{"id":5165,"date":"2019-12-21T12:24:42","date_gmt":"2019-12-21T16:24:42","guid":{"rendered":"https:\/\/www.freelancinggig.com\/blog\/?p=5165"},"modified":"2019-12-21T12:24:44","modified_gmt":"2019-12-21T16:24:44","slug":"apache-flink-vs-kafka-whats-the-difference","status":"publish","type":"post","link":"https:\/\/www.freelancinggig.com\/blog\/2019\/12\/21\/apache-flink-vs-kafka-whats-the-difference\/","title":{"rendered":"Apache Flink vs Kafka: What\u2019s the Difference?"},"content":{"rendered":"

The stream of open-source processing space is\nexploding currently, with several systems that are now becoming available\npresenting the users with various alternatives. In Apache Software Foundation,\nthere are more than 10 streams for the processing projects, some are in the\nincubation and few of them graduated to the status of the top-level project.<\/p>\n\n\n\n

In this post, we will mainly focus on discussing comparing the Flink and Kafka Streams with one other on the processing stream, and we also wish to attempt and provide clarity on a question that comes in your mind. The streams of Flink and Kafka were created with some different cases in your mind. While these also have some kind of overlap inapplicability, they usually are designed to solve the orthogonal problems and also have different sweet spots along with placement in a stack of data infrastructure. Before discussing the differences, let us quickly have a small glance about what are Apache Flink and Kafka Streams.<\/p>\n\n\n\n

Apache Flink<\/strong><\/em><\/p>\n\n\n\n

The roots of Apache Flink are in the\nhigh-performance for the cluster computing, and the data processing the set of\nframeworks. The Flink also runs the self-contained computations of streaming\nwhich may also get deployed on the resources given by the resource manager.\nFlink jobs usually consume streams and it also produces data in the streams or\ndatabases. Flink is mainly used with the Kafka as an underlying layer of\nstorage, but it is also is independent.<\/p>\n\n\n\n

Before the coming up of Flink, the users\nrelated to the stream that are processing the frameworks need to make some of\nthe hard choices and even trade-off either latency, all through and the result\naccuracy. Flink was the initial open framework of the source (and it is only\none), which has been also demonstrated for delivering<\/p>\n\n\n\n

(1) All through this in order of millions of\nevents for each second in the moderate clusters.<\/p>\n\n\n\n

(2) The latency of sub-second that may be as\nlow as some 10s of the milliseconds.<\/p>\n\n\n\n

(3) it is also exactly guaranteed that once\nsemantics for the state of the application, and also exactly about end-to-end\ndelivery through supported sources as well as sinks.<\/p>\n\n\n\n

(4) Precise results in the presence of out of\nthe order data arrival with their support for the time of the event.<\/p>\n\n\n\n

Flink is mainly based on the cluster\narchitecture having the master and the worker nodes. Moreover, the link\nclusters are available and they may be deployed standalone with resource\nmanagers like YARN and Mesos. Such kind of architecture allows Flink to use the\nlightweight as the checkpointing as the mechanism to guarantee the precise\nresults if there are any kind of failures, as well it also allows simple and\ncorrect re-processing through the save points devoid of sacrificing latency and\nalso the throughput. Flink has even proven to run much robustly in the\nproduction at quite a huge scale by various companies, powering the\napplications which are used each day by the end customers.<\/p>\n\n\n\n

Kafka<\/strong><\/em><\/p>\n\n\n\n

In contrast to the Flink, Streams API<\/a> is known\nto be much powerful and embeddable stream for the processing engine to build\nthe standard applications of Java for stream processing efficiently. Such kind\nof the Java applications is specifically well-suited, for instance, to build\nthe reactive and wonderful applications, microservices along with the\nevent-driven systems. As the native component of Kafka after version 0.10, it\nis a completely awesome stream for the processing solution which creates the\ntop of the battle-tested foundation related to Kafka to make such stream processing\napplications much scalable, fault-tolerant, elastic, distributed as well as\neasy to build. On the other hand, a gap about Streams API fills are quite less\nto be focused analytics domain and others building the core applications along\nwith the microservices that process the data streams.<\/p>\n\n\n\n

Here, the goal of Streams API is mainly to\nsimplify the processing of stream adequately to make it truly accessible as the\nmainstream model of application programming. To help in the goal, below\nmentioned are some of some deliberate decisions of design that are made in\nStreams API.<\/p>\n\n\n\n

1) It is an embeddable library without any\ncluster, only Kafka and application. With Streams API you will be able to\ncompletely focus on building the applications which drive the business instead\nof building the clusters. It makes approachable to the application developers\nlooking for stream processing, as it will seamlessly integrate along with the\nexisting package of company.<\/p>\n\n\n\n

2) It is completely integrated with the core\nabstractions in Kafka, and hence all strengths of Kafka such as failover,\nfault-tolerance, elasticity, security, and scalability— are available with the\nbuilt-in to Streams API; However, Kafka is also battle-tested and it is even\ndeployed at scale in several companies all around the world, permitting Streams\nAPI to create the great foundation<\/p>\n\n\n\n

 3) It also simply introduces new\nconcepts along with the functionality to permit for stream processing, like\ncompletely integrating abstractions of the streams and tables that you may\nsimply use interchangeably to achieve, for instance, high performing join\noperations with regular queries.<\/p>\n\n\n\n

How are Flink and Streams API Kafka different<\/strong><\/p>\n\n\n\n

The table below lists the most important\ndifferences between the two systems.<\/p>\n\n\n\n

The fundamental and the key differences between the Flink and a Streams Kafka API program mainly lie in a way that they are usually deployed as well as managed (quite often it also has their set of implications about who owns such applications from the perspective of an organization and also how the parallel processing that includes the fault tolerance gets coordinated. They are mainly core differences between the Flink and a Streams Kafka API program as they are ingrained in the architecture of such two different systems.<\/p>\n\n\n\n

Conclusion:<\/strong><\/p>\n\n\n\n

Both, Flume systems and Apache Kafka offers\nthe most reliable, scalable as well as high-performance for the purpose to\nhandle some of the huge volumes of data with complete ease. On the other hand,\nKafka is a much general kind of purpose system where various publishers, as\nwell as subscribers, will be able to share diverse topics.<\/p>\n","protected":false},"excerpt":{"rendered":"

The stream of open-source processing space is exploding currently, with several systems that are now becoming available presenting the users with various alternatives. In Apache …<\/p>\n","protected":false},"author":3,"featured_media":5166,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[149],"tags":[1711,1710,1712],"class_list":["post-5165","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-startup-resources","tag-apache-flink","tag-apache-flink-vs-kafka","tag-kafka"],"yoast_head":"\nApache Flink vs Kafka: What\u2019s the Difference? - Developers, Designers & Freelancers - FreelancingGig<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.freelancinggig.com\/blog\/2019\/12\/21\/apache-flink-vs-kafka-whats-the-difference\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Apache Flink vs Kafka: What\u2019s the Difference? - Developers, Designers & Freelancers - FreelancingGig\" \/>\n<meta property=\"og:description\" content=\"The stream of open-source processing space is exploding currently, with several systems that are now becoming available presenting the users with various alternatives. 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