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.
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).
It is perfectly suited if some heavy operation needs to be automated where jobs can be queued up. Scalable app which is required to develop in a lesser time frame. Good for when users should be able to change rules more frequently without any downtime, like promotions. The server is well proven in the market. BizTalk server is the best fit if all other integration adapters are developed using Microsoft applications and if all applications are meant to be processed on a Windows environment.
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.
BizTalk uses Microsoft Visual Studio as the IDE (integrated development environment) tool, and it's very easy to use.
The orchestration engine of BizTalk addresses resource issue very well for long-running business processes by dehydrating and rehydrating orchestration instances.
BizTalk is very easy to integrate if the development is mainly on the Microsoft software family.
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.
BizTalk needs to be better at tracking down errors after the fact. Input files by default get deleted after processing successfully, unless you specifically specify that they don't. This can be an issue where you need to see what the input file contained, since you may have errors showing up in your target platform, in our case this was SAP.
BizTalk logging needs improved. It needs to be able to log the content of the messages it sends and receives. It would be good if the log had a link to the input and output files.
BizTalk needs to allow a simple way to preserve the input and output files for debugging purposes. A master setting on the orchestration would be helpful for this.
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
Microsoft BizTalk is not an intuitive product. It requires many hours of looking through the settings to achieve what you need. Using 3rd party DLLs is a nightmare as you are forced to register them in the GAC of the server and this process is quite cumbersome just to use a DLL.
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.
BizTalk Server has been supported for more than 15 years. It is well proven in the market. Microsoft has provided excellent support with technical issues.
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 did look at Jitterbit dataloader from Salesforce.com. Jitterbit seems like a pretty decent solution if you are doing a lot of uploads into Salesforce.com. However, its not nearly as flexible as Microsoft Biztalk is. Biztalk allows you to create any type of custom solution you wish, whereas Jitterbit is much more limited.
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.
