{"id":60257,"date":"2018-03-05T12:01:54","date_gmt":"2018-03-05T06:31:54","guid":{"rendered":"https:\/\/www.whizlabs.com\/?p=60257"},"modified":"2024-05-13T10:43:14","modified_gmt":"2024-05-13T05:13:14","slug":"importance-of-apache-spark","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/importance-of-apache-spark\/","title":{"rendered":"Importance of Apache Spark in Big Data Industry"},"content":{"rendered":"<p style=\"text-align: justify\"><span lang=\"EN-GB\">Hadoop has already proved its huge potential in the Big data industry by providing better insights on data to make the business grow. With its unbeatable Big data processing capability using batch processing it has redefined Big data domain. Since<\/span><span lang=\"EN-GB\">\u00a0Apache Spark stepped into Big data industry, it has met the enterprises&#8217; expectations in a better way regarding data processing, querying, and generating analytics reports in a faster way.\u00a0<\/span>Here&#8217;s why<b>\u00a0<\/b><a href=\"https:\/\/www.whizlabs.com\/blog\/why-is-apache-spark-faster\/\" target=\"_blank\" rel=\"noopener\">Apache Spark Faster<\/a>.<\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Apache Spark is widely considered as the future of Big Data Platform. In this blog, we will discuss the various aspects of why Apache Spark is gaining more importance in the big data industry.<\/span><\/p>\n<blockquote><p><span lang=\"EN\"><strong>Also Read:<\/strong>\u00a0<a href=\"https:\/\/www.whizlabs.com\/blog\/introduction-to-apache-spark\/\" target=\"_blank\" rel=\"noopener\">An Introduction to Apache Spark<\/a><\/span><\/p><\/blockquote>\n<h2 style=\"text-align: justify\"><b><span lang=\"EN-GB\">Apache Spark Improves Business in Big Data Industry<\/span><\/b><\/h2>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">The primary importance of Apache Spark in the Big data industry is because of its in-memory data processing that makes it high-speed data processing engine compare to MapReduce.<\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Apache Spark has huge potential to contribute to Big data related business in the industry. The different business advantages it carries are \u2013 <\/span><\/p>\n<ul style=\"text-align: justify\">\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">It is an ideal tool for companies that focus on Internet of Things. Spark can handle many analytics challenges because of its low-latency in-memory data processing capability. Besides that, it has well-built libraries for machine learning and graph analytics algorithms.<\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">By utilizing Spark, organizations can enable themselves to analyze data coming from IoT sensors. It becomes possible as Spark can easily process continuous streams of low-latency data. Hence, organizations can create real-time dashboards and explore data to monitor and optimize their business.<\/span><\/li>\n<li>With its high-level libraries for data streaming, machine learning, SQL queries, graph analysis, Spark helps Big data scientists to create complex workflows easily. This not only ensures less coding but also the faster insights on organization&#8217;s big data analysis.<\/li>\n<li><span lang=\"EN-GB\">Data scientists can prototype solutions easily using Spark which led to better feedback.<\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">Fog computing is going to be the next biggest thing after IoT for de-centralized data processing. Apache Spark has the power of analyzing the huge amount of distributed data. As a result, it will help organizations to work on making IoT based applications for new businesses.<\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">Spark can work on top of existing Hadoop Distributed File System (HDFS), and it works well with Hadoop. Hence, organizations don&#8217;t need to build a new set up for Spark. Using the same data and cluster they can deploy Spark on the same Hadoop cluster. It is a more noticeable cost-saving enhancement for the organizations.<\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">As Spark is compatible with many programming languages like Java, Scala, Python, R, etc., it is easy to use and require less coding. Moreover, there is a significant community of programmers for Spark. Hence, organizations don&#8217;t need to hire expensive resources separately. <\/span><\/li>\n<\/ul>\n<h2 style=\"text-align: justify\"><b><span lang=\"EN-GB\">Know the Apache Spark Technology Underneath and Its Features<\/span><\/b><span lang=\"EN-GB\"><\/span><\/h2>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Apache Spark is a Big data processing interface which provides not only programming interface in the data cluster but also adequate fault tolerance and data parallelism. This open-source platform is efficient in speedy processing of massive datasets.<\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Big data processing needs superior abilities which Apache Spark provides better than Hadoop MapReduce. <b><\/b><\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">The features of Apache Spark are as follows:<\/span><\/p>\n<p><a href=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/05\/features-of-Apache-Spark.png\"><img decoding=\"async\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/05\/features-of-Apache-Spark.png\" alt=\"features of Apache Spark\" width=\"560\" height=\"315\" class=\"aligncenter size-full wp-image-61742\" \/><\/a><\/p>\n<h4 style=\"text-align: justify\"><b><span lang=\"EN-GB\">An Integrated Framework<\/span><\/b><span lang=\"EN-GB\"><\/span><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Apache Spark delivers a better-integrated framework which supports all ranges of Big data formats like batch data, text data, real-time streaming data, graphical data, etc.<\/span><\/p>\n<h4 style=\"text-align: justify\"><b><span lang=\"EN-GB\">Data Processing Speed<\/span><\/b><span lang=\"EN-GB\"><\/span><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Spark processes data in a cyclic data flow and in-memory data sharing way using its execution engine. Interestingly Spark engine supports its DAG(Directed Acrylic Graph) mechanism which carries out multiple jobs with the same set of data. As a result, Spark can process data almost 100 times faster than Hadoop MapReduce.<\/span><\/p>\n<h4 style=\"text-align: justify\"><b><span lang=\"EN-GB\">Multiple Programming Language Support<\/span><\/b><span lang=\"EN-GB\"><\/span><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Apache Spark lets programmers write applications using Python, Clojure, Scala or Java as it has the inbuilt support of over 80 high-level operators.<\/span><\/p>\n<h4 style=\"text-align: justify\"><b><span lang=\"EN-GB\">Enhanced Support for Multiple Operations <\/span><\/b><span lang=\"EN-GB\"><\/span><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Spark provides numerous essential supports related to data processing in big data industry like \u2013<\/span><\/p>\n<ul style=\"text-align: justify\">\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">For streaming data<\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">SQL queries<\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">Graphic data processing,<\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">Machine learning, <\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">MapReduce operations. <\/span><\/li>\n<\/ul>\n<h4 style=\"text-align: justify\"><b><span lang=\"EN-GB\">Multi-platform Support<\/span><\/b><span lang=\"EN-GB\"><\/span><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Apache Spark provides extended interoperability regarding its running platform or supported data structure. Spark supports applications running in \u2013<\/span><\/p>\n<ul style=\"text-align: justify\">\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">cloud <\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">standalone cluster mode<\/span><\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Besides, that Spark can access varied data structures<\/span><\/p>\n<ul style=\"text-align: justify\">\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">HBase<\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">Tachyon <\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">HDFS <\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">Cassandra<\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">Hive<\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">Hadoop data source <\/span><\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Spark can be deployed on <\/span><\/p>\n<ul style=\"text-align: justify\">\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">A distributed framework such as YARN or Mesos <\/span><\/li>\n<li><span lang=\"EN-GB\"> <\/span><span lang=\"EN-GB\">Standalone server\u00a0<\/span><\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/05\/Apache-Spark-Features.png\"><img decoding=\"async\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/05\/Apache-Spark-Features.png\" alt=\"Apache Spark Features\" width=\"707\" height=\"455\" class=\"aligncenter wp-image-60284 size-full\" \/><\/a><\/p>\n<h2 style=\"text-align: justify\"><b><span lang=\"EN-GB\">Important Features That Make Apache Spark a Better Choice <\/span><\/b><\/h2>\n<h4 style=\"text-align: justify\"><b><span lang=\"EN-GB\">Apache Spark Data Streaming is Superior to Traditional Systems<\/span><\/b><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Given below is a figure displaying why Spark streaming is superior to traditional systems:<\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Traditionally data streaming follows static task scheduling. On the other hand in Spark data streaming it is dynamic scheduling of tasks which make the overall processing faster.<\/span><\/p>\n<h4><b><span lang=\"EN-GB\"><a href=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/05\/Apache-Spark-Data-Streaming.jpg\"><img decoding=\"async\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/05\/Apache-Spark-Data-Streaming.jpg\" alt=\"Apache Spark Data Streaming\" width=\"504\" height=\"357\" class=\"aligncenter wp-image-60279 size-full\" \/><\/a><\/span><\/b><\/h4>\n<h4 style=\"text-align: justify\"><span lang=\"EN-GB\">Apache Spark Structured Streaming for Infinite Data Streaming<\/span><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Structured Streaming is the part of Spark 2.x which is a higher-level API. It helps in creating a more natural abstraction for writing applications. Using Structure Streaming, developers can create infinite streaming data frames as well datasets. With Structured Streaming, a user can efficiently handle message delivery. <\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Structured streaming facilitates users with the Catalyst query optimizer. Moreover, it can run in an interactive manner. As a result, it allows users to perform SQL queries for live streaming data.<\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Though structured streaming is still a new venture in Apache Spark, it is the future of data streaming. <\/span><\/p>\n<h4 style=\"text-align: justify\"><b><span lang=\"EN-GB\">Enterprise can Use Apache Spark on the Top of Existing Hadoop Structure<\/span><\/b><span lang=\"EN-GB\"><\/span><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Apache Spark can be considered as an enhancement on the existing Hadoop infrastructure of a company for a speedy Big Data processing. One can easily deploy Apache Spark applications. It can run on existing Hadoop v1 and v2 cluster using an existing Hadoop Distributed File System(HDFS).<\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Though HDFS works as the primary data storage by Spark, it can work with other data sources compatible with Hadoop like HBase, Cassandra, etc.<\/span><\/p>\n<h4 style=\"text-align: justify\"><strong><span lang=\"EN-GB\">Apache Spark: A New Dimension in Big data Industry for Data Scientists<\/span><\/strong><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Apache Spark shows an arena for the data scientists where they can build sophisticated data analysis models. The volume and type of data they can use for such analysis were beyond imagination before Spark.<\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Visualization is an integral part while dealing with data analysis for business purposes. This is more important for Big data analysis. Spark Core helps data scientists to create such reports and dashboards using Java, Python, R scripts, etc. <strong><\/strong><\/span><\/p>\n<h4 style=\"text-align: justify\"><strong><span lang=\"EN-GB\">Spark\u2019s Machine Learning Capability may Help in Data Lake Flow<\/span><\/strong><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Recent organization trends towards data lake which is millions of pieces of data need predictive and automatic rules on accessibility. It not only enhances the business agility but also escapes manual interventions. <\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Apache Spark with its inbuilt machine learning algorithms can help in this data lake processing.<\/span><\/p>\n<h4 style=\"text-align: justify\"><strong><span lang=\"EN-GB\">Spark Edges Over Other Open Source Projects in Enterprise Adoption<\/span><\/strong><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Among all the Apache open source projects, Apache Spark has become the most in-demand technology in Big data industry across multiple verticals. In the current market scenario, there is an increasing demand to support BI related workloads with Spark SQL and Hadoop.\u00a0 <\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Moreover, there is a strong open source community support for Spark which makes increasing adoption rate of Spark by the enterprises.<strong><\/strong><\/span><\/p>\n<h2 style=\"text-align: justify\"><strong><span lang=\"EN-GB\">Can Learning Spark Benefit You as a Professional?<\/span><\/strong><\/h2>\n<blockquote>\n<p style=\"text-align: justify\"><em><strong><span lang=\"EN-GB\">\u00a0In a single sentence \u2013 Yes, walk with the pace of technology!<\/span><\/strong><\/em><strong><span lang=\"EN-GB\"><\/span><\/strong><\/p>\n<\/blockquote>\n<ul>\n<li><span lang=\"EN-GB\"> <\/span><b><span lang=\"EN-GB\">Coming years are all set to witness an increasing demand for Spark Developers<\/span><\/b><span lang=\"EN-GB\"><\/span><\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">As Spark has proved itself as a smarter alternative to MapReduce, enterprises more prefer to adopt it. Hence, besides Hadoop developers, demands for the Spark developers are high in the market. <\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">There are increasing needs for permanent as well as contractual positions for Spark developers in the market. IT professionals can leverage this upcoming skill set gap by pursuing a certification in Apache Spark.<\/span><\/p>\n<h4><b><span lang=\"EN-GB\"><a href=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/05\/Future-Areas-of-Apache-Spark.jpg\"><img decoding=\"async\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/05\/Future-Areas-of-Apache-Spark.jpg\" alt=\"Apache Spark\" width=\"540\" height=\"255\" class=\"aligncenter wp-image-60280 size-full\" \/><\/a><\/span><\/b><\/h4>\n<ul>\n<li style=\"text-align: justify\"><b><span lang=\"EN-GB\">Apache Spark offers impressive pay packages<\/span><\/b><\/li>\n<\/ul>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Since Spark developers are significantly in demand, chances of getting a job in this field his high. The average salary for an Apache Spark Developer in the US is $133,021 per annum which is almost 29% above Indian salary. However, if you convert the amount it is nothing less than the best pay package in the IT industry.<\/span><\/p>\n<h4 style=\"text-align: justify\"><strong><span lang=\"EN-GB\">Bottom Line<\/span><\/strong><\/h4>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Spark is being widely used in Big data industry for interactive scaling out batch data processing requirements. In addition to that, it is expected to play a key role in the next generation BI applications. Thus it is wise to take holistic, hands-on training in Spark to excel in the Big data industry. Moreover, it will boost productivity in case they are new to Scala programming.<\/span><\/p>\n<p style=\"text-align: justify\"><span lang=\"EN-GB\">Learning Spark as certification preparation also covers coding in Python, R, Java, etc. Whizlabs offers aspiring Hadoop and Big data professionals complete training guides for Cloudera and HortonWorks Hadoop related certifications. Our <a href=\"https:\/\/www.whizlabs.com\/spark-developer-certification\/\" target=\"_blank\" rel=\"noopener\">HDP Certified Developer (HDPCD) Spark Certification<\/a> covers all the technical details of Spark along with hands-on. It will meticulously help anyone to grab the concepts.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Hadoop has already proved its huge potential in the Big data industry by providing better insights on data to make the business grow. With its unbeatable Big data processing capability using batch processing it has redefined Big data domain. Since\u00a0Apache Spark stepped into Big data industry, it has met the enterprises&#8217; expectations in a better way regarding data processing, querying, and generating analytics reports in a faster way.\u00a0Here&#8217;s why\u00a0Apache Spark Faster. Apache Spark is widely considered as the future of Big Data Platform. In this blog, we will discuss the various aspects of why Apache Spark is gaining more importance [&hellip;]<\/p>\n","protected":false},"author":220,"featured_media":61743,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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Malhotra","author_link":"https:\/\/www.whizlabs.com\/blog\/author\/aditi\/"},"uagb_comment_info":16,"uagb_excerpt":"Hadoop has already proved its huge potential in the Big data industry by providing better insights on data to make the business grow. With its unbeatable Big data processing capability using batch processing it has redefined Big data domain. Since\u00a0Apache Spark stepped into Big data industry, it has met the enterprises&#8217; expectations in a better&hellip;","_links":{"self":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/60257","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/users\/220"}],"replies":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/comments?post=60257"}],"version-history":[{"count":3,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/60257\/revisions"}],"predecessor-version":[{"id":95652,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/60257\/revisions\/95652"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media\/61743"}],"wp:attachment":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=60257"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=60257"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=60257"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}