Apache Kafka and Amazon SQS compete in the messaging and data streaming category. Apache Kafka seems to have the upper hand with its scalability and open-source flexibility, while SQS stands out with its serverless model and integration with the AWS ecosystem.
Features: Apache Kafka offers features such as replication, which ensures data safety in case of node failure, partitioning that enables parallel processing, and easy integration with distributed processing tools like Apache Spark. Its scalability and ability to retain messages make it valuable for high-throughput and real-time applications. Amazon SQS provides message durability, high availability, and smooth integration with AWS infrastructure. Dead-letter queues add robustness for error handling, and its serverless design ensures users don’t need to manage infrastructure.
Room for Improvement: Apache Kafka could benefit from enhanced GUI tools for easier management and reduced complexity in infrastructure maintenance. Improving monitoring tools and minimizing its reliance on Zookeeper would enhance user experience. Amazon SQS could improve in areas such as expanding message size limits, introducing real-time streaming capabilities, and offering more flexible pricing for larger data volumes. Enhancing message ordering in standard queues and UI for queue management would also be beneficial.
Ease of Deployment and Customer Service: Apache Kafka typically requires on-premises deployment and relies on community and open-source support, demanding substantial expertise for customization. In contrast, Amazon SQS is primarily deployed on the public cloud with easier integration into the AWS ecosystem, supported by Amazon's robust customer service infrastructure.
Pricing and ROI: Apache Kafka's open-source nature offers cost savings on software but incurs costs related to infrastructure and expertise for deployment. Organizations can see substantial ROI when leveraging its complex capabilities with in-house skills. Amazon SQS follows a pay-as-you-go pricing model, which can be costly at a large scale but provides a predictable cost structure and effortless scaling, making it favorable for AWS-integrated solutions.
Using Amazon SQS has led to increased productivity and reduced man-hour costs.
They meet their tasks effectively.
I want to receive good technical support, which I only need once a month or every six months, and the experience has been unsatisfactory.
There is plenty of community support available online.
The Apache community provides support for the open-source version.
I can easily scale up or down with Amazon SQS without any issues.
Amazon SQS is highly scalable, automatically managing itself based on the load.
Customers have not faced issues with user growth or data streaming needs.
I need to enable my solution with high availability and scalability.
With Amazon SQS, such maintenance is not needed, making it more reliable and secure.
The stability of Amazon SQS is very good, as I find it to be very stable.
This feature of Apache Kafka has helped enhance our system stability when handling high volume data.
Apache Kafka is stable.
Apache Kafka is a mature product and can handle a massive amount of data in real time for data consumption.
It would be beneficial if there was a provision to configure and retain messages for longer than a week.
The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds.
We are always trying to find the best configs, which is a challenge.
The long-term data storage feature in Apache Kafka depends on the setting, but I believe the maximum duration is seven days.
On a scale of one to ten, where one is very cheap, I would rate the pricing as one.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
Its pricing is reasonable.
If there's a failure in the system after consuming a message, SQS's settings ensure the message is not deleted until confirmation.
If we compare with other solutions such as RabbitMQ for messaging, Amazon SQS is easier to use and easier to create the queue.
Apache Kafka is particularly valuable for managing high levels of transactions.
Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing.
It allows the use of data in motion, allowing data to propagate from one source to another while it is in motion.
| Product | Market Share (%) |
|---|---|
| Amazon SQS | 7.8% |
| IBM MQ | 22.5% |
| ActiveMQ | 22.4% |
| Other | 47.3% |
| Product | Market Share (%) |
|---|---|
| Apache Kafka | 3.8% |
| Apache Flink | 12.3% |
| Databricks | 10.0% |
| Other | 73.9% |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 4 |
| Large Enterprise | 14 |
| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 18 |
| Large Enterprise | 49 |
Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS eliminates the complexity and overhead associated with managing and operating message oriented middleware, and empowers developers to focus on differentiating work. Using SQS, you can send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be available. Get started with SQS in minutes using the AWS console, Command Line Interface or SDK of your choice, and three simple commands.
SQS offers two types of message queues. Standard queues offer maximum throughput, best-effort ordering, and at-least-once delivery. SQS FIFO queues are designed to guarantee that messages are processed exactly once, in the exact order that they are sent.
Apache Kafka is an open-source distributed streaming platform that serves as a central hub for handling real-time data streams. It allows efficient publishing, subscribing, and processing of data from various sources like applications, servers, and sensors.
Kafka's core benefits include high scalability for big data pipelines, fault tolerance ensuring continuous operation despite node failures, low latency for real-time applications, and decoupling of data producers from consumers.
Key features include topics for organizing data streams, producers for publishing data, consumers for subscribing to data, brokers for managing clusters, and connectors for easy integration with various data sources.
Large organizations use Kafka for real-time analytics, log aggregation, fraud detection, IoT data processing, and facilitating communication between microservices.
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