Apache Kafka is an open-source software platform for processing streams of events. Developed for the streaming and archiving of data, Kafka is now supported by a large ecosystem of third-party applications. Developed by the founders of Confluent, Kafka was initially created in 2007 while the three creators were at LinkedIn. Now, each holds his or her own professional profile at LinkedIn. In 2018, Amazon launched a fully managed version of Kafka.
Official Website For Apache Kafka : kafka.apache.org
Apache Kafka Overview
Apache A distributed event store and stream processing platform is called Kafka. It is a Java and Scala-based open-source system created by the Apache Software Foundation. A unified, high-throughput, low-latency platform for handling real-time data feeds is what the project seeks to provide. The Kafka Streams libraries are available for stream processing applications, and Kafka can connect to external systems (for data import/export) via Kafka Connect. Kafka relies on a “message set” abstraction, which naturally groups messages together to lessen the overhead of the network roundtrip, and uses a binary TCP-based protocol that is optimized for efficiency. Because of the larger network packets, larger sequential disk operations, and contiguous memory blocks that result from this, Kafka is able to convert a bursty stream of errant message writes into linear writes.
While Kafka has grown rapidly over the last decade, the platform’s developers and community have been working to make it more user-friendly. Today, Kafka-based streaming data solutions are available through cloud providers, and Apache Kafka is the most popular open source solution. However, with increasing competition from cloud providers and emerging open source projects, Confluent has a distinct advantage. With many large enterprises moving toward multi-cloud environments and hybrid cloud deployments, it is crucial for companies to standardize their real-time streaming data infrastructure stack. In addition, cloud providers don’t generally make their products interoperable.
Apache Kafka Architecture
Key-value messages that originate from arbitrary numbers of processes known as producers are stored in Kafka. The data can be divided up into many “partitions” inside various “themes.” Messages are indexed and saved combined with a date and rigorously arranged inside a partition according to their offsets (the position of a message within a partition). Partition messages can be read by “consumers,” or other processes. Kafka provides the Streams API for stream processing, enabling developers to create Java programs that read data from Kafka and return results to Kafka. Other external stream processing platforms including Apache Apex, Apache Beam, Apache Flink, Apache Spark, Apache Storm, and Apache NiFi can be used in conjunction with Apache Kafka.
The partitions of all topics are distributed throughout the cluster nodes, and Kafka runs on a cluster of one or more servers (referred to as brokers). Partitions are also copied across numerous brokers. This architecture enables Kafka to replace some of the traditional messaging systems like Java Message Service (JMS), Advanced Message Queuing Protocol (AMQP), etc. by allowing it to deliver enormous streams of messages in a fault-tolerant manner. Kafka now enables transactional writes, which use the Streams API to deliver exactly-once stream processing, starting with the 0.11.0.0 release.
Regular and compressed topics are supported by Kafka. Regular topics can have a retention time or a space bound configured. Kafka is permitted to delete old data to free up storage space if there are records that are older than the designated retention time or if the space bound for a partition is surpassed. Although subjects are typically set up with a retention period of seven days, it is also possible to keep data on file permanently. Records for condensed topics don’t expire depending on time or space restrictions. Kafka, on the other hand, never deletes the most recent message associated with a key and instead treats following messages as updates to earlier messages with the same key. By writing a so-called tombstone message with null-value for a particular key, users can completely remove messages.
Despite the aforementioned advantages, Kafka has some significant downsides. Kafka can suffer from high volume of message loss and can result in data loss, which can be a problem for many organizations. In addition, it is susceptible to quality issues and quantity issues. With this in mind, Apache Pulsar can eliminate data loss and keep Kafka up to date. Those drawbacks are reasons to upgrade to a more modern version of Apache Kafka.
Confluent has released Confluent for Kubernetes, a platform that brings cloud-native capabilities to Apache Kafka clusters in private infrastructures. Confluent’s Apache Kafka expertise helps developers and DevOps teams take advantage of this new private cloud Kafka service. So, what can Confluent do for your business? The company has developed Confluent for Kafka as an enterprise-ready platform. Despite its shortcomings, Confluent is a cloud-native platform that helps companies and developers run applications that require large amounts of data.
LinkedIn’s founders, meanwhile, scaled the Kafka application to handle billions of messages. With their software, they eventually open-sourced it to the Apache Software Foundation. Earlier this year, Confluent, which spun out of LinkedIn, launched a fully managed version of Kafka for enterprises. In a round of funding led by LinkedIn and its own investors, the company has since acquired the custom of many tech luminaries. Confluent has even been a major contributor to the Kafka open-source project.
