This document outlines the key tasks and responsibilities of a Hadoop administrator. It discusses five top Hadoop admin tasks: 1) cluster planning which involves sizing hardware requirements, 2) setting up a fully distributed Hadoop cluster, 3) adding or removing nodes from the cluster, 4) upgrading Hadoop versions, and 5) providing high availability to the cluster. It provides guidance on hardware sizing, installing and configuring Hadoop daemons, and demos of setting up a cluster, adding nodes, and enabling high availability using NameNode redundancy. The goal is to help administrators understand how to plan, deploy, and manage Hadoop clusters effectively.
The Hadoop Cluster Administration course at Edureka starts with the fundamental concepts of Apache Hadoop and Hadoop Cluster. It covers topics to deploy, manage, monitor, and secure a Hadoop Cluster. You will learn to configure backup options, diagnose and recover node failures in a Hadoop Cluster. The course will also cover HBase Administration. There will be many challenging, practical and focused hands-on exercises for the learners. Software professionals new to Hadoop can quickly learn the cluster administration through technical sessions and hands-on labs. By the end of this six week Hadoop Cluster Administration training, you will be prepared to understand and solve real world problems that you may come across while working on Hadoop Cluster.
A day in the life of hadoop administrator!Edureka!
The document outlines the daily tasks of a Hadoop administrator, which include:
- Monitoring the cluster using tools like Cloudera Manager and Nagios in the morning
- Planning the day and reviewing past tasks in a meeting
- Running regular utility tasks like files mergers and backups
- Scheduling and configuring jobs, analyzing failed tasks, and troubleshooting issues
- Upgrading and updating the Hadoop cluster as needed
This document discusses the Hadoop cluster configuration at InMobi. It includes details about the cluster hardware specifications with 450 nodes and 5PB of storage. It also describes the software stack including Hadoop, Falcon, Oozie, Kafka and monitoring tools like Nagios and Graphite. The document then outlines some common issues faced like tasks hogging CPU resources and solutions implemented like cgroups resource limits. It provides examples of NameNode HA failover challenges and approaches to address slow running jobs.
This document provides an overview and configuration instructions for Hadoop, Flume, Hive, and HBase. It begins with an introduction to each tool, including what problems they aim to solve and high-level descriptions of how they work. It then provides step-by-step instructions for downloading, configuring, and running each tool on a single node or small cluster. Specific configuration files and properties are outlined for core Hadoop components as well as integrating Flume, Hive, and HBase.
Hadoop Administrator Online training course by (Knowledgebee Trainings) with mastering Hadoop Cluster: Planning & Deployment, Monitoring, Performance tuning, Security using Kerberos, HDFS High Availability using Quorum Journal Manager (QJM) and Oozie, Hcatalog/Hive Administration.
Contact : [email protected]
With the advent of Hadoop, there comes the need for professionals skilled in Hadoop Administration making it imperative to be skilled as a Hadoop Admin for better career, salary and job opportunities.
With the advent of Hadoop, there comes the need for professionals skilled in Hadoop Administration making it imperative to be skilled as a Hadoop Admin for better career, salary and job opportunities.
The following blogs will help you understand the significance of Hadoop Administration training:
http://www.edureka.co/blog/why-should-you-go-for-hadoop-administration-course/
http://www.edureka.co/blog/how-to-become-a-hadoop-administrator/
http://www.edureka.co/blog/hadoop-admin-responsibilities/
Bharath Mundlapudi presented on Disk Fail Inplace in Hadoop. He discussed how a single disk failure currently causes an entire node to be blacklisted. With newer hardware trends of more disks per node, this wastes significant resources. His team developed a Disk Fail Inplace approach where Hadoop can tolerate disk failures until a threshold. This included separating critical and user files, handling failures at startup and runtime in DataNode and TaskTracker, and rigorous testing of the new approach.
Introduction to Cloudera's Administrator Training for Apache HadoopCloudera, Inc.
The document provides an overview of Cloudera's Administrator Training course for Apache Hadoop. The training covers topics such as planning and deploying Hadoop clusters, installing and configuring Hadoop components like HDFS, Hive and Impala, using Cloudera Manager for administration, configuring advanced cluster options and HDFS high availability, and Hadoop security. The hands-on course includes exercises for deploying Hadoop clusters, importing data, and troubleshooting issues.
Learn to setup a Hadoop Multi Node ClusterEdureka!
This document provides an overview of key topics covered in Edureka's Hadoop Administration course, including Hadoop components and configurations, modes of a Hadoop cluster, setting up a multi-node cluster, and terminal commands. The course teaches students how to deploy, configure, manage, monitor, and secure an Apache Hadoop cluster over 24 hours of live online classes with assignments and a project.
Setting High Availability in Hadoop ClusterEdureka!
This document discusses achieving high availability in Hadoop clusters. It begins by introducing Hadoop and its core components like HDFS, YARN, and MapReduce. It then explains the single point of failure issue with the NameNode in Hadoop 1.x. Hadoop 2.0 introduced solutions like having an active and standby NameNode that log all filesystem edits to shared storage. ZooKeeper is used for failover detection and coordination. The document also discusses securing HDFS through access control lists and using Hadoop as a data warehouse with tools like Hive, Impala, and BI tools. Hands-on sections walk through setting up high availability for HDFS and YARN.
Improving Hadoop Cluster Performance via Linux ConfigurationAlex Moundalexis
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
A Day in the Life of a Hadoop AdministratorEdureka!
This document outlines the daily tasks of a Hadoop administrator, which include monitoring the cluster, planning maintenance tasks, executing regular utility tasks like backups and file merging, upgrading systems, assisting developers, and troubleshooting issues. It also provides demonstrations on achieving high availability in Hadoop and YARN clusters, and discusses tools for monitoring cluster resources, user permissions, and common error messages. The document promotes an online Hadoop administration certification course from Edureka that teaches skills for planning, deploying, monitoring, tuning and securing Hadoop clusters.
With the advent of Hadoop, there comes the need for professionals skilled in Hadoop Administration making it imperative to be skilled as a Hadoop Admin for better career, salary and job opportunities.
The document discusses data ingestion and storage in Hadoop. It covers topics like ingesting data into Hadoop, using Hadoop as a data warehouse, Pig scripting, using Flume to ingest Twitter and web server logs, Hive as a query layer, HBase as a NoSQL database, and setting up high availability for HBase. It also discusses differences between Hadoop 1.0 and 2.0, how to set up a Hadoop 2.0 cluster including configuration files, and demonstrates upgrading Hadoop.
This document provides an overview and instructions for using Hadoop including:
- Hadoop uses HDFS for distributed storage and divides files into 64MB chunks across data servers.
- The master node tracks the namespace and metadata while slave nodes store data blocks.
- Commands like start-all.sh and stop-all.sh are used to start and stop Hadoop across nodes.
- The hadoop dfs command is used to interact with files in HDFS using options like -ls, -put, -get. Configuration files allow customizing Hadoop.
This document provides an agenda and overview for a presentation on Hadoop 2.x configuration and MapReduce performance tuning. The presentation covers hardware selection and capacity planning for Hadoop clusters, key configuration parameters for operating systems, HDFS, and YARN, and performance tuning techniques for MapReduce applications. It also demonstrates the Hadoop Vaidya performance diagnostic tool.
This document provides guidance on sizing and configuring Apache Hadoop clusters. It recommends separating master nodes, which run processes like the NameNode and JobTracker, from slave nodes, which run DataNodes, TaskTrackers and RegionServers. For medium to large clusters it suggests 4 master nodes and the remaining nodes as slaves. The document outlines factors to consider for optimizing performance and cost like selecting balanced CPU, memory and disk configurations and using a "shared nothing" architecture with 1GbE or 10GbE networking. Redundancy is more important for master than slave nodes.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a programming model called MapReduce where developers write mapping and reducing functions that are automatically parallelized and executed on a large cluster. Hadoop also includes HDFS, a distributed file system that stores data across nodes providing high bandwidth. Major companies like Yahoo, Google and IBM use Hadoop to process large amounts of data from users and applications.
Hadoop Interview Questions and Answers by rohit kapakapa rohit
Hadoop Interview Questions and Answers - More than 130 real time questions and answers covering hadoop hdfs,mapreduce and administrative concepts by rohit kapa
This document outlines the course content for a Hadoop Administration course. It covers topics such as introducing Big Data concepts, understanding Hadoop and HDFS, the MapReduce framework, planning and maintaining Hadoop clusters, installing Hadoop ecosystem tools, managing jobs, monitoring clusters, troubleshooting issues, and populating HDFS from external sources. Contact [email protected] for inquiries about hadoop development, administration, testing, or advanced Hadoop topics.
Hadoop installation, Configuration, and Mapreduce programPraveen Kumar Donta
This presentation contains brief description about big data along with that hadoop installation, configuration and MapReduce wordcount program and its explanation.
This document provides an introduction and overview of installing Hadoop 2.7.2 in pseudo-distributed mode. It discusses the core components of Hadoop including HDFS for distributed storage and MapReduce for distributed processing. It also covers prerequisites like Java and SSH setup. The document then describes downloading and extracting Hadoop, configuring files, and starting services to run Hadoop in pseudo-distributed mode on a single node.
Hadoop Operations for Production Systems (Strata NYC)Kathleen Ting
Hadoop is emerging as the standard for big data processing and analytics. However, as usage of the Hadoop clusters grow, so do the demands of managing and monitoring these systems.
In this full-day Strata Hadoop World tutorial, attendees will get an overview of all phases for successfully managing Hadoop clusters, with an emphasis on production systems — from installation, to configuration management, service monitoring, troubleshooting and support integration.
We will review tooling capabilities and highlight the ones that have been most helpful to users, and share some of the lessons learned and best practices from users who depend on Hadoop as a business-critical system.
The document describes the key limitations of Hadoop 1.x including single point of failure of the NameNode, lack of horizontal scalability, and the JobTracker being overburdened. It then discusses how Hadoop 2.0 addresses these issues through features like HDFS federation for multiple NameNodes, NameNode high availability, and YARN which replaces MapReduce and allows sharing of cluster resources for various workloads.
This document provides an overview of a Hadoop administration course offered on the edureka.in website. It describes the course topics which include understanding big data, Hadoop components, Hadoop configuration, different server roles, and data processing flows. It also outlines how the course works, with live classes, recordings, quizzes, assignments, and certification. The document then provides more detail on specific topics like what is big data, limitations of existing solutions, how Hadoop solves these problems, and introductions to Hadoop, MapReduce, and the roles of a Hadoop cluster administrator.
Big data is generated from a variety of sources at a massive scale and high velocity. Hadoop is an open source framework that allows processing and analyzing large datasets across clusters of commodity hardware. It uses a distributed file system called HDFS that stores multiple replicas of data blocks across nodes for reliability. Hadoop also uses a MapReduce processing model where mappers process data in parallel across nodes before reducers consolidate the outputs into final results. An example demonstrates how Hadoop would count word frequencies in a large text file by mapping word counts across nodes before reducing the results.
Bharath Mundlapudi presented on Disk Fail Inplace in Hadoop. He discussed how a single disk failure currently causes an entire node to be blacklisted. With newer hardware trends of more disks per node, this wastes significant resources. His team developed a Disk Fail Inplace approach where Hadoop can tolerate disk failures until a threshold. This included separating critical and user files, handling failures at startup and runtime in DataNode and TaskTracker, and rigorous testing of the new approach.
Introduction to Cloudera's Administrator Training for Apache HadoopCloudera, Inc.
The document provides an overview of Cloudera's Administrator Training course for Apache Hadoop. The training covers topics such as planning and deploying Hadoop clusters, installing and configuring Hadoop components like HDFS, Hive and Impala, using Cloudera Manager for administration, configuring advanced cluster options and HDFS high availability, and Hadoop security. The hands-on course includes exercises for deploying Hadoop clusters, importing data, and troubleshooting issues.
Learn to setup a Hadoop Multi Node ClusterEdureka!
This document provides an overview of key topics covered in Edureka's Hadoop Administration course, including Hadoop components and configurations, modes of a Hadoop cluster, setting up a multi-node cluster, and terminal commands. The course teaches students how to deploy, configure, manage, monitor, and secure an Apache Hadoop cluster over 24 hours of live online classes with assignments and a project.
Setting High Availability in Hadoop ClusterEdureka!
This document discusses achieving high availability in Hadoop clusters. It begins by introducing Hadoop and its core components like HDFS, YARN, and MapReduce. It then explains the single point of failure issue with the NameNode in Hadoop 1.x. Hadoop 2.0 introduced solutions like having an active and standby NameNode that log all filesystem edits to shared storage. ZooKeeper is used for failover detection and coordination. The document also discusses securing HDFS through access control lists and using Hadoop as a data warehouse with tools like Hive, Impala, and BI tools. Hands-on sections walk through setting up high availability for HDFS and YARN.
Improving Hadoop Cluster Performance via Linux ConfigurationAlex Moundalexis
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
A Day in the Life of a Hadoop AdministratorEdureka!
This document outlines the daily tasks of a Hadoop administrator, which include monitoring the cluster, planning maintenance tasks, executing regular utility tasks like backups and file merging, upgrading systems, assisting developers, and troubleshooting issues. It also provides demonstrations on achieving high availability in Hadoop and YARN clusters, and discusses tools for monitoring cluster resources, user permissions, and common error messages. The document promotes an online Hadoop administration certification course from Edureka that teaches skills for planning, deploying, monitoring, tuning and securing Hadoop clusters.
With the advent of Hadoop, there comes the need for professionals skilled in Hadoop Administration making it imperative to be skilled as a Hadoop Admin for better career, salary and job opportunities.
The document discusses data ingestion and storage in Hadoop. It covers topics like ingesting data into Hadoop, using Hadoop as a data warehouse, Pig scripting, using Flume to ingest Twitter and web server logs, Hive as a query layer, HBase as a NoSQL database, and setting up high availability for HBase. It also discusses differences between Hadoop 1.0 and 2.0, how to set up a Hadoop 2.0 cluster including configuration files, and demonstrates upgrading Hadoop.
This document provides an overview and instructions for using Hadoop including:
- Hadoop uses HDFS for distributed storage and divides files into 64MB chunks across data servers.
- The master node tracks the namespace and metadata while slave nodes store data blocks.
- Commands like start-all.sh and stop-all.sh are used to start and stop Hadoop across nodes.
- The hadoop dfs command is used to interact with files in HDFS using options like -ls, -put, -get. Configuration files allow customizing Hadoop.
This document provides an agenda and overview for a presentation on Hadoop 2.x configuration and MapReduce performance tuning. The presentation covers hardware selection and capacity planning for Hadoop clusters, key configuration parameters for operating systems, HDFS, and YARN, and performance tuning techniques for MapReduce applications. It also demonstrates the Hadoop Vaidya performance diagnostic tool.
This document provides guidance on sizing and configuring Apache Hadoop clusters. It recommends separating master nodes, which run processes like the NameNode and JobTracker, from slave nodes, which run DataNodes, TaskTrackers and RegionServers. For medium to large clusters it suggests 4 master nodes and the remaining nodes as slaves. The document outlines factors to consider for optimizing performance and cost like selecting balanced CPU, memory and disk configurations and using a "shared nothing" architecture with 1GbE or 10GbE networking. Redundancy is more important for master than slave nodes.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a programming model called MapReduce where developers write mapping and reducing functions that are automatically parallelized and executed on a large cluster. Hadoop also includes HDFS, a distributed file system that stores data across nodes providing high bandwidth. Major companies like Yahoo, Google and IBM use Hadoop to process large amounts of data from users and applications.
Hadoop Interview Questions and Answers by rohit kapakapa rohit
Hadoop Interview Questions and Answers - More than 130 real time questions and answers covering hadoop hdfs,mapreduce and administrative concepts by rohit kapa
This document outlines the course content for a Hadoop Administration course. It covers topics such as introducing Big Data concepts, understanding Hadoop and HDFS, the MapReduce framework, planning and maintaining Hadoop clusters, installing Hadoop ecosystem tools, managing jobs, monitoring clusters, troubleshooting issues, and populating HDFS from external sources. Contact [email protected] for inquiries about hadoop development, administration, testing, or advanced Hadoop topics.
Hadoop installation, Configuration, and Mapreduce programPraveen Kumar Donta
This presentation contains brief description about big data along with that hadoop installation, configuration and MapReduce wordcount program and its explanation.
This document provides an introduction and overview of installing Hadoop 2.7.2 in pseudo-distributed mode. It discusses the core components of Hadoop including HDFS for distributed storage and MapReduce for distributed processing. It also covers prerequisites like Java and SSH setup. The document then describes downloading and extracting Hadoop, configuring files, and starting services to run Hadoop in pseudo-distributed mode on a single node.
Hadoop Operations for Production Systems (Strata NYC)Kathleen Ting
Hadoop is emerging as the standard for big data processing and analytics. However, as usage of the Hadoop clusters grow, so do the demands of managing and monitoring these systems.
In this full-day Strata Hadoop World tutorial, attendees will get an overview of all phases for successfully managing Hadoop clusters, with an emphasis on production systems — from installation, to configuration management, service monitoring, troubleshooting and support integration.
We will review tooling capabilities and highlight the ones that have been most helpful to users, and share some of the lessons learned and best practices from users who depend on Hadoop as a business-critical system.
The document describes the key limitations of Hadoop 1.x including single point of failure of the NameNode, lack of horizontal scalability, and the JobTracker being overburdened. It then discusses how Hadoop 2.0 addresses these issues through features like HDFS federation for multiple NameNodes, NameNode high availability, and YARN which replaces MapReduce and allows sharing of cluster resources for various workloads.
This document provides an overview of a Hadoop administration course offered on the edureka.in website. It describes the course topics which include understanding big data, Hadoop components, Hadoop configuration, different server roles, and data processing flows. It also outlines how the course works, with live classes, recordings, quizzes, assignments, and certification. The document then provides more detail on specific topics like what is big data, limitations of existing solutions, how Hadoop solves these problems, and introductions to Hadoop, MapReduce, and the roles of a Hadoop cluster administrator.
Big data is generated from a variety of sources at a massive scale and high velocity. Hadoop is an open source framework that allows processing and analyzing large datasets across clusters of commodity hardware. It uses a distributed file system called HDFS that stores multiple replicas of data blocks across nodes for reliability. Hadoop also uses a MapReduce processing model where mappers process data in parallel across nodes before reducers consolidate the outputs into final results. An example demonstrates how Hadoop would count word frequencies in a large text file by mapping word counts across nodes before reducing the results.
This document provides an introduction to Hadoop administration. It discusses key topics like understanding big data and Hadoop, Hadoop components, configuring and setting up a Hadoop cluster, commissioning and decommissioning data nodes, and includes demos of setting up a cluster and managing the secondary name node. The overall objectives are to help students understand Hadoop fundamentals, the responsibilities of an administrator, and how to manage a Hadoop cluster.
Presentation on Big Data Hadoop (Summer Training Demo)Ashok Royal
This document summarizes a practical training presentation on Big Data Hadoop. It was presented by Ashutosh Tiwari and Ashok Rayal from Poornima Institute of Engineering & Technology, Jaipur under the guidance of Dr. E.S. Pilli from MNIT Jaipur. The training took place from May 28th to July 9th 2014 at MNIT Jaipur and consisted of studying Hadoop and related papers, building a Hadoop cluster, and implementing a near duplicate detection project using Hadoop MapReduce. The near duplicate detection project aimed to comparatively analyze documents to find similar ones based on a predefined threshold. Snapshots of the HDFS, MapReduce processing, and output of the project are
This document discusses SQL Server 2012 FileTables, which allow files to be stored and managed directly within a SQL Server database. FileTables represent both options of storing files and metadata together in the database or separately across file systems and databases. FileTables provide full Windows file system access to files stored in SQL Server tables while retaining relational properties and queries. They enable seamless access to files from applications without changes to client code.
Hadoop Security in Big-Data-as-a-Service Deployments - Presented at Hadoop Su...Abhiraj Butala
The talk covers limitations of current Hadoop eco-system components in handling security (Authentication, Authorization, Auditing) in multi-tenant, multi-application environments. Then it proposes how we can use Apache Ranger and HDFS super-user connections to enforce correct HDFS authorization policies and achieve the required auditing.
This document outlines the agenda for a BigData Cloud Architects Meetup Group meeting on August 9, 2014 from 2:00-4:00pm. The agenda includes a discussion on securing Hadoop deployments to enterprise compliance regulations and a Q&A session. There will also be introductions, a topic presentation on the week's exciting topic, and a wrap-up. The meetup group was started in June 2013 and meets bi-weekly on Saturdays.
This document provides an overview of Hadoop and MapReduce concepts. It discusses:
- HDFS architecture with NameNode and DataNodes for metadata and data storage. HDFS provides reliability through block replication across nodes.
- MapReduce framework for distributed processing of large datasets across clusters. It consists of map and reduce phases with intermediate shuffling and sorting of data.
- Hadoop was developed based on Google's papers describing their distributed file system GFS and MapReduce processing model. It allows processing of data in parallel across large clusters of commodity hardware.
The document discusses fault tolerance in Apache Hadoop. It describes how Hadoop handles failures at different layers through replication and rapid recovery mechanisms. In HDFS, data nodes regularly heartbeat to the name node, and blocks are replicated across racks. The name node tracks block locations and initiates replication if a data node fails. HDFS also supports name node high availability. In MapReduce v1, task and task tracker failures cause re-execution of tasks. YARN improved fault tolerance by removing the job tracker single point of failure.
Apache Spark Introduction @ University College LondonVitthal Gogate
Spark is a fast and general engine for large-scale data processing. It uses resilient distributed datasets (RDDs) that can be operated on in parallel. Transformations on RDDs are lazy, while actions trigger their execution. Spark supports operations like map, filter, reduce, and join and can run on Hadoop clusters, standalone, or in cloud services like AWS.
Hadoop World 2011: Hadoop Troubleshooting 101 - Kate Ting - ClouderaCloudera, Inc.
Attend this session and walk away armed with solutions to the most common customer problems. Learn proactive configuration tweaks and best practices to keep your cluster free of fetch failures, job tracker hangs, and the like.
It introduces and illustrates use cases, benefits and problems for Kerberos deployment on Hadoop; how Token support and TokenPreauth can help solve the problems. It also briefly introduces Haox project, a Java client library for Kerberos.
Combining Machine Learning Frameworks with Apache SparkDatabricks
This document discusses combining machine learning frameworks with Apache Spark. It provides an overview of Apache Spark and MLlib, describes how to distribute TensorFlow computations using Spark, and discusses managing machine learning workflows with Spark through features like cross validation, persistence, and distributed data sources. The goal is to make machine learning easy, scalable, and integrate with existing workflows.
The document provides an overview of MongoDB administration including its data model, replication for high availability, sharding for scalability, deployment architectures, operations, security features, and resources for operations teams. The key topics covered are the flexible document data model, replication using replica sets for high availability, scaling out through sharding of data across multiple servers, and different deployment architectures including single/multi data center configurations.
The document contains screenshots and descriptions of the setup and configuration of a Hadoop cluster. It includes images showing the cluster with different numbers of live and dead nodes, replication settings across nodes, and outputs of commands like fsck and job execution information. The screenshots demonstrate how to view cluster health metrics, manage nodes, and run MapReduce jobs on the Hadoop cluster.
This document provides an overview of security topics related to Hadoop. It discusses what Hadoop is, common versions and distributions. It outlines some key security risks like default passwords, open ports, old versions with vulnerabilities. It also summarizes encryption options for data in motion and at rest, and security solutions like Knox and Ranger for centralized authorization policies.
Where is my next jobs in the age of Big Data and AutomationTrieu Nguyen
The document discusses how automation is impacting knowledge work jobs and proposes that the best approach is augmentation, where humans and machines work together. It provides examples of how different knowledge work jobs like teachers, lawyers, and financial advisors could take steps to augment their work with automation. The key steps include humans mastering automated systems, identifying new areas for automation, focusing on tasks they currently do best, finding niche roles, and building automated systems. The implications are that organizations should adopt an augmentation perspective, select the right technologies, design work for humans and machines, provide transition options for employees, and appoint a leader to manage workplace changes.
1) The document describes the steps to install a single node Hadoop cluster on a laptop or desktop.
2) It involves downloading and extracting required software like Hadoop, JDK, and configuring environment variables.
3) Key configuration files like core-site.xml, hdfs-site.xml and mapred-site.xml are edited to configure the HDFS, namenode and jobtracker.
4) The namenode is formatted and Hadoop daemons like datanode, secondary namenode and jobtracker are started.
This document discusses managing Hadoop clusters in a distribution-agnostic way using Bright Cluster Manager. It outlines the challenges of deploying and maintaining Hadoop, describes an architecture for a unified cluster and Hadoop manager, and highlights Bright Cluster Manager's key features for provisioning, configuring and monitoring Hadoop clusters across different distributions from a single interface. Bright provides a solution for setting up, managing and monitoring multi-purpose clusters running both HPC and Hadoop workloads.
The document discusses Hadoop infrastructure at TripAdvisor including:
1) TripAdvisor uses Hadoop across multiple clusters to analyze large amounts of data and power analytics jobs that were previously too large for a single machine.
2) They implement high availability for the Hadoop infrastructure including automatic failover of the NameNode using DRBD, Corosync and Pacemaker to replicate the NameNode across two servers.
3) Monitoring of the Hadoop clusters is done through Ganglia and Nagios to track hardware, jobs and identify issues. Regular backups of HDFS and Hive metadata are also performed for disaster recovery.
This document provides instructions for configuring a single node Hadoop deployment on Ubuntu. It describes installing Java, adding a dedicated Hadoop user, configuring SSH for key-based authentication, disabling IPv6, installing Hadoop, updating environment variables, and configuring Hadoop configuration files including core-site.xml, mapred-site.xml, and hdfs-site.xml. Key steps include setting JAVA_HOME, configuring HDFS directories and ports, and setting hadoop.tmp.dir to the local /app/hadoop/tmp directory.
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
Facing enterprise specific challenges – utility programming in hadoopfann wu
This document discusses managing large Hadoop clusters through various automation tools like SaltStack, Puppet, and Chef. It describes how to use SaltStack to remotely control and manage a Hadoop cluster. Puppet can be used to easily deploy Hadoop on hundreds of servers within an hour through Hadooppet. The document also covers Hadoop security concepts like Kerberos and folder permissions. It provides examples of monitoring tools like Ganglia, Nagios, and Splunk that can be used to track cluster metrics and debug issues. Common processes like datanode decommissioning and tools like the HBase Canary tool are also summarized. Lastly, it discusses testing Hadoop on AWS using EMR and techniques to reduce EMR costs
These slides provide highlights of my book HDInsight Essentials. Book link is here: http://www.packtpub.com/establish-a-big-data-solution-using-hdinsight/book
HdInsight essentials Hadoop on Microsoft Platformnvvrajesh
This book gives a quick introduction to Hadoop-like problems, and gives a primer on the real value of HDInsight. Next, it will show how to set up your HDInsight cluster.
Then, it will take you through the four stages: collect, process, analyze, and report.
For each of these stages you will see a practical example with the working code.
The document outlines the goals and contents of a book about HDInsight, Microsoft's Hadoop distribution. The book aims to provide an overview of Hadoop, describe how to deploy HDInsight on-premise and on Azure, and provide examples of ingesting, transforming, and analyzing data with HDInsight. Each chapter is summarized briefly, covering topics like Hadoop concepts, installing HDInsight, administering HDInsight clusters, loading and processing data in HDInsight.
This document provides an overview and introduction to Hadoop, HDFS, and MapReduce. It covers the basic concepts of HDFS, including how files are stored in blocks across data nodes, and the role of the name node and data nodes. It also explains the MapReduce programming model, including the mapper, reducer, and how jobs are split into parallel tasks. The document discusses using Hadoop from the command line and writing MapReduce jobs in Java. It also mentions some other projects in the Hadoop ecosystem like Pig, Hive, HBase and Zookeeper.
Why is everyone interested in Big Data and Hadoop?
Why you should use Hadoop?
Read this to and you as well you quickly and easily be the proud owner of a Hadoop kit of your own, using Cloudera Free Edition.
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This presentation is still being edited and new slides added every day. Stay tuned...
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This document discusses deploying and researching Hadoop in virtual machines. It provides definitions of Hadoop, MapReduce, and HDFS. It describes using CloudStack to deploy a Hadoop cluster across multiple virtual machines to enable distributed and parallel processing of large datasets. The proposed system is to deploy Hadoop applications on virtual machines from a CloudStack infrastructure for improved performance, reliability and reduced power consumption compared to a single virtual machine. It outlines the hardware, software, architecture, design, testing and outputs of the proposed system.
This document discusses deploying and researching Hadoop in virtual machines. It provides definitions of Hadoop, MapReduce, and HDFS. It describes using CloudStack to deploy a Hadoop cluster across multiple virtual machines to enable distributed and parallel processing of large datasets. The proposed system is to deploy Hadoop applications on virtual machines from a CloudStack infrastructure for improved performance, reliability and reduced power consumption compared to a single virtual machine. It outlines the hardware, software, architecture, design, testing and outputs of the proposed system.
This document provides an overview of a 30-hour training on Apache Hadoop administration. The training aims to give participants a comprehensive understanding of installing, configuring, operating and maintaining an Apache Hadoop cluster. Participants will learn how to install Hadoop clusters, configure components like HDFS, MapReduce and YARN, load and manage data, configure security and high availability, monitor performance, and troubleshoot issues. The course covers both theoretical concepts and hands-on exercises using tools like Cloudera and Hortonworks distributions, and includes topics like planning hardware, basic administration, advanced configuration, and managing related projects like Hive and Pig.
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Deployment and Management of Hadoop Clusters
1. Deployment and Management
of Hadoop Clusters
Amal G Jose
Big Data Analytics
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2. • Introduction
• Cluster design and deployment
• Backup and Recovery
• Hadoop Upgrade
• Routine Administration Tasks
Agenda
3. Introduction
• What is Hadoop ?
• What makes Hadoop different ?
• Need for a hadoop cluster ?
4. This has 4 parts:
• Cluster Planning.
• OS installation & Hardening.
• Cluster Software Installation.
• Cluster configuration.
Cluster Installation
5. Cluster Planning
Hadoop Daemon Configuration
Namenode Dedicated servers.
OS is installed on the RAID device.
The dfs.name.dir will reside on the
same RAID device. One more copy is
configured to have on NFS.
Secondary Namenode Dedicated Server
OS is installed on RAID device
Jobtracker Dedicated Server.
OS installed on JBOD configuration
Datanode/Tasktracker Individual servers.
OS installed on JBOD configuration
6. Workload Patterns For Hadoop
• Balanced Workload
• Compute Intensive
• I/O Intensive
• Unknown or evolving workload patterns
8. Name Node
Job Tracker
Ganglia-Daemon
Name Node
Job Tracker
Ganglia-Daemon
MN
Hive
Pig
Oozie
Mahout
Ganglia-Master
Hive
Pig
Oozie
Mahout
Ganglia-Master
CN
Typical Hadoop Cluster Topology
Task Tracker
Data Node
Ganglia-Daemon
Task Tracker
Data Node
Ganglia-Daemon
SN
9. • Creating the instances based on the
requirement
Creating Instances (in case of cloud)
10. • We will be installing the Hadoop on the RHEL6 64-
bit servers.
• OS should be hardened based on RHEL6
hardening document.
• Setting iptable rules necessary for hadoop
services.
• In case of Amazon EC2 instances create
key/value pairs for logging in.
• GUI can be disabled to make more room for
hadoop.
• Time should be made same in all the servers.
Operating System Hardening
11. • Choosing the distribution of Hadoop.
• Creation of Local Yum Repository.
• Java Installation in all the machines.
Cluster Software Installation
13. Installation Methods
• Hadoop can be installed either manually
or automatically using some tools such as
ClouderaManager, Ambari etc.
• One click installation tools helps the users
to install hadoop on clusters without any
pain.
14. Manual Installation
• Install hadoop daemons in the nodes.
• We can either use tarball or rpm for
installation.
• rpm installation will be easier.
15. Setting up Client Node
• What is client node ?
• Necessity of a client node ?
• How to configure a client node ?
• What all services are installed ?
• Need for multiuser segregation ?
16. Cluster Configuration
• Storage location for namenode,
secondarynamenode and datanode.
• Number of task slots (map/reduce slots).
– Number of task slots/node = (memory
available/child jvm size)
• Backup location for namenode.
• Configuring mysql for hive and oozie.
17. Namenode - Single point of
Failure
• Why namenode is the single point of
failure?
• How to resolve this issue?
• How backup can be achieved?
19. Monitoring Hadoop Cluster
• For manual installation, we can use
Ganglia.
• Automated installation tools have built-in
monitoring mechanisms available.
24. Steps for Hadoop Upgrade
• Make sure that any previous upgrade is finalized before proceeding
with another upgrade.
• Shut down MapReduce and kill any orphaned task processes on the
tasktrackers.
• Shut down HDFS and backup the namenode directories.
• Install new versions of Hadoop HDFS and MapReduce on the
cluster and on clients.
• Start HDFS with the -upgrade option.
• Wait until the upgrade is complete.
• Perform some sanity checks on HDFS.
• Start MapReduce.
• Roll back or finalize the upgrade (optional).
26. Summary
• Hadoop Cluster design
• Hadoop Cluster Installation
• Back up and Recovery
• Hadoop Upgrade
• Routine Administration Procedures
27. For more info, visit:
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Additional Information