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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IBM Event Automation
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IBM Event Automation enables businesses to accelerate their event-driven efforts. The event streams, event endpoint management and event processing capabilities help lay the foundation of an event-driven architecture for unlocking the value of events.
For brokering messages, Confluent Kafka is well suited since it offers a managed solution ready to use. Scenarios where the solution is not very well suited are for example, where pricing is an issue. The solution costs quite a lot for basic usage (for example: for 3 clusters, pricing is above 100k$ a year).
IBM Event Streams is well suited for companies developing event driven Microservices. One of the biggest challenger with microservices is that your data gets distributed into little silos - event streaming (or better known as event sourcing) allows you to get a central source of truth in your event store. We are taking this approach with IBM Event Streams and it is well suited for building an event streaming / sourcing architecture.
Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers.
It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).
The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.
The Kafka Tool is a community-made Java application that looks and feels from the past century.
Logging can be confusing. This certainly shows when we have to do troubleshooting.
Hybrid scenarios - pub/sub, but there are services in and outside a Kubernetes cluster. Then there are a ~3 options, but only 2 (the harder ones) are production-safe.
Kafka has suited our use case very well so far. Going forward we are planning to expand our platform manifold so the load on Kafka and our reliance on Kafka is going to increase only.
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
The product was very user friendly and extremely easy to get started with. The documentation is excellent and the free tier makes it very easy to get started with without having to make deep or long term financial commitments.
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 met with the support team and they have deep technical and development understanding of the needs and the problems which IBM Event Streams addresses. If you are looking for a product backed by a highly technical support team then IBM Event Streams is probably the best choice. I was specifically impressed by the level of technical understanding my support team demonstrated.
Apache Kafka is built for scale. From high throughput and real-time data streaming, it has a strong advantage over RabbitMQ with its low latency. This put Apache Kafka at the forefront as the platform of choice for large datasets messaging and ensuring scalability when data scale up tremendously. RabbitMQ however has its strengths in traditional messaging. Routing and message delivery reliability are the bedrock of RabbitMQ and this is where RabbitMQ excels. In my previous workplace, RabbitMQ was of choice as reliability matters more than scale. In two words. Apache Kafka for scale, RabbitMQ for reliability. And for cloud deployment and large dataset messaging in what I am doing now, Apache Kafka is the default choice.
We are still in the evaluation process. To enable an event streaming platform for enterprise, IBM Event Streams is a strong candidate due to the ease of use and setup in the cloud. There are many capabilities we are trying to understand to see if IBM Event Streams is the right fit for our needs. I would rate its platform behind Confluent Cloud.
Positive: bursts of traffic on special holidays are easy to handle because Kafka can absorb and buffer all the messages we need to process long enough to let an understaffed set of back-end services catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines running.
Positive: makes decoupling the web and API services from the deeper back-end services easier by providing topics as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
Negative: our engineers have made mistakes such as accidentally dropping a few thousand messages due to the CLI being confusing to use, and as a result a customer lost some of their precious data. I'd say that was more our fault than Kafka's though.
In using downstreams, the minimal features and the rate of releases were slow, makes us feel that there's no upgrades and other than that there's poor marketing of the product.
The adoption around the service is low, requires focused marketing.
Lack of visibility into topic depth , Monitoring capabilities
