Hadoop windows

August 30, 2016 3 minute read My latest Pluralsight course is out now: Hadoop for .NET DevelopersIt takes you through running Hadoop on Windows and using .NET to write MapReduce queries - proving that you can do Big Data on the Microsoft stack.The course has five modules, starting with the architecture of Hadoop and working through a proof-of-concept approach, evaluating different options for running Hadoop and integrating it with .NET.1. Introducing HadoopHadoop is the core technology in Big Data problems - it provides scalable, reliable storage for huge quantities of data, and scalable, reliable compute for querying that data. To start the course I cover HDFS and YARN - how they work and how they work together. I use a 600MB public dataset (from the 2011 UK census), upload it to HDFS and demonstrate a simple Java MapReduce query. Unlike my other Pluralsight course, Real World Big Data in Azure, there are word counts in this course - to focus on the technology, I keep the queries simple for this one.2. Running Hadoop on WindowsHadoop is a Java technology, so you can run it on any system with a compatible JVM. You don’t need to run Hadoop from the JAR files though, there are packaged options which make it easy to run Hadoop on Windows. I cover four options: Hadoop in Docker - using my Hadoop with .NET Core Docker image to run a Dockerized Hadoop cluster; Hortonworks Data Platform, a packaged Hadoop distribution which is available for Linux and Windows; Syncfusion’s Big Data Platform, a new Windows-only Hadoop distribution which has a friendly UI; Azure HDInsight, Microsoft’s managed Hadoop platform in the cloud. If you’re starting out with Hadoop, the Big Data Platform is a great place to start - it’s a simple two-click install, and it comes with lots of sample code.3. Working with Hadoop in .NETJava is the native programming language for MapReduce queries, but Hadoop provides integration for any language with the Hadoop Streaming API. I walk through building a MapReduce program with the full .NET Framework, then using .NET Core, and compare those options with Microsoft’s Hadoop SDK for .NET (spoiler: the SDK is a nice framework, but hasn’t seen much activity for a while). Using .NET Core for MapReduce jobs gives you the option to write queries in C# and run them on Linux or Windows clusters, as I blogged about in Hadoop and .NET Core - A Match Made in Docker.4. Querying Data with MapReduceBasic MapReduce jobs are easy with .NET and .NET Core, but in this module we look at more advanced functionality and see how to write performant, reliable .NET MapReduce jobs. In this module I extend the .NET queries to

Use: combiners; multiple reducers; the distributed cache; counters and logging. You can run Hadoop on Windows and use .NET for queries, and still make use of high-level Hadoop functionality to tune your queries.5. Navigating the Hadoop EcosystemHadoop is a foundational technology, and querying with MapReduce gives you a lot of power - but it’s a technical approach which needs custom-built components. A whole ecosystem has emerged to take advantage of the core Hadoop foundations of storage and compute, but make accessing the data faster and easier. In the final module, I look at some of the major technologies in the ecosystem and see how they work with Hadoop, and with each other, and with .NET: Hive - querying data in Hadoop with a SQL-like language; HBase - a real-time Big Data NoSQL database which uses Hadoop for storage; Spark - a compute engine which uses Hadoop, but caches data in-memory to provide fast data access. If the larger ecosystem interests you, I go into more depth with a couple of free eBooks: Hive Succinctly and HBase Succinctly, and I also cover them in detail on Azure in my Pluralsight course HDInsight Deep Dive: Storm, HBase and Hive.The goal of Hadoop for .NET Developers is to give you a thorough grounding in Hadoop, so you can run your own PoC using the approach in the course, to evaluate Hadoop with .NET for your own datasets.

Group: Apache HadoopApache Hadoop CommonLast Release on Oct 18, 2024Apache Hadoop Client aggregation pom with dependencies exposedLast Release on Oct 18, 2024Apache Hadoop HDFSLast Release on Oct 18, 2024Apache Hadoop MapReduce CoreLast Release on Oct 18, 2024Hadoop CoreLast Release on Jul 24, 2013Apache Hadoop AnnotationsLast Release on Oct 18, 2024Apache Hadoop Auth - Java HTTP SPNEGOLast Release on Oct 18, 2024Apache Hadoop Mini-ClusterLast Release on Oct 18, 2024Apache Hadoop YARN APILast Release on Oct 18, 2024Apache Hadoop MapReduce JobClientLast Release on Oct 18, 2024Apache Hadoop YARN CommonLast Release on Oct 18, 2024Apache Hadoop MapReduce CommonLast Release on Oct 18, 2024Apache Hadoop YARN ClientLast Release on Oct 18, 2024This module contains code to support integration with Amazon Web Services.It also declares the dependencies needed to work with AWS services.Last Release on Oct 18, 2024Apache Hadoop HDFS ClientLast Release on Oct 18, 2024Apache Hadoop MapReduce AppLast Release on Oct 18, 2024Apache Hadoop YARN Server TestsLast Release on Oct 18, 2024Apache Hadoop MapReduce ShuffleLast Release on Oct 18, 2024Hadoop TestLast Release on Jul 24, 2013Apache Hadoop YARN Server CommonLast Release on Oct 18, 2024Prev12345678910Next

Hadoop is a distributed computing framework for processing and storing massive datasets. It runs on Ubuntu and offers scalable data storage and parallel processing capabilities.Installing Hadoop enables you to efficiently handle big data challenges and extract valuable insights from your data.To Install Hadoop on Ubuntu, the below steps are required:Install Java.Create a User.Download Hadoop.Configure Environment.Configure Hadoop.Start Hadoop.Access Web Interface.Prerequisites to Install Hadoop on UbuntuComplete Steps to Install Hadoop on UbuntuStep 1: Install Java Development Kit (JDK)Step 2: Create a dedicated user for Hadoop & Configure SSHStep 3: Download the latest stable releaseStep 4: Configure Hadoop Environment VariablesStep 5: Configure Hadoop Environment VariablesStep 6: Start the Hadoop ClusterStep 7: Open the web interfaceWhat is Hadoop and Why Install it on Linux Ubuntu?What are the best Features and Advantages of Hadoop on Ubuntu?What to do after Installing Hadoop on Ubuntu?How to Monitor the Performance of the Hadoop Cluster?Why Hadoop Services are Not starting on Ubuntu?How to Troubleshoot issues with HDFS?Why My MapReduce jobs are failing?ConclusionPrerequisites to Install Hadoop on UbuntuBefore installing Hadoop on Ubuntu, make sure your system is meeting below specifications:A Linux VPS running Ubuntu.A non-root user with sudo privileges.Access to Terminal/Command line.Complete Steps to Install Hadoop on UbuntuOnce you provided the above required options for Hadoop installation Ubuntu including buying Linux VPS, you are ready to follow the steps of this guide.In the end, you will be able to leverage its capabilities to efficiently manage and analyze large datasets.Step 1: Install Java Development Kit (JDK)Since Hadoop requires Java to run, use the following command to install the default JDK and JRE:sudo apt install default-jdk default-jre -yThen, run the command below to Verify the installation by checking the Java version:java -versionOutput:java version "11.0.16" 2021-08-09 LTSOpenJDK 64-Bit Server VM (build 11.0.16+8-Ubuntu-0ubuntu0.22.04.1)As you see, if Java is installed, you’ll see the version information.Step 2: Create a dedicated user for Hadoop & Configure SSHTo create a new user, run the command below and create the Hadoop user:sudo adduser hadoopTo add the user to the sudo group, type:sudo usermod -aG sudo hadoopRun the command below to switch to the Hadoop user:sudo su - hadoopTo install OpenSSH server and client, run:sudo apt install openssh-server openssh-client -yThen, generate SSH keys by running the following command:ssh-keygen -t rsaNotes:Press Enter to save the key to the default location.You can optionally set a passphrase for added security.Now, you can add the public key to authorized_keys:cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keysTo set permissions