Managed Apache Pulsar vs Managed Apache Kafka
You’ve probably done some research and narrowed down your options regarding Managed Apache Pulsar vs Managed Apache Kafka. Choosing which messaging and queuing system to use is not an easy decision to make. Each of these systems ships out with unique architecture and features and performs differently under different conditions. How do you choose then?
To make an informed decision, you should have the means to do the Managed Apache Pulsar vs. Managed Apache Kafka comparison. Since this is not a straightforward thing to do, we decided to land you a helping hand. Below you will find everything you need to know about Pulsar vs. Kafka.
What is Kafka
Today, Kafka is a comprehensive event streaming platform capable of handling trillions of events a day. It can process event streams and ships out with permanent storage and pub/sub functionalities. Kafka started as an internal LinkedIn project. In 2011, LinkedIn open-sourced it to enable developers worldwide to contribute to Kafka development.
What is Pulsar
Once Kafka was open-sourced, Yahoo’s team took it and used the code to develop Pulsar. Pulsar played a pivotal role in Yahoo’s HQs. The company used this multi-tenant solution for server-to-server messaging to connect all Yahoo apps. Finally, in 2016 the company open-sourced it. Apache Software Foundation took it in 2018 as one of its top-level projects. Pulsar inherited everything Kafka had to offer, but the developers added some additional perks.
What is Managed Apache Pulsar & Kafka
Both Pulsar and Kafka have dedicated web pages on apache.org. They are both open-source projects and can be downloaded, installed, and configured manually. However, it will require you to invest and set up a proper IT infrastructure and have a team of experienced professionals to do it. Whenever you need to fine-tune the system, run maintenance, or update, the experts on your team would need to do it.
At the exact opposite of this scenario, we have Managed Apache Pulsar and Kafka services. Managed means that somebody else will take care of all the technical Pulsar and Kafka operations. You will be able to reap the benefits of either of these systems while focusing on core business processes. That’s precisely why many organizations choose experts with years of experience to add Pulsar or Kafka to their tech stack.
Key Considerations: Managed Apache Pulsar vs. Managed Apache Kafka
Several companies are offering Managed Apache Pulsar and Kafka services. Managed service is quite attractive to organizations that want to save time and get their distributed messaging and queueing system operational as soon as possible.
However, if we look under the hood of these managed services, there are still some noteworthy differences you should know about. The most important ones include price, scalability, and the system’s capability to handle future workloads.
While both Pulsar and Kafka are open-source projects, which means they are free to use, their managed counterparts are not. Every managed service comes with a unique price tag. We can’t go into a detailed Managed Apache Pulsar vs. Managed Apache Kafka price analysis because most quotes are only available on request.
The good thing about both Managed Apache Pulsar and Kafka services is that most vendors enable you to try them for free. Additionally, the vendors feature cost calculators, which you can use to assess the average cost of using their services. When looking at pricing models, you should pay attention to two models in specific – pay as you go and provision-based pricing.
“Pay as you go” is generally considered a more cost-efficient option as you will end up paying only for the resources you use. With provision-based pricing, you will have the freedom to custom-tailor your packages. However, you will still need to pay for the provisioned resources even though you end up not using them at all.
Being able to use a distributed messaging and queueing system at scale is very important, especially today. Most organizations have to handle multiple data streams and process structured and unstructured data. The ability to go with the upscale or downscale option for your operation on demand while maintaining high throughput and low latency is paramount for success.
Both Kafka and Pulsar are scalable solutions but not to the same extent. The main difference comes from Kafka and Pulsar brokers. In Kafka, brokers are not stateless. It simply means that every broker contains the complete log for its partitions.
What happens if a broker fails? You can replace it with another broker. The same happens if the load spikes up. You can’t seamlessly add a broker before synching its state with other brokers. You also need to pay attention that other brokers contain replicas of the new broker partitions.
On the other hand, we have Pulsar, which scales seamlessly. We can thank stateless brokers for that. Pulsar keeps storing the data separate from brokering of data. To do it, Pulsar uses Apache BookKeeper. The brokers simply take the data from producers and send it to consumers. Meanwhile, the data remains in the BookKeeper. With Managed Apache Pulsar, zero data loss becomes a reality.
If a broker fails or the load spikes up, Pulsar makes things easy for you. All you have to do is add another broker, and it will quickly pick up and start working.
Next, we have storage architecture. Kafka uses distributed commit logs as a storage layer, while Pulsar uses an index-based storage system. Imagine one of the following scenarios:
- The log on your server is full, and you have a demand to scale out;
- The server where the log is stored fails;
Maintaining the same performance of your system while copying a log from one server to another is borderline impossible. Plus, it takes time. Pulsar resolves this issue seamlessly. First of all, Pulsar doesn’t keep the data on one server. It distributes the segments of the log across nodes, thus preventing bottlenecks from occurring. If your server fails or you need to scale out, there is no need to rebalance. All you have to do is add another server.
Which is Better for Future Workloads
Managed Apache Pulsar vs. Managed Apache Kafka – which is better for future workloads? Future workloads are massive. Organizations collect and process data from multiple internal and external sources. They need to build numerous and massive data pipelines, scale-out, scale-up, and maintain excellent performance.
Managed Apache Pulsar is ahead of managed Kafka when it comes to handling future workloads. It has more advanced architecture and enables seamless up- and down-scaling. You can expand Pulsar’s capacity to hundreds of nodes.
It’s more versatile and powerful than Kafka regarding future-proofing an organization against huge demand spikes across data pipelines and at scale. Plus, it enables you to build a secure and stable messaging infrastructure service on Kubernetes.
If this was enough to convince you to try a Managed Apache Pulsar service, you should try out Pandio. Pandio is a cloud-native Managed Apache Pulsar solution that successfully harnesses the power of AI to enable you to build data pipelines seamlessly, access, move, and process your data anywhere and at any time. Feel free to speak with Pandio experts to find the most optimal solution for your specific use case.