For example, it's quite possible to use the Java client to create producers and consumers that send and retrieve data from a number of topics published by a Kafka installation. Decouple data pipelinesįlexibility is built into the Java client. Kafka gives you all the data you want all the time. Working with a traditional database just doesn't provide this type of ongoing, real-time data access. Having access to enormous amounts of data in real time adds a new dimension to data processing. Kafka is designed to emit hundreds of thousands-if not millions-of messages a second. When developers use the Java client to consume messages from a Kafka broker, they're getting real data in real time. The ease of use that the Kafka client provides is the essential value proposition, but there's more, as the following sections describe. Developers do not have to write a lot of low-level code to create useful applications that interact with Kafka. Essentially, the Java client makes programming against a Kafka client a lot easier. One of the more popular is the Java client. What are the benefits of using a Java client?Īs mentioned above, there are a number of language-specific clients available for writing programs that interact with a Kafka broker. After that, we'll move on to an examination of Kafka's underlying architecture before eventually diving in to the hands-on experimentation. We'll start with a brief look at the benefits that using the Java client provides. In subsequent articles, I'll cover some more advanced topics, such as how to write Kafka producers and consumers in a specific programming language. I'll also demonstrate how to produce and consume messages using the Kafka Command Line Interface (CLI) tool. In addition, I'll provide instructions about how to get Kafka up and running on a local machine. In this piece, I'll cover these essentials. Yet, no matter what, at the most essential level a developer needs to understand how Kafka works in terms of accepting, storing, and emitting messages. The service provider takes care of the rest. All that developers need to concern themselves with when using a service provider is producing messages into and consuming messages out of Kafka. Using a Kafka service provider abstracts away the work and maintenance that goes with supporting large-scale Kafka implementations. For enterprise installations, many companies will use a scalable platform such as Red Hat OpenShift or a service provider. Of course, there's a lot more work that goes into implementing Kafka clusters at the enterprise level. One of the nice things about Kafka from a developer's point of view is that getting it up and running and then doing hands-on experimentation is a fairly easy undertaking. But you can write application code that interacts with Kafka in a number of other programming languages, such as Go, Python, or C#. Kafka's native API was written in Java as well. Using Java and other programming languages with KafkaĪpache Kafka itself is written in Java and Scala, and, as you'll see later in this article, it runs on JVMs. It's distributed, which means it's highly scalable adding new nodes to a Kafka cluster is all it takes. It can feed events to complex event streaming systems or IFTTT and IoT systems or be used in accordance with in-memory microservices for added durability. Kafka is used to collect big data, conduct real-time analysis, and process real-time streams of data-and it has the power to do all three at the same time. You can think of Kafka as a giant logging mechanism on steroids. Kafka is fast, it's big, and it's highly reliable. It's all the rage these days, and with good reason: It's used to accept, record, and publish messages at a very large scale, in excess of a million messages per second. Apache Kafka is a distributed, open source messaging technology.
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