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Apache Kafka is a highly versatile event streaming service. Common Apache Kafka use cases include real-time data processing, application activity monitoring, monitoring and logging systems, stream processing, and metrics and logging.^{Footnote 6}  Kafka is used in industries from financial services to healthcare and more. Kafka use cases in banking include bulk transaction queue and batch data processing. Other potential uses include fraud analysis, consumer reporting, A/B testing, message queuing, and more.

Kafka receives information from multiple data sources and organizes it into topics. The topics can be analyzed as needed. A Kafka Producer operates as an interface between different applications and topics, and a Kafka Topic Log database consists of segmented and ordered data. With Kafka, you can feed a data stream into processing pipelines in real-time. Essentially, Kafka operates as the central nervous system of a data analytics framework, and it can be customized for nearly countless applications.^{Footnote 7}

Kafka and RabbitMQ are both messaging systems, but there are some key differences between them. Kafka retains messages for a designated period, such as seven days, but RabbitMQ removes messages once it’s received confirmation from the consumer. RabbitMQ pushes messages to consumers while tracking the load, determining how many messages each consumer is processing. The platform features traditional vertical scaling with the addition of power, but Kakfa scales horizontally with additional nodes.^{Footnote 8}

Apache Pulsar is a newer solution that is designed on Kafka’s API interface. That design means you can upgrade Pulsar and integrate it to different use case scenarios, while Kafka can be more difficult to customize and update. Kafka is a one-system solution, but Pulsar functions as a streaming platform and distributed message system. Both systems can be good choices for businesses that need to handle high amounts of data.^{Footnote 9}

Apache Kafka and Apache Spark function slightly differently. Spark will pull data, hold, process and push data, supporting the ETL process. In contrast, Kafka uses the Kafka Connect API and Kafka Streams API to build data pipelines, and doesn’t support ETL services. Kafka uses a continuous processing model, while Spark features a micro-batch processing model. Kafka doesn’t support any programming language for data transformation, but Spark supports multiple languages and frameworks, meaning it can use existing frameworks.^{Footnote 10}

While Kafka is an open source software, Amazon Kinesis is a managed service offered by Amazon Web Services. Because Kinesis is a managed service, it’s less flexible and versatile than Kafka. Kinesis is accessible and easy to scale, especially for anyone already using Amazon Web Services. Kafka only officially supports Java, while Kinesis supports several languages.^{Footnote 11}