Executive summary yahoo hadoop and green

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Executive summary yahoo hadoop and green

This is the full report "Hadoop: This report is written with the enterprise decision maker in mind. The goal is to give decision makers a crash course on what Hadoop is and why it is important.

Hadoop technology can be daunting at first and it represents a major shift from traditional enterprise data warehousing and data analytics. Within these pages is an overview that covers just enough to allow you to make intelligent decisions about Hadoop in your enterprise.

It has given birth to dozens of successful startups and many companies have well documented Hadoop success stories. An analogy to this is when someone tells you they are using Linux as their operating system: Core Apache Hadoop Core Hadoop is a software platform and framework for distributed computing of data.

Hadoop is a platform in the sense that it is a long-running system that runs and Executive summary yahoo hadoop and green computing tasks. Hadoop is a framework in the sense that it provides a layer of abstraction to developers of data applications and data analytics that hides a lot of the intricacies of the system.

The core Apache Hadoop project is organized into three major components that provide a foundation for the rest of the ecosystem: However, Hadoop allows you to both store lots of data and process lots of data with YARN and MapReduce, which is in stark contrast to traditional storage that just stores data e.

The Hadoop Ecosystem The Hadoop ecosystem is a collection of tools and systems that run alongside of or on top of Hadoop. Running "alongside" Hadoop means the tool or system has a purpose outside of Hadoop, but Hadoop users can leverage it. Nobody maintains an official ecosystem list, and the ecosystem is constantly changing with new tools being adopted and old tools falling out of favor.

There are several Hadoop "distributions" like there are Linux distributions that bundle up core technologies into one supportable platform. Each vendor provides different tools and services with their distributions, and the right vendor for your company depends on your particular use case and other needs.

Hadoop red sits at the middle as the "kernel" of the Hadoop ecosystem green. The various components that make up the ecosystem all run on a cluster of servers blue. Hadoop Masks Being a Distributed System Hadoop is a distributed system, which means it coordinates the usage of a cluster of multiple computational resources referred to as servers, computers, or nodes that communicate over a network.

Distributed systems empower users to solve problems that cannot be solved by a single computer. A distributed system can store more data than can be stored on just one machine and process data much faster than a single machine can.

However, this comes at the cost of increased complexity, because the computers in the cluster need to talk to one another, and the system needs to handle the increased chance of failure inherent in using more machines.

These are some of the tradeoffs of using a distributed system.

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This makes the life of the user a whole lot easier because he or she can focus on analyzing data instead of manually coordinating different computers or manually planning for failures.

There is a point to this, I promise. Hadoop hides the nasty details of distributed computing from users by providing a unified abstracted API on top of the distributed system underneath Example An example MapReduce job written in Java to count words This code is for word counting, the canonical example for MapReduce.

MapReduce can do all sorts of fancy things, but in this relatively simple case it takes a body of text, and it will return the list of words seen in the text along with how many times each of those words was seen.

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Nowhere in the code is there mention of the size of the cluster or how much data is being analyzed. MapReduce code works the same and looks the same regardless of cluster size This makes the code incredibly portable, which means a developer can test the MapReduce job on their workstation with a sample of data before shipping it off to the larger cluster.

Executive summary yahoo hadoop and green

No modifications to the code need to be made if the nature or size of the cluster changes later down the road. Also, this abstracts away all of the complexities of a distributed system for the developer, which makes his or her life easier in several ways: D in computer science becomes optional I joke The accessibility of Hadoop to the average software developer in comparison to previous distributed computing frameworks is one of the main reasons why Hadoop has taken off in terms of popularity.

Enter Barnardo and Francisco two Centinels. Just as there are abstractions for writing code for MapReduce jobs, there are abstractions when writing commands to interact with HDFS—mainly that nowhere in HDFS commands is there information about how or where data is stored.

When a user submits a Hadoop HDFS command, there are a lot of things that happen behind the scenes that the user is not aware of.

All the user sees is the results of the command without realizing that sometimes dozens of network communications needed to happen to retrieve the result. Behind the scenes, HDFS is taking each of these files, splitting them up into multiple blocks, distributing the blocks over several computers, replicating each block three times, and registering where they all are.

There could have been a catastrophic failure in which an entire rack of computers shut down in the middle of a series of commands and the commands still would have been completed without the user noticing and without any data loss.This is what a successful digital transformation looks like, based on research into the characteristics of enterprises that have succeeded with transformations in real life.

leslutinsduphoenix.com: News analysis, commentary, and research for business technology professionals. The OntologySummit is an annual series of events (first started by Ontolog and NIST in ) that involves the ontology community and communities related to each year's theme chosen for the summit.

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