In the world of event streaming platforms, Apache Kafka is probably the most well-known. While Kafka was originally developed as a sort of messaging queue, LinkedIn saw scalability in the platform and both developed it and turned it into an open-source offering, leaving the messaging system roots in the past. Apache Kafka offers a wide variety of use cases and is essential for the data pipelines of developers and operators across the world.
How, then, does the Apache Kafka platform work? What are some key metrics and applications of Kafka? And how can it impact data streams? To learn more about varied Kafka topics here’s what you need to understand.
The Basics of Apache Kafka
What is Apache Kafka, exactly? Outside of a fault-tolerant event streaming platform, how exactly does this Java application work? At its core, Kafka is a high-throughput and low-latency platform that is capable of handling large data streams and events in real-time. Kafka was also developed to be able to harness Hadoop more capably and with a degree of reliability. These can include website activity, responses, and other real-time applications. The Apache Software Foundation initially developed Kafka but now it’s an aggregate of LinkedIn. Kafka can utilize Kafka Connect as one of its connectors for external systems and offers Kafka Streams, a processing library built on the Java API.
The real-time analytics and confluent platform include fault tolerance and delivery guarantees. However, it’s the real-time component that helps Kafka stand out from the competition. While other platforms and microservices can handle streams of messages and offer fault tolerance, they weren’t designed to be durable enough for real-time analytics. Existing batch-based solutions often have large complications when it comes to data movement and replication which is another area of opportunity for Kafka to persist. Where enterprise messaging and event systems weren’t able to handle this given topic, Kafka found solutions to offer to its consumer group.
How Kafka Works
Kafka generally looks and operates like a traditional publish-subscribe model. It incorporates confluent publishers and targets alongside stream processing. Kafka also circumnavigates data loss in many workloads and all messages that Kafka develops can both persist and are durable enough for replication. This high availability offered by Kafka queries makes it a good fit for a real-time stream. One of the key components for the Kafka consumer is the data store or log which is critical for a high volume of data. The log is a time-order data sequence and queue. The data can ultimately be anything which is crucial for big data. It also has a timestamp so the data can be time-ordered.
However, the Kafka broker isn’t your traditional message broker. In fact, the Kafka broker doesn’t come with a lot of the additional bells, whistles, or new features that a group may expect from a messaging system. The analytics of Kafka doesn’t include message IDs and the messaging system is only addressed by offset. This offset is found in the log. Kafka also doesn’t sync up who or what consumes any of the given messages in a queue. However, since it greatly differs from a standard broker, it can offer some values that a broker can’t. It lightens database loads, there are no deletes within the client library or the log, and it can easily utilize an operating system for caches. As such, the speed and scalability offered by the compaction of Kafka make it nearly invaluable for web-scale enterprises and organizations.
From the Kafka Connect functionality to Kafka Clusters, Apache Kafka has impacted event streaming for the foreseeable future. With data replica capability and fault-tolerance, only time will tell how Kafka continues to develop.
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