In the realm of data processing and distributed systems, two popular approaches for handling data flow are messaging queues and streaming. Although they can sometimes serve similar purposes, they cater to different use cases and have different designs. In this blog, we'll delve into their individual characteristics, exploring the differences between the two.


  • Messaging Queues: Messaging queues are a form of middleware that handle messages (data packets) between applications. They ensure that messages sent from a producer service are properly received by a consumer service, even if the consumer is not ready to process them immediately.
  • Streaming: Streaming is the continuous transfer of data where data can be processed as it comes in. Streaming platforms allow for real-time data processing, allowing immediate insights and actions based on the incoming data.

Purpose & Use Cases

Messaging Queues:

  • Used to decouple producing and consuming applications.
  • Suited for scenarios where guaranteed message delivery is vital.
  • Often leveraged for load leveling and balancing between producers and consumers.


  • Designed for real-time data processing and analytics.
  • Perfect for scenarios where time-sensitive actions are necessary, like real-time fraud detection or live dashboarding.
  • Often used for processing large amounts of fast-moving data.

Representative Systems

Example of Messaging Queues

Scenario: E-commerce Order Processing

Imagine you run an e-commerce website. When a user places an order, several steps are involved:

  1. Order validation.
  2. Payment processing.
  3. Inventory check.
  4. Shipment initiation.

Given the discrete nature of these tasks, you can use a messaging queue like NATS. Here's how:

  1. When an order is placed, it's sent as a message into the queue.
  2. The payment service picks up the order message, processes the payment, and sends a confirmation message back into the queue.
  3. The inventory service listens to the queue, picks up the payment confirmation message, checks the inventory, and then sends a shipment initiation message.
  4. The shipping service then picks up the shipment initiation message and starts the shipping process.


  • Each service operates independently and at its own pace. If the inventory service goes down temporarily, messages (like order confirmations) will still be in the queue, waiting to be processed when the service is back online.
  • Ensures no orders are missed even during high traffic.

Example of Streaming

Scenario: Real-time Analytics Dashboard for Social Media Mentions

Suppose you're monitoring mentions of a brand on social media to gauge its popularity and respond to any PR crises in real-time.

  1. Every tweet, post, or comment mentioning the brand is captured by an ingestion service.
  2. This data is streamed into Kafka topics in real-time.
  3. Multiple services consume this stream simultaneously:
    • A sentiment analysis service processes the mentions to determine if they're positive, negative, or neutral.
    • A real-time dashboard service updates live visuals with the volume and nature of mentions.
    • An alerting service monitors for sudden spikes in negative sentiments and sends alerts to PR teams.


  • The ability to process vast amounts of data in real-time.
  • Multiple consumers can read and analyze data concurrently, allowing for diverse applications from a single data source.

Data Persistence

  • Messaging Queues: Messages remain in the queue until they are consumed or until they expire based on some policy.
  • Streaming: Data in streaming platforms is often persisted for a specified period, allowing consumers to replay the data if required.

Data Ordering & Delivery

  • Messaging Queues:
    • Typically ensure that messages are delivered in the order they are received.
    • Messages are often consumed once and only once by a single consumer.
  • Streaming:
    • Maintains data order across multiple partitions.
    • Allows multiple consumers to read the same data simultaneously, facilitating a publish-subscribe model.

Volume & Throughput

  • Messaging Queues: Well-suited for scenarios with fluctuating data rates, accommodating spikes in data inflow without overloading consumers.
  • Streaming: Built for high-throughput and can handle massive volumes of data flowing into the system continuously.


  • Messaging Queues: Typically scaled by increasing the number of consumer instances or by partitioning messages.
  • Streaming: Designed for horizontal scalability, allowing addition of more nodes to handle larger data loads and more consumers.

Complexity & Features

  • Messaging Queues: Generally straightforward with the primary focus on ensuring message delivery without data loss.
  • Streaming: Often come with a wider range of features, like data windowing, event-time processing, and complex event processing, making them more intricate.

Related Technologies

While the focus is on messaging queues and streaming platforms, it's essential to acknowledge related technologies that augment these systems:

  • Stream Processing Systems: Apache Flink and RisingWave are examples of systems designed to process and analyze data streams in real-time. They often partner with streaming platforms like Kafka to provide a holistic data streaming solution. Specifically, RisingWave can consume data from both messaging queues and streaming platforms, offering users a simple way to manage, process and analyze data.


While messaging queues and streaming might seem to have overlapping functions, they cater to different aspects of data handling and processing. Messaging queues are ideal for ensuring data is reliably transmitted between decoupled systems, while streaming platforms shine in scenarios where real-time processing of vast data volumes is essential. The right choice depends on the specifics of your application and its data processing requirements.

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