynchronous communication between services, where each message is processed once by a single consumer. Data streams, in contrast, handle continuous, real-time flows of data that can be consumed by multiple subscribers simultaneously. The key differences lie in their processing models, data retention, and consumption patterns.

Message queues, like RabbitMQ or Amazon SQS, operate in a point-to-point model. When a producer sends a message to a queue, it is stored until a consumer retrieves and processes it. Once processed, the message is typically removed from the queue. This ensures tasks like order processing or job scheduling are handled exactly once, even if multiple workers are listening. For example, an e-commerce system might use a queue to manage payment transactions: each payment request is processed by one service, avoiding duplicates. Queues prioritize reliability and decoupling but are not optimized for high-volume, real-time data broadcasting.

Data streams, such as Apache Kafka or Amazon Kinesis, use a publish-subscribe model. Producers write records to a stream, which persists data for a configurable period. Multiple consumers can read the stream independently, replay past data, or process events in real time. This is ideal for scenarios like monitoring user activity across microservices or feeding real-time dashboards. For instance, a ride-sharing app might stream GPS data to compute driver locations, update pricing, and notify users simultaneously. Streams emphasize durability, scalability, and parallel processing, making them suited for use cases requiring historical analysis or event sourcing.

Choose message queues when tasks require guaranteed, one-time processing and loose coupling between services. Opt for data streams when handling high-throughput, time-sensitive data that needs to be shared across multiple systems or reprocessed. For example, queues work well for batch job distribution, while streams are better for live analytics or event-driven architectures. Understanding these distinctions helps in selecting the right tool for scalability, latency, and data access needs.