...Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures (of individual machines, or racks of machines) are commonplace and thus should be automatically handled in software by the framework.[3] The core of Apache Hadoop consists of a storage part (Hadoop Distributed File System (HDFS)) and a processing part (MapReduce). Hadoop splits files into large blocks and distributes them amongst the nodes in the cluster. To process the data, Hadoop MapReduce transfers packaged code for nodes to process in parallel, based on the data each node needs to process. This approach takes advantage of data locality[4]—nodes manipulating the data that they have on hand—to allow the data to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are connected via high-speed networking.[5] The base Apache Hadoop framework is composed of the following modules: Hadoop Common – contains libraries and utilities needed by other Hadoop modules; Hadoop Distributed File System (HDFS) – a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster; Hadoop YARN – a resource-management platform responsible...
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...www.linuxidc.com Hadoop入门实战手册 更多Hadoop相关信息见Hadoop 专题页面 http://www.linuxidc.com/topicnews.aspx?tid=13 北京宽连十方数字技术有限公司 技术研究部 （2011年7月） Linux¹«Éç(LinuxIDC.com) ÊÇ°üÀ¨Ubuntu,Fedora,SUSE¼¼Êõ£¬×îÐÂIT×ÊѶµÈLinuxרҵÀàÍøÕ¾¡£ www.linuxidc.com 目录 1 概述 ........................................................................................................................... 4 1.1 什么是Hadoop？ .................................................................................................. 4 1.2 为什么要选择Hadoop？ ....................................................................................... 4 1.2.1 系统特点 ........................................................................................................ 4 1.2.2 使用场景 ........................................................................................................ 5 2 术语 ........................................................................................................................... 5 3 Hadoop的单机部署 .................................................................................................... 6 3.1 目的 ..................................................................................................................... 6 3.2 先决条件 .............................................................................................................. 6 3.2.1 支持平台 ........................................................................................................ 6 3.2.2 所需软件 .........
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...Hadoop Cluster Setup Hadoop is a framework written in Java for running applications on large clusters of commodity hardware and incorporates features similar to those of the Google File System (GFS) and of the MapReduce computing paradigm. Hadoop’s HDFS is a highly fault-tolerant distributed file system and, like Hadoop in general, designed to be deployed on low-cost hardware. This document describes how to install, configure and manage non-trivial Hadoop clusters ranging from a few nodes to extremely large clusters with thousands of nodes. Required Software Required software for Linux and Windows include: 1. Java 1.6.x, preferably from Sun, must be installed. 2. ssh must be installed and sshd must be running to use the Hadoop scripts that manage remote Hadoop daemons. Installation Installing a Hadoop cluster typically involves unpacking the software on all the machines in the cluster. Typically one machine in the cluster is designated as the NameNode and another machine the as JobTracker, exclusively. These are the masters. The rest of the machines in the cluster act as both DataNode and TaskTracker. These are the slaves. The root of the distribution is referred to as HADOOP_HOME. All machines in the cluster usually have the same HADOOP_HOME path. Steps for Installation 1. Install java 1.6 Check java version: $ java –version 2. Adding dedicated user group $ sudo addgroup hadoop $ sudo adduser --ingroup hadoop hduser 3. Install ssh $ su - hduser Generate...
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...Hadoop Distribution Comparison Tiange Chen The three kinds of Hadoop distributions that will be discussed today are: Apache Hadoop, MapR, and Cloudera. All of them have the same goals of performance, scalability, reliability, and availability. Furthermore, all of them have advantages including massive storage, great computing power, flexibility (Store and process data whenever you want, instead of preprocess before storing data like traditional relational databases. And it enables users to easily access new data sources including social media, email conversations, etc..), fault tolerance (One node fails, jobs still works on other nodes because data is replicated to other nodes in the beginning, so the computing does not fail), low cost (Use commodity hardware to store data), and scalability (More nodes, more storage, and little administration.). Apache Hadoop is the standard Hadoop distribution. It is open source project, created and maintained by developers from all around the world. Public access allows many people to test it, and problems can be noticed and fixed quickly, so their quality is reliable and satisfied. (Moccio, Grim, 2012) The core components are Hadoop Distribution File System (HDFS) as storage part and MapReduce as processing part. HDFS is a simple and robust coherency model. It is able to store large amount of information and provides steaming read performance. However, it is not strong enough in the aspect of easy management and seamless integration...
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...CASE STUDY OF HIVE USING HADOOP 1 Sai Prasad Potharaju, 2 Shanmuk Srinivas A, 3 Ravi Kumar Tirandasu 1,2,3 SRES COE,Department of Computer Engineering , Kopargaon,Maharashtra, India 1 psaiprasadcse@gmail.com Abstract Hadoop is a framework of tools. It is not a software that you can download on your computer. These tools are used to running applications on big data which has huge in capacity,need to process quickly and can be in variety forms. To manage the big data HIVE used as a data warehouse system for Hadoop that facilitates ad-hoc queries and the analysis of large datasets stored in Hadoop .Hive provides a SQL-LIKE languages called HIVEQL. In this paper we explains how to use hive using Hadoop with a simple real time example and also explained how to create a table,load the data into table from external file ,retrieve the data from table and their different statistics like CPU time for each stage of query execution ,cumulative CPU time and time taken to fetch records. Key Words:Hadoop,Hive,MapReduce,HDFS,HIVEQL 1. 1.1. INTRODUCTION Hadoop Hadoop is a open source and is distributed under Apache license. It is a framework of tools and not a software that you can download. These tools are used to running applications on big data .Big data means data with respective to its volume, speed, variety forms(Unstructured).In traditional approach big data is processed by using powerful computer but this computer will do good job until some...
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...De-Identified Personal Health Care System Using Hadoop The use of medical Big Data is increasingly popular in health care services and clinical research. The biggest challenges in health care centers are the huge amount of data flows into the systems daily. Crunching this BigData and de-identifying it in a traditional data mining tools had problems. Therefore to provide solution to the de-identifying personal health information, Map Reduce application uses jar files which contain a combination of MR code and PIG queries. This application also uses advanced mechanism of using UDF (User Data File) which is used to protect the health care dataset. Responsibilities: Moved all personal health care data from database to HDFS for further processing. Developed the Sqoop scripts in order to make the interaction between Hive and MySQL Database Wrote MapReduce code for DE-Identifying data. Loaded the processed results into Hive tables. Generated test cases using MRunit. Best-Buy – Rehosting of Web Intelligence project The purpose of the project is to store terabytes of log information generated by the ecommerce website and extract meaning information out of it. The solution is based on the open source Big Data s/w Hadoop .The data will be stored in Hadoop file system and processed using PIG scripts. Which intern includes getting the raw html data from the websites, Process the html to obtain product and pricing information, Extract various reports out of the product pricing...
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...REVOLUTION ANALYTICS WHITE PAPER Advanced ‘Big Data’ Analytics with R and Hadoop 'Big Data' Analytics as a Competitive Advantage Big Analytics delivers competitive advantage in two ways compared to the traditional analytical model. First, Big Analytics describes the efficient use of a simple model applied to volumes of data that would be too large for the traditional analytical environment. Research suggests that a simple algorithm with a large volume of data is more accurate than a sophisticated algorithm with little data. The algorithm is not the competitive advantage; the ability to apply it to huge amounts of data—without compromising performance—generates the competitive edge. Second, Big Analytics refers to the sophistication of the model itself. Increasingly, analysis algorithms are provided directly by database management system (DBMS) vendors. To pull away from the pack, companies must go well beyond what is provided and innovate by using newer, more sophisticated statistical analysis. Revolution Analytics addresses both of these opportunities in Big Analytics while supporting the following objectives for working with Big Data Analytics: 1. 2. 3. 4. Avoid sampling / aggregation; Reduce data movement and replication; Bring the analytics as close as possible to the data and; Optimize computation speed. First, Revolution Analytics delivers optimized statistical algorithms for the three primary data management paradigms being employed to address...
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...(SLAs) for internal customers using big data analytics services ● Support multiple internal users on same platform SOLUTION ● Implemented enterprise Hadoop platform on Cisco UCS CPA for Big Data - a complete infrastructure solution including compute, storage, connectivity and unified management ● Automated job scheduling and process orchestration using Cisco Tidal Enterprise Scheduler as alternative to Oozie RESULTS ● Analyzed service sales opportunities in one-tenth the time, at one-tenth the cost ● $40 million in incremental service bookings in the current fiscal year as a result of this initiative ● Implemented a multi-tenant enterprise platform while delivering immediate business value LESSONS LEARNED ● Cisco UCS can reduce complexity, improves agility, and radically improves cost of ownership for Hadoop based applications ● Library of Hive and Pig user-defined functions (UDF) increases developer productivity. ● Cisco TES simplifies job scheduling and process orchestration ● Build internal Hadoop skills ● Educate internal users about opportunities to use big data analytics to improve data processing and decision making NEXT STEPS ● Enable NoSQL Database and advanced analytics capabilities on the same platform. ● Adoption of the platform across different business functions. Enterprise Hadoop architecture, built on Cisco UCS Common Platform Architecture (CPA) for Big Data, unlocks hidden business intelligence. Challenge Cisco is the worldwide...
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...Apache Hadoop YARN: Yet Another Resource Negotiator Vinod Kumar Vavilapallih Mahadev Konarh Siddharth Sethh h: Arun C Murthyh Carlo Curinom Chris Douglasm Jason Lowey Owen O’Malleyh f: Sharad Agarwali Hitesh Shahh Sanjay Radiah facebook.com Robert Evansy Bikas Sahah m: Thomas Gravesy Benjamin Reed f hortonworks.com, Eric Baldeschwielerh microsoft.com, i : inmobi.com, y : yahoo-inc.com, Abstract The initial design of Apache Hadoop [1] was tightly focused on running massive, MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has become the data and computational agor´ —the de a facto place where data and computational resources are shared and accessed. This broad adoption and ubiquitous usage has stretched the initial design well beyond its intended target, exposing two key shortcomings: 1) tight coupling of a specific programming model with the resource management infrastructure, forcing developers to abuse the MapReduce programming model, and 2) centralized handling of jobs’ control flow, which resulted in endless scalability concerns for the scheduler. In this paper, we summarize the design, development, and current state of deployment of the next generation of Hadoop’s compute platform: YARN. The new architecture we introduced decouples the programming model from the resource management infrastructure, and delegates many scheduling functions (e.g., task faulttolerance) to per-application components. We provide experimental...
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...from good decisions and identify new opportunities to gain a competitive advantage. Hadoop It is open source software designed to provide massive storage and large data processing power. Hadoop has the ability to handle tasks running at the same time. Hadoop has a storage and processing part. It works by dividing files into large blocks and distributing them amongst the nodes (Kozielski & Wrembel, 2014). In processing, it works with MapReduce to ensure that codes are transferred and nodes are processed in parallel. By using nodes, Hadoop allows data manipulation making it is process faster and more efficiently. It has four main components: The Hadoop Common which contains utilities required, the Hadoop Distributed File System which is the storage part, Hadoop Yarn which manages and computes resources and Hadoop MapReduce which is a program responsible for processing large-scale data. It can process large amounts of data quickly by using multiple computers (Kozielski & Wrembel, 2014). Hadoop is being turned into a data processing operating system by large organizations. This is because it allows numerous data manipulations and analytical processes. Other data analysis programs such as SQL run on Hadoop and perform well on this system. The ability of Hadoop running many programs lowers cost of data analysis and allows businesses to analyze different amounts of data on products and consumers. Hadoop not only provides an organization with more data to work...
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...Big Data is Scaling BI and Analytics How the information surge is changing the way organizations use business intelligence and analytics Information Management Magazine, Sept/Oct 2011 Shawn Rogers Like what you see? Click here to sign up for Information Management's daily newsletter to get the latest news, trends, commentary and more. The explosive growth in the amount of data created in the world continues to accelerate and surprise us in terms of sheer volume, though experts could see the signposts along the way. Gordon Moore, co-founder of Intel and the namesake of Moore's law, first forecast that the number of transistors that could be placed on an integrated circuit would double year over year. Since 1965, this "doubling principle" has been applied to many areas of computing and has more often than not been proven correct. When applied to data, not even Moore's law seems to keep pace with the exponential growth of the past several years. Recent IDC research on digital data indicates that in 2010, the amount of digital information in the world reached beyond a zettabyte in size. That's one trillion gigabytes of information. To put that in perspective, a blogger at Cisco Systems noted that a zettabyte is roughly the size of 125 billion 8GB iPods fully loaded. Advertisement As the overall digital universe has expanded, so has the world of enterprise data. The good news for data management professionals is that our working data won't reach zettabyte scale for some...
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...Big Data Big Data and Business Strategy Businesses have come a long way in the way that information is being given to management, from comparing quarter sales all the way down to view how customers interact with the business. With so many new technology’s and new systems emerging, it has now become faster and easier to get any type of information, instead of using, for example, your sales processing system that might not get all the information that a manger might need. This is where big data comes into place with how it interacts with businesses. We can begin with how to explain what big data is and how it is used. Big data is a term used to describe the exponential growth and availability of data for both unstructured and structured systems. Back in 2001, Doug Laney (Gartner) gave a definition that ties in more closely on how big data is managed with a business strategy, which is given as velocity, volume, and variety. Velocity which is explained as how dig data is constantly and rapidly changing within time and how fast companies are able to keep up with in a real time manner. Which sometimes is a challenge to most companies. Volume is increasing also at a high level, especially with the amount of unstructured data streaming from social media such as Facebook. Also including the amount of data being collected from customer information. The final one is variety, which is what some companies also struggle with in handling many varieties of structured and unstructured data...
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...Big Data and Hadoop Harshawardhan S. Bhosale1, Prof. Devendra P. Gadekar2 1 Department of Computer Engineering, JSPM’s Imperial College of Engineering & Research, Wagholi, Pune Bhosale.harshawardhan186@gmail.com 2 Department of Computer Engineering, JSPM’s Imperial College of Engineering & Research, Wagholi, Pune devendraagadekar84@gmail.com Abstract: The term ‘Big Data’ describes innovative techniques and technologies to capture, store, distribute, manage and analyze petabyte- or larger-sized datasets with high-velocity and different structures. Big data can be structured, unstructured or semi-structured, resulting in incapability of conventional data management methods. Data is generated from various different sources and can arrive in the system at various rates. In order to process these large amounts of data in an inexpensive and efficient way, parallelism is used. Big Data is a data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it. Hadoop is the core platform for structuring Big Data, and solves the problem of making it useful for analytics purposes. Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance. Keywords -Big Data, Hadoop, Map Reduce...
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...In this tutorial, the required steps has been described for setting up a pseudo-distributed, single-node Hadoop cluster backed by the Hadoop Distributed File System, running on Ubuntu Linux. Installing Python $ sudo apt-get install python-software-properties $ sudo add-apt-repository ppa:ferramroberto/java Update the source list $ sudo apt-get update Install Sun Java 6 JDK $ sudo apt-get install sun-java6-jdk Select Sun's Java as the default on your machine. (See 'sudo update-alternatives --config java' for more information.) $ sudo update-java-alternatives -s java-6-sun The full JDK which will be placed in /usr/lib/jvm/java-6-sun (well, this directory is actually a symlink on Ubuntu). After installation, make a quick check whether Sun’s JDK is correctly set up: $ java –version java version "1.6.0_20" Java(TM) SE Runtime Environment (build 1.6.0_20-b02) Java HotSpot(TM) Client VM (build 16.3-b01, mixed mode, sharing) Adding a dedicated Hadoop system user $ sudo addgroup hadoop $ sudo adduser --ingroup hadoop hduser This will add the user hduser and the group hadoop to your local machine. Configuring SSH user@ubuntu:~$ su – hduser hduser@ubuntu:~$ ssh-keygen -t rsa -P "" Generating public/private rsa key pair. Enter file in which to save the key (/home/hduser/.ssh/id_rsa): Created directory '/home/hduser/.ssh'. Your identification has been saved in /home/hduser/.ssh/id_rsa. Your public key has been saved in /home/hduser/.ssh/id_rsa...
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...White Paper Big Data Analytics Extract, Transform, and Load Big Data with Apache Hadoop* ABSTRACT Over the last few years, organizations across public and private sectors have made a strategic decision to turn big data into competitive advantage. The challenge of extracting value from big data is similar in many ways to the age-old problem of distilling business intelligence from transactional data. At the heart of this challenge is the process used to extract data from multiple sources, transform it to fit your analytical needs, and load it into a data warehouse for subsequent analysis, a process known as “Extract, Transform & Load” (ETL). The nature of big data requires that the infrastructure for this process can scale cost-effectively. Apache Hadoop* has emerged as the de facto standard for managing big data. This whitepaper examines some of the platform hardware and software considerations in using Hadoop for ETL. – e plan to publish other white papers that show how a platform based on Apache Hadoop can be extended to W support interactive queries and real-time predictive analytics. When complete, these white papers will be available at http://hadoop.intel.com. Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 The ETL Bottleneck in Big Data Analytics The ETL Bottleneck in Big Data Analytics. . . . . . . . . . . . . . . . . . . . . . 1 Big Data refers to the large amounts, at least terabytes, of poly-structured...
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