{"id":10921,"date":"2026-02-11T15:12:54","date_gmt":"2026-02-11T09:42:54","guid":{"rendered":"https:\/\/blog.tenthplanet.in\/?p=10921"},"modified":"2026-03-03T10:04:47","modified_gmt":"2026-03-03T10:04:47","slug":"pentaho-big-data","status":"publish","type":"post","link":"https:\/\/tenthplanet.in\/blogs\/pentaho-big-data\/","title":{"rendered":"Feature-wise Comparison Between Hadoop 2.x vs Hadoop 3.x"},"content":{"rendered":"\n<p>Comparing Hadoop 2.x and Hadoop 3.x helps organizations choose the right Hadoop distribution for Pentaho 10.2 integration. Understanding feature differences enables informed decisions based on business requirements, performance needs, and compatibility with Pentaho 10.2&#8217;s modern architecture.<\/p>\n\n\n\n<p>Learn about <a href=\"https:\/\/tenthplanet.in\/blogs\/pentaho-hadoop-integration-2\/\">Pentaho Hadoop integration<\/a> or explore <a href=\"https:\/\/tenthplanet.in\/blogs\/pentaho-platform-what-pentaho-offers-out-of-the-box-2\/\">Pentaho platform capabilities<\/a> for comprehensive big data solutions.<\/p>\n\n\n\n<p>We can compare Hadoop 2.x and Hadoop 3.x, analyze the features to determine which provides the better combination for Pentaho 10.2 deployments.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>License<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **Apache 2.0 is used for license.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **Apache 2.0 is used for license.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Minimum supported version of Java<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **Minimum supported version of java is java 7.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **Minimum supported version of java is java 8.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Fault Tolerance<\/h3>\n\n\n\n<p>HDFS is highly fault tolerant. It handles faults by the process of replica creation.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **Fault tolerance is handled by replication. HDFS by default replicates each block three times for a number of purposes.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **Fault tolerance is handled by Erasure coding. Erasure Coding is to use in the place of Replication, which provides the same level of fault tolerance.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Data Balancing<\/h3>\n\n\n\n<p>HDFS provides a balancer utility. This utility analyzes block placement and balances data across the Data Nodes.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **For data, balancing uses HDFS balancer. It distributes data across the disks of a datanode. HDFS might not always place data in a uniform way across the disks due to following reasons:<\/li>\n<\/ul>\n\n\n\n<p>A lot of writes and deletes<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Disk replacement<\/li>\n\n\n\n<li>**Version 3.x \u2013 **For data, balancing uses Intra-data node balancer, which is invoked via the HDFS disk balancer CLI. It distributes data in a uniform way on all disks of a datanode.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Storage Overhead<\/h3>\n\n\n\n<p>**HDFS **replicates each block for the purpose of fault tolerance.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **HDFS has 200% overhead in storage space.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **Storage overhead is only 50%.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Storage Overhead Example<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **If there is 6 block so there will be 18 blocks occupied the space because of the replication scheme.<\/li>\n\n\n\n<li><strong>Version 3.x \u2013<\/strong> If there is 6 block so there will be 9 blocks occupied the space 6 block and 3 for parity.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">YARN Timeline Service<\/h3>\n\n\n\n<p>The Storage and retrieval of application&#8217;s current and historic information in a generic fashion is addressed in YARN through the Timeline Serve<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **Uses an old timeline service which has scalability issues.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **Improve the timeline service v2 and improves the scalability and reliability of timeline service.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Default Ports Range<\/h3>\n\n\n\n<p>The default ports of Hadoop services are in the Linux ephemeral port range (32768-61000)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **In Hadoop 2.0 some default ports are Linux ephemeral port range. So at the time of startup, they will fail to bind.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **But in Hadoop 3.0 these ports have been moved out of the ephemeral range.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Compatible File System<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **HDFS, FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 file system Windows Azure Storage Blobs file system.<\/li>\n\n\n\n<li>**Version 3.x \u2013 *<em>Microsoft Azure Data Lake filesystem<\/em>*, **HDFS, FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 file system Windows Azure Storage Blobs file system.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">MR API Compatibility<\/h3>\n\n\n\n<p>The MapReduce Application Master REST API&#8217;s allow the user to get status on the running MapReduce application master.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n\n\n\n<li>**Version 3.x \u2013 **Here also MR API is compatible with running Hadoop 1.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Support for Microsoft Windows<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **It can be deployed on windows.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **It also supports for Microsoft windows.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Slots\/Container<\/h3>\n\n\n\n<p>Signifies an allocated resources to an ApplicationMaster. ResourceManager is responsible for issuing resource\/container to an ApplicationMaster.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **Hadoop 1 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **It also works on the concept of a container.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Single Point of Failure<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Version 2.x \u2013<\/strong> It has Features to overcome Single point of failover, so whenever Namenode fails it recovers automatically.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **It has Features to overcome Single point of failover, so whenever Namenode fails it recovers automatically.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">HDFS Federation<\/h3>\n\n\n\n<p>HDFS Federation improves the existing HDFS architecture through a clear separation of namespace and storage, enabling generic block storage layer.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **In Hadoop 1.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Scalability<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **In hadoop 2.x, we can scale up to 10,000 Nodes per cluster.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **Hadoop 3.x provides better scalability compared with Hadoop 2.x. We can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Faster Access to Data<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **Due to data Node caching we can fast access the data.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **Similar to Hadoop 2.x, In Hadoop 3.x also due to data node caching we can fast access the data.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Platform<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>**Version 2.x \u2013 **It can serve as a platform for a wide variety of data analytics as possible to run event processing, streaming, and real-time operations.<\/li>\n\n\n\n<li>**Version 3.x \u2013 **Similar to Hadoop version 2.0, It can also serve as a platform for a wide variety of data analytics as possible to run event processing, streaming, and real-time operations.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">What are the key differences between Hadoop 2.x and 3.x?<\/h3>\n\n\n\n<p>Key differences include performance improvements (Hadoop 3.x provides better performance), storage efficiency (Hadoop 3.x uses erasure coding reducing storage overhead), Java version support (Hadoop 3.x requires Java 8+), containerization support (Hadoop 3.x better supports Docker\/Kubernetes), and compatibility with modern architectures.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How does Pentaho 10.2 integrate with Hadoop?<\/h3>\n\n\n\n<p>Pentaho 10.2 integrates with Hadoop through PDI&#8217;s native Hadoop connectors, support for HDFS (Hadoop Distributed File System), Hive integration for SQL queries, Spark integration for distributed processing, and compatibility with both Hadoop 2.x and 3.x distributions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Which Hadoop version should I choose for Pentaho 10.2?<\/h3>\n\n\n\n<p>Choose Hadoop version based on business requirements, performance needs, and compatibility with Pentaho 10.2&#8217;s modern architecture. Hadoop 3.x provides better performance and modern features, while Hadoop 2.x may be suitable for existing deployments with compatibility requirements.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What are the benefits of Hadoop 3.x for Pentaho integration?<\/h3>\n\n\n\n<p>Benefits of Hadoop 3.x include better performance (improved processing speed), storage efficiency (erasure coding reduces storage overhead), modern architecture support (Docker, Kubernetes), Java 8+ support (compatible with Pentaho 10.2&#8217;s Java 17), and enhanced security features.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Does Pentaho 10.2 support both Hadoop 2.x and 3.x?<\/h3>\n\n\n\n<p>Yes. Pentaho 10.2 supports both Hadoop 2.x and 3.x distributions through native connectors and integration capabilities. PDI provides connectors for HDFS, Hive, and Spark, enabling integration with both Hadoop versions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How does Hadoop integration work with Pentaho 10.2?<\/h3>\n\n\n\n<p>Hadoop integration with Pentaho 10.2 works through PDI&#8217;s native connectors for HDFS (data storage), Hive (SQL queries), and Spark (distributed processing). Pentaho 10.2&#8217;s modern architecture (Java 17, Docker) provides enhanced compatibility with Hadoop 3.x.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can I get help with Hadoop selection and Pentaho integration?<\/h3>\n\n\n\n<p>Yes. TenthPlanet provides expert Hadoop selection and Pentaho integration services including feature comparison, performance analysis, compatibility testing, and integration implementation. We help organizations choose the right Hadoop distribution for their Pentaho deployment.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83c\udfaf Ready to choose the right Hadoop version?<\/h2>\n\n\n\n<p>Comparing Hadoop 2.x and 3.x helps organizations make informed decisions based on business requirements, performance needs, and Pentaho 10.2 compatibility. Learn how to choose the right Hadoop distribution for your Pentaho integration.<\/p>\n\n\n\n<p><a href=\"https:\/\/tenthplanet.in\/getintouch\/\">Contact TenthPlanet<\/a> for expert Hadoop selection and Pentaho integration services.<\/p>\n\n\n\n<p>Note: This guide provides a comprehensive comparison of Hadoop 2.x and 3.x for Pentaho integration. Actual selection may vary based on your specific business requirements, performance needs, and deployment environment.<\/p>\n\n\n\n<p><strong>Related Resources:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/tenthplanet.in\/resources\/category\/pentaho\/#casestudies\">TenthPlanet Case Studies<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/tenthplanet.in\/pentaho\/services\/\">TenthPlanet Pentaho Services<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/tenthplanet.in\/getintouch\/\">Contact TenthPlanet<\/a><\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n","protected":false},"excerpt":{"rendered":"<p>Comparing Hadoop 2.x and Hadoop 3.x helps organizations choose the right Hadoop distribution for Pentaho 10.2 integration. Understanding feature differences [&hellip;]<\/p>\n","protected":false},"author":23,"featured_media":11183,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[424],"tags":[807,808,809,810,811,812,671],"class_list":["post-10921","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pentaho","tag-big-data-integration","tag-bigdata","tag-hadoop-2-x-vs-3-x","tag-hadoop-comparison","tag-hadoop-selection","tag-pentaho-big-data","tag-pentaho-hadoop-integration"],"acf":[],"_links":{"self":[{"href":"https:\/\/tenthplanet.in\/blogs\/wp-json\/wp\/v2\/posts\/10921","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tenthplanet.in\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tenthplanet.in\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tenthplanet.in\/blogs\/wp-json\/wp\/v2\/users\/23"}],"replies":[{"embeddable":true,"href":"https:\/\/tenthplanet.in\/blogs\/wp-json\/wp\/v2\/comments?post=10921"}],"version-history":[{"count":0,"href":"https:\/\/tenthplanet.in\/blogs\/wp-json\/wp\/v2\/posts\/10921\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/tenthplanet.in\/blogs\/wp-json\/wp\/v2\/media\/11183"}],"wp:attachment":[{"href":"https:\/\/tenthplanet.in\/blogs\/wp-json\/wp\/v2\/media?parent=10921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tenthplanet.in\/blogs\/wp-json\/wp\/v2\/categories?post=10921"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tenthplanet.in\/blogs\/wp-json\/wp\/v2\/tags?post=10921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}