- published: 14 Jul 2012
- views: 280944
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 are common and should be automatically handled by the framework.
The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part called MapReduce. Hadoop splits files into large blocks and distributes them across nodes in a cluster. To process data, Hadoop transfers packaged code for nodes to process in parallel based on the data that needs to be processed. This approach takes advantage of data locality— nodes manipulating the data they have access to— to allow the dataset 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 distributed via high-speed networking.
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy. The term often refers simply to the use of predictive analytics or certain other advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk.
Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on." Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,connectomics, complex physics simulations, biology and environmental research.
Data (/ˈdeɪtə/ DAY-tə, /ˈdætə/ DA-tə, or /ˈdɑːtə/ DAH-tə) is a set of values of qualitative or quantitative variables; restated, pieces of data are individual pieces of information. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs or images. Data as a general concept refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing.
Raw data, i.e. unprocessed data, is a collection of numbers, characters; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data is raw data that is collected in an uncontrolled in situ environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording.
The Latin word "data" is the plural of "datum", and still may be used as a plural noun in this sense. Nowadays, though, "data" is most commonly used in the singular, as a mass noun (like "information", "sand" or "rain").
Big means large or of great size.
Big or BIG may also refer to:
In computing, a file system (or filesystem) is used to control how data is stored and retrieved. Without a file system, information placed in a storage area would be one large body of data with no way to tell where one piece of information stops and the next begins. By separating the data into individual pieces, and giving each piece a name, the information is easily separated and identified. Taking its name from the way paper-based information systems are named, each group of data is called a "file". The structure and logic rules used to manage the groups of information and their names is called a "file system".
There are many different kinds of file systems. Each one has different structure and logic, properties of speed, flexibility, security, size and more. Some file systems have been designed to be used for specific applications. For example, the ISO 9660 file system is designed specifically for optical discs.
File systems can be used on many different kinds of storage devices. Each storage device uses a different kind of media. The most common storage device in use today is a hard drive whose media is a disc that has been coated with a magnetic film. The film has ones and zeros 'written' on it sending electrical pulses to a magnetic "read-write" head. Other media that are used are magnetic tape, optical disc, and flash memory. In some cases, such as with tmpfs, the computer's main memory (RAM) is used to create a temporary file system for short-term use.
-For a deeper dive, check our our video comparing Hadoop to SQL http://www.youtube.com/watch?v=3Wmdy80QOvw&feature;=c4-overview&list;=UUrR22MmDd5-cKP2jTVKpBcQ -Or see our video outlining critical Hadoop Scalability fundamentals https://www.youtube.com/watch?v=h5vAj9FPl0I To Talk with a Specialist go to: http://www.intricity.com/intricity101/
The availability of large data sets presents new opportunities and challenges to organizations of all sizes. So what is Big Data? How can Hadoop help me solve problems in processing large, complex data sets? Please go to http://www.LearningTree.com/WhatIsBigData to learn more about Big Data & our Big Data training offerings. In this video expert instructor Bill Appelbe will explain what Hadoop is, actual examples of how it works and how it compares to traditional databases such as Oracle & SQL Server. And finally, what is included in the Hadoop ecosystem.
http://zerotoprotraining.com This video explains what is Apache Hadoop. You will get a brief overview on Hadoop. Subsequent videos explain the details.
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This video points out three things that make Hadoop different from SQL. While a great many differences exist, this hopefully provides a little more context to bring mere mortals up to speed. There are some details about Hadoop that I purposely left out to simplify this video. http://www.intricity.com To Talk with a Specialist go to: http://www.intricity.com/intricity101/
To register for Hadoop, please visit our website ravindrababuravula.com
http://goo.gl/QA2KaQ Click on the link to watch the updated version of this video - http://www.youtube.com/watch?v=d0coIjRJ2qQ This is Part 1 of 8 week Big Data and Hadoop course. The 3hr Interactive live class covers What is Big Data, What is Hadoop and Why Hadoop? We also understand the details of Hadoop Distributed File System ( HDFS). The Tutorial covers in detail about Name Node, Data Nodes, Secondary Name Node, the need for Hadoop. It goes into the details of concepts like Rack Awareness, Data Replication, Reading and Writing on HDFS. We will also show how to setup the cloudera VM on your machine. More details below: Welcome, Let's Get Going on our Hadoop Journey... - - - - - - - - - - - - - - How it Works? 1. This is a 8 Week Instructor led Online Course. 2. We have a 3-hour ...
This video will walk beginners through the basics of Hadoop – from the early stages of the client-server model through to the current Hadoop ecosystem.
Техносфера Mail.ru Group, МГУ им. М.В. Ломоносова. Курс "Методы распределенной обработки больших объемов данных в Hadoop" Лекция №1 "Введение в Big Data и MapReduce" Лектор - Алексей Романенко. Что такое «большие данные». История возникновения этого явления. Необходимые знания и навыки для работы с большими данными. Что такое Hadoop, и где он применяется. Что такое «облачные вычисления», история возникновения и развития технологии. Web 2.0. Вычисление как услуга (utility computing). Виртуализация. Инфраструктура как сервис (IaaS). Вопросы параллелизма. Управление множеством воркеров. Дата-центры и масштабируемость. Типичные задачи Big Data. MapReduce: что это такое, примеры. Распределённая файловая система. Google File System. HDFS как клон GFS, его архитектура. Слайды лекции http://...
Este curso mostra conceitos de Big Data e os fundamentos do Apache Hadoop. Este primeiro módulo começa com a teoria necessária para entender a utilização de uma plataforma Big Data corporativa e em seguida é visto o que cada ferramenta faz dentro do ecossistema Hadoop. Vamos mostrar a arquitetura Hadoop, com sua base HDFS e Map Reduce e nas aulas práticas será explicado como instalar, importar dados para o Hive/HBase, escrever programas Map Reduce e controlar jobs usando Oozie. http://cursos.escolalinux.com.br
Got back out, back off the forefront
i never said, or got to say bye to my boy, but
its often i try
i think about how id be screaming
and the times would be bumping
all our minds would be flowing
taking care of shit like, hey holmes what you needing
as lifes coming off whack it will open your eyes
As i proceed to get loose
You seem to have some doubt
i feel you next to me fiending getting spacey
with the common love of music
think of this as the sun and the mind as a tool
but we could bounce back from this one with attitude will and some spirit
with attitude will and your spirit we'll shove it aside
soulfly
fly high
soulfly
fly free
Shut your shit, please say what you will.
I can't think. Sidestep around
I'm bound to the freestyle.
Push down to the ground.
With a nova dash but they watch you.
Now climb up, super slide,
the spirits so low it's coming over you!!!
soulfly
fly high
soulfly
fly free
when you walk in to this world
walk in to this world, with your head up high