{"id":173245,"date":"2023-11-11T20:19:43","date_gmt":"2023-11-11T19:19:43","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=173245"},"modified":"2023-11-11T20:25:53","modified_gmt":"2023-11-11T19:25:53","slug":"demystifying-spark-streaming-a-deep-dive-into-its-functionality","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/demystifying-spark-streaming-a-deep-dive-into-its-functionality","title":{"rendered":"Demystifying Spark Streaming: A Deep Dive into Its Functionality"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"173245\" class=\"elementor elementor-173245\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-363348f e-flex e-con-boxed e-con\" data-id=\"363348f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6df060a2 elementor-widget elementor-widget-heading\" data-id=\"6df060a2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Spark Streaming is an innovative solution for real-time data processing. It is a component of the Apache Spark framework that offers exceptional performance, scalability and reliability.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a74177c elementor-widget elementor-widget-text-editor\" data-id=\"7a74177c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>This distributed real-time processing system has been designed to meet the <a href=\"https:\/\/datascientest.com\/en\/apache-kafka-the-real-time-data-processing-platform\">most complex real-time data processing requirements.<\/a> It enables complex analysis and transformation tasks to be performed on data from a variety of sources (such as social networks, connected devices or sensors).<\/p><p>Thanks to its advanced features, such as management of <a href=\"https:\/\/datascientest.com\/en\/dataflow-power-bi-everything-you-need-to-know-about-the-self-service-etl-tool\">very large data flows<\/a>, integration of various data sources and support for fault tolerance, Spark Streaming has established itself as the benchmark choice for companies looking to process real-time data efficiently.<\/p><p>The areas of application for such technology are extremely varied. Examples<strong> include fraud detection<\/strong>, financial market monitoring, personalised recommendations for online purchases, and of course social network analysis.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-908fa66 e-flex e-con-boxed e-con\" data-id=\"908fa66\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7455d1c3 elementor-widget elementor-widget-heading\" data-id=\"7455d1c3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How does data streaming work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4d282253 elementor-widget elementor-widget-text-editor\" data-id=\"4d282253\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Data streaming<\/strong> is a real-time process that involves processing data that is generated continuously and analysing it in real time.<\/p><p>Spark Streaming uses a &#8216;micro-batch&#8217; architecture, which means that data is divided into small batches and processed sequentially.<\/p><p>Each batch is treated as an <strong>RDD (Resilient Distributed Dataset) in Spark,<\/strong> allowing it to benefit from Spark&#8217;s parallel processing power. As a reminder, an RDD is the database unit in <a href=\"https:\/\/datascientest.com\/en\/apache-spark-its-functions-and-benefits\">Apache Spark<\/a>, which is an immutable collection of data shared in parallel across multiple nodes in a cluster.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6d2e4277 elementor-widget elementor-widget-image\" data-id=\"6d2e4277\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1071\" height=\"239\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/image2-1.png\" class=\"attachment-full size-full wp-image-173249\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/image2-1.png 1071w, https:\/\/datascientest.com\/en\/files\/2023\/11\/image2-1-300x67.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/11\/image2-1-1024x229.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/11\/image2-1-768x171.png 768w\" sizes=\"(max-width: 1071px) 100vw, 1071px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b1ab57f elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"7b1ab57f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn how to do data streaming with Spark<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-617e7126 e-flex e-con-boxed e-con\" data-id=\"617e7126\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4b35f0f9 elementor-widget elementor-widget-heading\" data-id=\"4b35f0f9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What are DStreams?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-67baf2ed elementor-widget elementor-widget-text-editor\" data-id=\"67baf2ed\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Spark Streaming<\/strong> provides a high-level abstraction called a Discretized Stream, or DStream, which represents a continuous stream of data.<\/p><p>They can be thought of as <strong>continuous RDDs,<\/strong> where each RDD represents data generated within a defined interval.<\/p><p>DStreams can be created from input streams from sources such as Kafka, Twitter or Flume, or by applying high-level operations on other DStreams, and can be used to perform various operations such as filtering, aggregating, joining, etc., to produce the resulting data in real time.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11ddd95f elementor-widget elementor-widget-image\" data-id=\"11ddd95f\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1290\" height=\"408\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/image3-1.png\" class=\"attachment-full size-full wp-image-173250\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/image3-1.png 1290w, https:\/\/datascientest.com\/en\/files\/2023\/11\/image3-1-300x95.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/11\/image3-1-1024x324.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/11\/image3-1-768x243.png 768w\" sizes=\"(max-width: 1290px) 100vw, 1290px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-466d8eef elementor-widget elementor-widget-heading\" data-id=\"466d8eef\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><u>Let's take an example:<\/u><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1433740d elementor-widget elementor-widget-text-editor\" data-id=\"1433740d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Imagine you work for a public transport company that wants to monitor passengers getting on and off buses in real time. To do this, the buses are equipped with sensors that send continuous information to the company&#8217;s servers.<\/p><p>Spark Streaming will allow us to use a <strong>DStream to process this data in real time, where each DStream<\/strong> will represent the data sent by the bus sensors in predefined time intervals. We will use operations on these DStreams to perform our real-time analyses.<\/p><p>Using aggregations, we can obtain the total number of passengers on the buses at any one time. We&#8217;ll also be able to use filters to identify buses that have reached their maximum capacity, so that we can send out passenger safety alerts.<\/p><p>In short, thanks to <strong>Spark Streaming<\/strong> we can carry out real-time analyses on the data sent by the sensors, and use the results to improve the safety and efficiency of the public transport system.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-733f9fbf e-flex e-con-boxed e-con\" data-id=\"733f9fbf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-75fc27b elementor-widget elementor-widget-heading\" data-id=\"75fc27b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What about fault tolerance and replication?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-69cfa0df elementor-widget elementor-widget-text-editor\" data-id=\"69cfa0df\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Spark Streaming<\/strong> guarantees fault tolerance using data replication and job recovery techniques.<\/p><p>Data replication involves duplicating data across multiple nodes to ensure that it is available in the event of a node failure. This measure also ensures the robustness of the system by guaranteeing that the data will not be lost.<\/p><p>Job resumption, on the other hand, is a mechanism that enables jobs to be restarted on other nodes in the event of a failure, so as to guarantee continuity of data processing even in the event of a problem.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7586212e elementor-widget elementor-widget-image\" data-id=\"7586212e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1200\" height=\"628\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/image1-1.png\" class=\"attachment-full size-full wp-image-173251\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/11\/image1-1.png 1200w, https:\/\/datascientest.com\/en\/files\/2023\/11\/image1-1-300x157.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/11\/image1-1-1024x536.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/11\/image1-1-768x402.png 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-416cec69 e-flex e-con-boxed e-con\" data-id=\"416cec69\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-62eea370 elementor-widget elementor-widget-heading\" data-id=\"62eea370\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b094db5 elementor-widget elementor-widget-text-editor\" data-id=\"4b094db5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Spark Streaming can therefore be used to address <a href=\"https:\/\/datascientest.com\/en\/big-data-training-everything-you-need-to-know\">Big Data issues in real time.<\/a> The fact that it supports a variety of data sources, and that it allows you to use a single framework for a wide range of needs, is nonetheless a significant advantage.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-72c6f94d elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"72c6f94d\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Start a training course on data streaming<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1907c0ef e-flex e-con-boxed e-con\" data-id=\"1907c0ef\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Spark Streaming is an innovative solution for real-time data processing. It is a component of the Apache Spark framework that offers exceptional performance, scalability and reliability. This distributed real-time processing system has been designed to meet the most complex real-time data processing requirements. It enables complex analysis and transformation tasks to be performed on data [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":173247,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Demystifying Spark Streaming: A Deep Dive into Its Functionality<\/title>\n<meta name=\"description\" content=\"Explore the realm of Spark Streaming, unraveling its intricacies and uncovering how this powerful technology operates. 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