SQS vs Kafka: A Comparison and Guide

Amazon SQS vs Apache Kafka

In the world of messaging systems, "SQS vs Kafka" has become a hot topic among developers. With Amazon SQS processing billions of messages per day and Apache Kafka being used by thousands of organizations worldwide, it's essential to understand their key differences and choose the right solution for your application. Here's what you should know about these platforms.

4 key takeaways on SQS vs Kafka from this article:

  • Amazon SQS is a fully managed, scalable queuing service, suitable for simple and small-scale applications.
  • Apache Kafka is a high-throughput, distributed messaging platform that excels in large-scale, real-time data streaming.
  • Key differences include ease of use, performance, scalability, data persistence, and cost.
  • Choose Amazon SQS for simpler applications and Kafka for high-performance, large-scale, real-time data processing.

What is Amazon SQS?

Amazon Simple Queue Service, more commonly known as Amazon SQS, is a fully managed message queue (Amazon MQ) that allows you to connect distributed systems and serverless applications with ease, speeding up processes and reducing complexity with the help of a powerful messaging queue.

Using AWS SQS, you can effortlessly send, store, and receive messages across your systems at any volume, without fear of missed messages or service downtime.

With Amazon SQS, you can choose from standard queues and FIFO queues. Both ensure your message reaches a single consumer and offer similar functionality.

Standard queues offer better throughput and best-effort ordering while guaranteeing at least once delivery. FIFO queues guarantee that messages are processed exactly once and in the same order they were put into the queue.

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What is Apache Kafka?

Apache Kafka is a distributed data store that's ideal for processing real-time data, known as streaming data. Streaming data is data generated non-stop by countless data sources (often thousands), with data sources sending data in simultaneously. Streaming platforms need to be able to handle this massive, constant data while processing it incrementally using Kafka topics and Kafka clusters.

Kafka allows users to store streams of records in the same order they were generated, publish-subscribe to record streams, and process streams in real time. As such, you use Kafka primarily when you need to build real-time pipelines and applications that process data streams.

By combining messaging, storing, and stream processing into one application, Kafka gives you better insights and processing capabilities in the form of a fault-tolerant managed service.

Now, let's look at the pros and cons of each for a more informed choice between SQS vs Kafka.

Pros and Cons of Amazon SQS

The pros and cons of Amazon SQS are plentiful. Let's start with the pros:

  • Reduce overhead. If you already use Amazon AWS to manage your operations and the infrastructure that powers them, SQS promises a highly available service that has no upfront cost and no need to acquire or configure third-party software. SQS queues are created dynamically and scale up or down automatically, depending on need.
  • Reliable delivery. You can transmit any amount of data using SQS without the fear of losing messages or seeing processes halt because one service is unavailable. SQS decouples the components of your applications so they can run and fail, independent of one another, improving your system's overall fault tolerance.
  • Secure sensitive data. With Amazon SQS, you can securely exchange sensitive data with the help of server-side encryption. SQS also integrates with the Key Management Service of AWS so you can easily and centrally manage your encryption keys.
  • Elastic and cost-effective. Leverage the AWS cloud to scale on-demand, making your system more elastic and cost-effective. There's no need to plan resources or provision them in advance, as Amazon will handle that automatically.

The cons?

Other than high costs when operating at scale, SQS lacks many basic features, like the ability for a message to trigger a Lambda function.

Reduced control over the MQ's performance and the lack of control over messages themselves can hold you back when using SQS.

Pros and Cons of Apache Kafka

Apache Kafka also has a long list of pros and cons. The pros include the following:

  • Scalable solution. Kafka uses a partitioned log model, allowing large amounts of data to be distributed across multiple servers. This makes your system scalable beyond a single server's capacity.
  • Fast streams. Reduce latency by decoupling your data streams with the help of Kafka for faster processing and more reliable results.
  • Durable partitions. Since partitions are distributed and replicated across multiple servers, Kafka is durable. All data is written to disk, protecting against server outages and failures and improving the fault tolerance of your data.

The cons?

Kafka also comes with many cons, with no complete set of monitoring tools and clumsy behavior.

Issues with message edits, among other limitations, make Kafka a less-than-ideal solution.

Still undecided about SQS vs Kafka? No problem, we got you covered. Read on.

Amazon SQS vs Apache Kafka

Confused about SQS vs Kafka? How Iron Can Help

Are you seeking a messaging system that can scale with your needs? While both Kafka and SQS are scalable, neither is as fast as IronMQ.

The lightweight, lightning fast messaging service offered by Iron.io outperforms competitors like RabbitMQ and ActiveMQ. It brings you an intuitive user interface and a robust feature set that allows you to do more with your data.

When you opt for Iron, you can avoid the downsides and limitations of Kafka and SQS and instead gain complete control of your messaging queue. Use HTTPS/Rest-based APIs for easy cloud integration, or deploy on-premises or in a hybrid environment.

Choose between point-to-point and publish-subscribe patterns and optimize settings to come up with a messaging queue that's best suited for your business and its use cases.

Are you ready to try Iron for yourself and see how IronMQ can improve your system's efficiency? Start a 14-day free trial and put Iron to the test.

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About Korak Bhaduri

Korak Bhaduri, Director of Operations at Iron.io, has been on a continuous journey exploring the nuances of serverless solutions. With varied experiences from startups to research and a foundation in management and engineering, Korak brings a thoughtful and balanced perspective to the Iron.io blog.


  1. blank Jonathan on June 25, 2023 at 9:12 am

    So regarding “SQS lacks many basic features, like the ability for a message to trigger a Lambda function.”
    If you strictly meant that it doesn’t “trigger”, then you are right. But a Lambda will poll an SQS.

    • blank Raul on August 8, 2023 at 8:37 pm

      I was going to write the same. You can easily attach a CloudWatch watcher to SQS, and the watcher can trigger a Lambda/SNS. So that statement is a bit misleading.

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