Apache Kafka and Amazon Kinesis are both technologies that can help organizations manage real-time data streams, but they’re each quite different. For one, Kinesis is an AWS managed service whereas Kafka can be installed anywhere. So why are they often compared? Well, a for a few reasons:
Similar core goals: Both platforms aim to provide high-throughput, low-latency, and fault-tolerant data streaming capabilities. They are designed to handle massive amounts of data in real-time, making them suitable for use cases such as event-driven architectures, real-time analytics, and log aggregation.
Overlapping use cases: Despite their differences, Kafka and Kinesis can be used interchangeably in many scenarios, such as building real-time streaming data pipelines, ingesting logs or metrics, or implementing event-driven applications. As a result, users often compare the two platforms to determine which one suits their specific needs and requirements better.
The rise of the cloud-native Kafka ecosystem: With the availability of managed Kafka solutions like Confluent Cloud, Amazon MSK, and Aiven, it is now easier to compare Kafka and Kinesis on a more level playing field in terms of operational ease. Both managed Kafka services and Amazon Kinesis take care of infrastructure management, scaling, and maintenance, allowing users to focus on building applications.
Thus, if you’re trying to decide between Apache Kafka and Amazon Kinesis, you’re in the right place—I’ll guide you through the most important points of comparison while highlighting the key differences between the two event streaming platforms. But first, let’s define what these two system actually do:
What is Apache Kafka?
Apache Kafka is an open-source distributed streaming platform designed to handle high-velocity, high-volume, and fault-tolerant data streams. It was originally developed by LinkedIn and later donated to the Apache Software Foundation. Kafka has quickly become a popular choice for building real-time data pipelines, event-driven architectures, and microservices applications.
What is Amazon Kinesis?
Amazon Kinesis is a managed, cloud-based service for real-time data streaming and processing provided by Amazon Web Services (AWS). Kinesis enables you to collect, process, and analyze large volumes of data in real-time, enabling quick decision-making and responsive applications. It is designed to handle massive amounts of data with low-latency and high-throughput capabilities.
To summarize, Kafka is a complex, open-source technology that can be deployed anywhere with few limits on horizontal scalability whereas Kinesis is a more user-friendly but proprietary technology that runs exclusively in the AWS ecosystem.
Now let’s compare Kinesis vs Kafka side-by-side on a wider set of key attributes.
Kinesis vs Kafka: Operational Attributes
To make this comparison easier to digest, I’ve tried to generalize about how each system compares based on the important attributes of a stream processing system.
Kinesis vs Kafka: Pricing
Given that Apache Kafka itself is an open-source framework, it can’t be compared directly with Amazon Kinesis in terms of pricing. What we can do instead is compare managed versions of Kafka with Kinesis. For this comparison, I’ll use Confluent Cloud. However, Confluent and Amazon will charge you in slightly different ways.
Let's compare the line items you'll typically see on your bill using each service. Note that all price examples are approximate and might have changed since the time of writing (April 2023). They also do not include new starter incentives such as free credits.
For Confluent, there are other pricing variables such as cluster type and the cloud provider where you’ll be hosting Confluent Cloud (AWS, Azure or GCP) but this comparison covers the core variables.
To generalize, Confluent Cloud’s pricing model is a little more expensive than the Kinesis “on demand” mode if you're a small-scale startup with low horizontal scaling requirements (i.e. partitions and shards). The Kinesis “on-demand” option might seem more expensive per hour, but it takes care of the horizontal scaling for you and you don’t have to worry about whether you’re using 5 or 50 shards. However, Confluent does offer generous free credit bundles for new customers and free partition allowances.
Generally speaking, once your use cases get more advanced or your data volumes and processing requirements increase, Confluent starts to become cheaper than Kinesis (since Kinesis charges extra for features which allow give your more control over horizontal scaling such as shard hours and Enhanced fan-out).
Kinesis vs Kafka: Time to production
While cost is a critical factor, the time it takes to get the system up and running in production is just as important, if not more so.
However, time to production depends on various factors such as your team's familiarity with the technology, the complexity of your application, and your existing infrastructure.
Here is a general comparison of the typical ranges of time for Kinesis vs Kafka:
If you opt for a managed Kafka service like Confluent Cloud, the setup and configuration time can be significantly reduced. In this case, getting up-and-running may also only take a couple of days, as you'll need to configure your application to interact with the managed service.
However, while Confluent Cloud reduces some complexity associated with managing Kafka, there is still a learning curve related to Kafka concepts, APIs, and stream processing libraries. The learning curve for Confluent Cloud may be shorter than self-managed Kafka, but it might still take a few days to a couple of weeks, depending on your team's prior knowledge and experience.
Of course, Confluent is not the only managed Kafka solution. There are other solutions such as Amazon MSK and Aiven Apache Kafka. There are also solutions that use Kafka under the hood, namely our own—Quix. Quix doesn’t fit in the managed Kafka category, because it is focused on stream processing. As such it includes a fully managed Kubernetes environment where you can build and run serverless containers using an online IDE and integrated data exploration tools. Quix connects to any Kafka instance and has data source and sink connectors for Kinesis.
When choosing between Apache Kafka and AWS Kinesis for your event streaming platform and distributed messaging needs, it's essential to forecast your throughput requirements while considering factors such as performance, architecture, features, and the overall ecosystem of each platform.
Kafka is an excellent choice if your organization is sensitive to vendor-lock-in and needs a high-performance, scalable, and feature-rich event streaming platform (provided you have the in-house Kafka expertise).
Kinesis may be more suitable if your organization is already heavily invested in the AWS ecosystem and you prefer the ease of a fully managed service that seamlessly integrates with other AWS services.
Ultimately, the choice between Kinesis vs Kafka will depend on your appetite for complexity versus cost. Kafka can be a lot cheaper but riskier because it has the potential to tie up your technical experts. Kinesis, on the other hand, can make your life a lot easier but you’ll risk bigger infrastructure bills somewhere down the line. And, in the middle are the managed Kafka services which all claim to offload some of Kafka’s complexity for a price. The choice is yours. But if you want the simplicity of Kinesis with the power of Kafka, check out Quix first.
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Mike Rosam is Co-Founder and CEO at Quix, where he works at the intersection of business and technology to pioneer the world's first streaming data development platform. He was previously Head of Innovation at McLaren Applied, where he led the data analytics product line. Mike has a degree in Mechanical Engineering and an MBA from Imperial College London.