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Apache Kafka is a versatile platform used in many industries and web applications. You can learn Kafka with online courses delivered through edX.

What is Kafka?

Apache Kafka is an event streaming platform designed to handle and deliver massive amounts of data. Originally developed for LinkedIn, Kafka began as an internal system to handle the social media platform’s daily messages. Today, Apache Kafka is an open-source data streaming solution that can take data from multiple sources and deliver that data to consumers.1

Apache Kafka processes data in real time from all kinds of sources: databases, sensors, mobile devices, and more. These event streams are then stored for retrieval, processing, and routing to various destinations. With event streaming, developers can create applications to process online payments, track shipments, predict patient outcomes, and react to customer interactions in real time.2

The Kafka architecture can be scaled up by adding new nodes or servers. Kafka is written in Java and Scala, and Kafka Streams, a Java stream processing library which makes it a natural and intuitive tool for developers already working with these programming languages. This platform is also compatible with other programming languages, making it a versatile and widely used tool.

Kafka’s core APIs allow developers to customize the way that their software operates. For example, the Admin API can manage and inspect Kafka objects while the Producer API writes a stream of events. The Kafka Connect API allows developers to build and run data import and export connectors that read and write event streams to and from external systems. This allows these external systems to integrate with the Kafka platform as well as apps and systems already in place.3

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Kafka tutorial curriculum

Apache Kafka courses can help you to develop the skills that you need to build data pipelines and Extract, Transform, Load (ETL) processes with the platform. During an Apache Kafka tutorial, you may:

  • Learn how to define data workflows, pipelines, and processes to ensure that you collect the necessary data.
  • Explore how to create and manage ETL, ELT (Extract, Load, Transform), and data pipelines.
  • Understand how to extract and transform data, create an ETL data pipeline, build a batch ETL workflow with Apache Airflow, and create a streaming data pipeline.
  • Implement these concepts with hands-on practice labs.

Jobs that use Apache Kafka

Apache Kafka has over one thousand use cases to date and it can be used to build data pipelines, implement data integration across sources, enable operational metrics, and more. It’s used by companies such as Target, Cisco, and Intuit, and is utilized in the health care, government, transportation, and financial services industries.4

With so many applications for Kafka, data scientists, data developers, and Kafka developers may all use the platform. Qualifications and requirements for a job that uses Kafka will depend on the individual role and the hiring company.

edX offers a variety of educational pathways, including boot camps as well as bachelor's degree programs and even master’s degree programs, for learners interested in advancing their knowledge in a variety of subjects. Find the right course for you.

How to become a Kafka developer online

Aside from a having general understanding of Kafka functionality, a successful Kafka developer should know how to install and maintain this tool, how to set up and configure Kafka brokers, and how to work with many platforms and application stacks.5

If you are interested in adding new skills to your resume, explore how online learning opportunities such as a data analytics boot camp or a bachelor’s degree in computer science can help you build the expertise and experience you need to pursue roles in this field.

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    Frequently Asked Questions

    What is Kafka used for?

    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. 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.

    How does Kafka work?

    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.

    What is the difference between Kafka and RabbitMQ?

    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.

    What is the difference between Apache Pulsar and Kafka?

    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.

    What is the difference between Kafka and Spark?

    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.

    What is the difference between Kafka and Kinesis?

    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.

    1. Everything You Need to Know About Apache Kafka. (2022). Peerbits. Retrieved January 2, 2023.

    2. Kafka 3.4 Documentation. (2023). Apache Kafka. Retrieved February 28, 2023.

    3. Introduction. (2022). Kafka. Retrieved January 2, 2023.

    4. Powered By. (2022). Kafka. Retrieved January 2, 2023.

    5. What Does a Kafka Developer Do? (2022). FreelancerMap. Retrieved January 2, 2023.