Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. The Kafka event streaming platform is used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
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Mule ESB
Score 9.0 out of 10
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Mule ESB, from Mulesoft, is an open source middleware solution.
Apache Kafka and Mule ESB are both enterprise service bus (ESB) and integration platforms, which allow users to capture real-time data from multiple sources. Apache Kafka is robust and complex for large companies with ESB experience, while Mule ESB’s easy to use interface will come in handy for those newer to integration platforms.
Features
Though both Apache Kafka and Mule ESB are used by enterprise-level companies, there are a few standout features of each that differentiate them.
Apache Kafka is, in essence, a message-brokering platform that delivers data from one point to another. Its uniqueness lies in its ability to distribute, store and process events. Users report that Apache Kafka is easy to configure and handles large amounts of data from different sources with ease. Its flexibility allows for success when handling millions of small files or a small number of large files, and it will accommodate bursts of traffic as well. Kafka is stable, secure, and well supported by Apache, so it can be a mission-critical part of a system without worry that it will fail. The fact that Apache continues to develop and improve it inspires confidence that it won’t go away anytime soon.
Mule ESB is a middleware tool for integrating various applications with each other. It is highly valued by its users for the sheer number of connections that are available. Most connections can be implemented via a drag and drop interface, making it easy to use for beginners. Mulesoft is a SalesForce company, so Salesforce integrations are robust yet simple. Pre-built connectors and templates make Mule ESB even faster to set up.
Limitations
Each of these ESB products has its own set of limitations.
Apache Kafka, while robust, does not support wildcard topic selection, but matches only specific topic names. Handling a large number of topics can make Kafka grind to a halt. Though it has monitoring tools, users report that the selection of these tools is incomplete. Handling of duplicate messages could be improved, as could load balancing and restart. The product documentation can be difficult to follow.
While many appreciate how lightweight Mule ESB is, it can slow down when handling bigger applications or large amounts of messages, and frequent patches imply instability. Troubleshooting can be difficult, and some data types are conspicuously unavailable. Some users also note that the price is a little high for them.
Pricing
As an open-source product, Apache Kafka is free to download and install. Mule ESB is part of the Mulesoft Anypoint Platform, which has a 30-day free trial. Contact Mulesoft to learn more about subscription pricing.
Apache Kafka is well-suited for most data-streaming use cases. Amazon Kinesis and Azure EventHubs, unless you have a specific use case where using those cloud PaAS for your data lakes, once set up well, Apache Kafka will take care of everything else in the background. Azure EventHubs, is good for cross-cloud use cases, and Amazon Kinesis - I have no real-world experience. But I believe it is the same.
If you’re bringing anything into Salesforce you should just invest now into Mule, you will get your money’s worth and find a myriad of uses to build APIs between many other systems. Once you build a component you can easily reuse it as a building block to attach to another source/destination. This makes it easy to ramp up quickly and spread usage of Mule throughout your enterprise. A good value for medium to large companies, but probably cheaper to outsource your job to a consulting firm if you are smaller.
Really easy to configure. I've used other message brokers such as RabbitMQ and compared to them, Kafka's configurations are very easy to understand and tweak.
Very scalable: easily configured to run on multiple nodes allowing for ease of parallelism (assuming your queues/topics don't have to be consumed in the exact same order the messages were delivered)
Not exactly a feature, but I trust Kafka will be around for at least another decade because active development has continued to be strong and there's a lot of financial backing from Confluent and LinkedIn, and probably many other companies who are using it (which, anecdotally, is many).
It is best suited for Rest API development. Mule ESB uses RAML as an API descriptor which is less complex and easy to understand. RAML is an open standard majorly supported by Mulesoft. Once RAML is developed, it is very easy (a few clicks)to create flows corresponding to the resources defined in the RAML. One can also include JSON schema validation in RAML, and with the use of APIkit router, Mule ESB makes the request validation very easy (it's automatic basically.)
Mule ESB comes with a large spectrum of community and enterprise connectors. We have connectors for all the major platforms like Facebook, Twitter, Salesforce, SAP, etc. This enables Mule ESB to integrate with the other systems in a faster and more robust way. Mule ESB has many components to fulfill the requirements of each integration (for example batch processing, parallel processing, choice, etc.)
Mule API gateway is one of the best tools (modules) of Mulesoft's offering. It supports API governance and management very well. One can easily enforce policies on their APIs with API gateway. It enables some of the must-have features in an API solution (i.e. throttling, oAuth, access levels, etc.)
Implementing a CI/CD (DevOps) environment for Mule ESB is a very easy task. Mule majorly uses MAVEN as its build tool, which in turn makes it best suitable for CI/CD approach. Mule also provides MAVEN plugins for auto deployments to the servers. Mule also has a best Unit testing module which is MUnit. MUnit can be used for both Unit and Functional testing, and it is easy to write and generates coverage reports in various formats.
Sometimes it becomes difficult to monitor our Kafka deployments. We've been able to overcome it largely using AWS MSK, a managed service for Apache Kafka, but a separate monitoring dashboard would have been great.
Simplify the process for local deployment of Kafka and provide a user interface to get visibility into the different topics and the messages being processed.
Learning curve around creation of broker and topics could be simplified
Apache Kafka is highly recommended to develop loosely coupled, real-time processing applications. Also, Apache Kafka provides property based configuration. Producer, Consumer and broker contain their own separate property file
Support for Apache Kafka (if willing to pay) is available from Confluent that includes the same time that created Kafka at Linkedin so they know this software in and out. Moreover, Apache Kafka is well known and best practices documents and deployment scenarios are easily available for download. For example, from eBay, Linkedin, Uber, and NYTimes.
I used other messaging/queue solutions that are a lot more basic than Confluent Kafka, as well as another solution that is no longer in the market called Xively, which was bought and "buried" by Google. In comparison, these solutions offer way fewer functionalities and respond to other needs.
Positive: Get a quick and reliable pub/sub model implemented - data across components flows easily.
Positive: it's scalable so we can develop small and scale for real-world scenarios
Negative: it's easy to get into a confusing situation if you are not experienced yet or something strange has happened (rare, but it does). Troubleshooting such situations can take time and effort.
