{"id":66477,"date":"2018-05-31T05:36:24","date_gmt":"2018-05-31T05:36:24","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=66477"},"modified":"2021-01-13T07:37:36","modified_gmt":"2021-01-13T07:37:36","slug":"apache-storm-vs-apache-spark","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/apache-storm-vs-apache-spark\/","title":{"rendered":"Apache Storm Vs Apache Spark [Comparison]"},"content":{"rendered":"

With the increase of real-time data, the need for real-time data streaming is also growing. Not to mention, the streaming technologies are leading the Big Data<\/span><\/a> world now. With the newer real-time streaming platforms, it becomes complex for the users to choose one. Apache Storm and Spark are two most popular real-time technologies in the list. <\/span><\/p>\n

Let\u2019s compare Apache Storm and Spark on the basis of their features, and help users to make a choice. The purpose of this article Apache Storm Vs Apache Spark is not to make a judgment about one or other, but to study the similarities and differences between the two. <\/span><\/p>\n

In this blog, we will cover the Apache Storm Vs Apache Spark comparison. Let\u2019s start first with the introduction to each, after that we will move to the comparison of Apache Storm Vs Spark on the basis of the features of both.<\/span><\/p>\n

What is Apache Storm Vs Apache Spark?<\/b><\/span><\/h2>\n

To understand Spark Vs Storm, let\u2019s first get into the fundamentals of both!<\/span><\/p>\n

Apache Storm<\/b><\/span><\/h4>\n

Apache Storm is an open source, fault-tolerant, scalable, and real-time stream processing computation system. It is the framework for real-time distributed data processing. It focuses on event processing or stream processing. Storm actualizes a fault tolerant mechanism to perform a computation or to schedule multiple computations of an event. Apache storm is based on streams and tuples.<\/span><\/p>\n

Apache Spark<\/b><\/span><\/h4>\n

Apache Spark is a lightning fast Big Data technology framework for cluster computing. It has been designed to perform fast computation on the processing of large datasets. It is an engine for distributed processing but does not have an inbuilt distributed storage system and resource manager. One need to plug into a storage system and cluster resource manager of own choice. <\/span><\/p>\n

Apache YARN or Mesos can be used for cluster manager and Google Cloud Storage, Microsoft Azure, HDFS (Hadoop Distributed File System) and Amazon S3 can be used for the resource manager.<\/span><\/p>\n

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Want to learn Apache Spark? Here is the comprehensive guide that will make you learn Apache Spark<\/a>!<\/span><\/p>\n<\/blockquote>\n

Comparison between Apache Storm Vs Apache Spark<\/b><\/span><\/h2>\n

Here we are going to explain feature wise difference between real-time processing tools like Apache Spark and Apache Storm. Let\u2019s have a look at each and every feature one by one to compare Apache Storm vs Apache Spark. It will help us to learn and decide which is one better to adopt on the basis of that particular feature.<\/span><\/p>\n

1. Processing Model<\/b><\/h4>\n