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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Docker
Score 8.9 out of 10
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Docker Enterprise was sold to Mirantis in 2019; that product is now sold as Mirantis Kubernetes Engine. But Docker now offers a 2-product suite that includes Docker Desktop, which they present as a fast way to containerize applications on a desktop; and, Docker Hub, a service for finding and sharing container images with a team and the Docker community, a repository of container images with an array of…
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.
You are going to be able to find the most resources and examples using Docker whenever you are working with a container orchestration software like Kubernetes. There will always some entropy when you run in a container, a containerized application will never be as purely performant as an app running directly on the OS. However, in most scenarios this loss will be negligible to the time saved in deployment, monitoring, etc.
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).
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
I have been using Docker for more than 3 years and it really simplifies the modern application development and deployment. I like the ability of Docker to improve efficiency, portability and scalability for developers and operations teams. Another reason for giving this rating is because Docker integrates CI/CD pipelines very well
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.
The reason why we are still using Docker right now is due to that is the best among its peers and suits our needs the best. However, the trend we foresee for the future might indicate Amazon lambda could potentially fit our needs to code enviornmentless in the near future.
It is the only tool in our toolset that has not [had] any issues so far. That is really a mark of reliability, and it's a testimony to how well the product is made, and a tool that does its job well is a tool well worth having. It is the base tool that I would say any organisation must have if they do scalable deployment.
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.
