Apache Kafka or Apache Pulsar | Which Is Better and Why?
Tags: Kafka, AI, Machine Learning, Apache Pulsar, Apache

When it comes to operational data monitoring, two names will likely pop into your head — either Apache Kafka or Pulsar. Both of these Apache web server-based event streaming platforms are widely praised for being the best event streaming, message queuing, and distributed software around.

Some people prefer Apache Kafka, others prefer Apache Pulsar, but these aren’t two peas in the same pod. While both are highly regarded event streaming platforms, they’re two drastically different things regarding performance, capabilities, and potential.

Many people aren’t sure which one is best, as both platforms have many users who regard their platform of choice to be the be-all-end-all event streaming software. In this article, we’ll give you a full, comparative, unbiased overview of both, compare them by a preset set of standards, and answer which is the better platform.

What is the Apache Web Server?

Apache is one of the world’s most prominent free and open-source web servers. Roughly about 40% of all websites on today’s internet use it, and it has been a major player for over two decades.

Apache allows you to micromanage virtually every aspect of anything you build on it and works well with mid-grade computer hardware.

Its stability is one of its most prominent features, and the Apache webserver is known as one of the more stable options. That, combined with its decent encryption, makes it a viable HTTP delivery choice.

Security-wise, Apache has minimal vulnerabilities but is known for its impairment in DDOs attacks by tools such as Slowloris, but this has been mostly patched out in the latest release.

One of the most prominent features of the Apache web server is its additional software and its virtually unmatched scalability. It’s used by two of the most popular event streaming platforms, Apache Kafka and Apache Pulsar.

What is Apache Kafka?

Apache Kafka is an open-source event streaming platform that was originally developed by LinkedIn. It’s a system of networks and requests that works through a standard TCP. It allows users to collect, store, and subsequently integrate data on a predetermined scale.

One of the most prominent uses of this queuing technology is building distributed applications and running them on any scale. Apache Kafka powers industry leader LinkedIn, which is always online. This software is renowned for its simplicity, adaptability, and ability to handle large amounts of data in real-time without a hitch.

It also has a unique real-time stream processing capability enabled by the Apache Kafka Streams API, allowing for streaming data architectures and real-time analytics. Both large and small companies use Apache Kafka, and it receives regular updates.

Pros:

Extremely low latency

Superb configuration

Ideal scalability

Fault tolerance

Cons:

Tweaking issues

Lack of monitoring capabilities

Slows down performance

What is Apache Pulsar?

Apache Pulsar is a cloud-based distributed messaging system with event streaming and message queuing capabilities, originally developed by Yahoo. It’s one of the most powerful and popular tools for this purpose on the internet, and it’s praised for its intuitiveness and flexibility.

It provides most of the same features as Apache Kafka but is generally regarded as more powerful and easier to use. It operates on a pub-sub pattern and is one of the most exciting tools that accompany technologies such as big data, which promises to change the digital landscape.

Unlike most of its competition, Apache Pulsar was specifically created to handle things such as geo-replication and multi-tenancy, which weren’t a common feature upon its release, and are still not common today.

Apache Pulsar comes integrated with Apache BookKeeper, which handles most of its storage needs, the remainder of which are handled by Tiered Storage.

It comes with all the same features as Apache Kafka, such as real-time data processing, fantastic applicability and integration, and real-time analytics.

Pros:

Easy to use

Low latency

Full-stack solution

Streamlined performance

Simple deployment

Easier to use API

Simple scale-out

Cons:

Comparing Apache Kafka and Apache Pulsar

Several things differentiate Apache Kafka from Apache Pulsar, and to define the full extent of the differences, we’ll need to set a framework. Below, we’ve listed five parameters on which we will judge both Apache Pulsar and Apache Kafka and compare the two to decipher which one excels in which parameter.

Ease Of Use

One thing that made Apache Kafka stand out among the crowd upon its release was its ease of use. Apache Kafka is the simplest event streaming service that still has some application in the business and data architecture world.

It’s easy to operate, it’s easy to set up — but most importantly, it’s very easy to deploy. That comes courtesy of Apache Kafka’s overall simplicity, which isn’t always a good thing. Its simplicity provides a lack of features that are integral to a service such as this.

On the other hand, Apache Pulsar, while being far more sophisticated than Apache Kafka in almost every way, is not nearly as easy to use as its competitor. Apache Pulsar requires far more powerful hardware for its applications due to its increased capabilities.

These features also make Apache Pulsar more complicated to configure, use, and deploy. That’s not necessarily an issue, as Apache Pulsar is still relatively easy to use for seasoned developers — it’s just a bit harder to get a hold of when compared to Apache Kafka. Apache Kafka also only requires Apache Zookeeper, whereas Pulsar requires both Zookeeper and Bookkeeper.

Features & Benefits

Apache Pulsar comes jam-packed with different features that make it superior to Apache Kafka. It comes with more built-in tools, vastly improved security features, and a far more intensive configuration. It also comes with Apache Bookkeeper, which makes storage as simple and streamlined as possible.

It has some intricate programming behind its data architecture and event streaming aspects, making them as streamlined, consistent, and prompt as possible.

Apache Kafka isn’t without its features and benefits, but they pale compared to those of Apache Pulsar. Even if that is the case, Apache Kafka is far simpler to use and deploy than Apache Pulsar. It comes with all of the features necessary for businesses that don’t have to deal with cosmic amounts of data.

Its security standards are up to par with most of its competition, and its storage capabilities are relatively decent. The scale-out isn’t as easy as with Apache Pulsar, as the brokers aren’t stateless. Apache Kafka also supports third-party tools, which might augment its selection of features, but it doesn’t come with much built-in.

While Pulsar might come with additional tools and features, it’s far more difficult to deploy and operate than Apache Kafka, making it less than ideal for some commercial applications.

Performance

Performance-wise, Apache Pulsar once again takes the cake. Apache Pulsar is renowned for its low latency, high performance, and consistency. It has an advanced infrastructure with simplified management, allowing you to use it however you like. Pulsar also has a built-in geo-aware replication feature, which only adds to its performance.

It comes with MPS ( Multi-Protocol Support), which means that it has seamless integration with other protocols, significantly augmenting its performance and giving it another layer.

All these features make Apache Pulsar the ideal choice for larger-scale operations, which can benefit from the performance that Pulsar has to offer.

Apache Kafka, on the other hand, has relatively decent performance but comes nowhere near Pulsar. For example, it doesn’t support stateless brokers, meaning that the scale-out process is far more tedious. Kafka is better suited to smaller loads and messages — it works well with messages up to 1KB, but larger ones might harm throughput and slow performance down.

Both of these are used for new technologies such as Big Data, and professionals use them in predictive analytics, with Pulsar being vastly better for these purposes.

One thing that might improve Kafka’s relatively meek performance is the addition of third party software and tools, as they can optimize Kafka to work better for different purposes.

In their base models, Pulsar beats Kafka by a significant margin in terms of performance.

Commercial Applicability

When it comes to commercial applicability and who uses Apache Pulsar and Apache Kafka, they’re a very tight match. Kafka is more widespread than Pulsar, mostly due to its relative simplicity, scalability, and decent performance.

Both Pulsar and Kafka are used to select things, one of which is AI. Artificial intelligence and machine learning are two mutually beneficial technologies that require a sophisticated, high-end data delivery system.

While both Kafka and Pulsar are platforms that can be used for such a purpose, Pulsar is again the significantly better option. AI and ML applications are notorious for their complexity and huge amounts of data measured in petabytes — something Kafka can’t handle properly.

While Kafka is less sophisticated than Pulsar, it’s also far more adaptable to different businesses. Website giants such as LinkedIn, Netflix, Uber, Airbnb, Coursera, Activision, and many more use Kafka. They have been using it for a while — and this is because Pulsar is either too complex, too expensive to incorporate, or not needed.

Which One is Better?

While the two are different, there isn’t a way to judge which one is better. Both Apache Kafka and Pulsar are viable contenders used by different companies for different purposes. Depending on your own unique company and your needs, you might need either one.

If you’re daring with huge amounts of data that require intricate deployment, message queuing, event streaming, and high-end capabilities, you’ll want to opt for Pulsar.

However, if you’re running something that doesn’t need that level of sophistication but requires simple and straightforward deployment, you should opt for Kafka.

In Conclusion

Both of these are fantastic platforms which have a vast selection of applications in the world of business, SaaS, and hot new technologies such as big data. Their intended and commercial purposes are message queuing, event streaming, and real-time data procuring, and which one you choose will depend on your unique needs.

If you’re looking for a platform for AI and ML applications, your best bet is probably Pandio. It’s an Apache Pulsar distributed messaging system capable of handling AI and horizontal scaling without huge CAPEX expenditures. Pandio is a cloud-based service that gives it fantastic benefits such as no operational burden, improved latency, and much faster scaling than traditional services. The best part? It comes with a free trial that lets you test the tool out for your specific needs.