Essential Guide

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Complete guide to Hadoop technology and storage

Use this guide to get a grasp on Hadoop technology basics, some of its benefits and drawbacks, and what it means for big data and cloud storage.

Hadoop technology has been discussed hand in hand with big data for some time now, but IT professionals still don't know the full extent of what the technology can do or how to use it.

The open source Hadoop framework is based on Google's MapReduce software and can process large data sets at a granular level. It offers analytics at a low cost and high speed that some analysts say can't be achieved any other way. Essential to the effectiveness of Hadoop is the Hadoop Distributed File System (HDFS), which allows parallel processing by spanning data over different nodes in a single cluster and provides fault tolerance.

However, HDFS is the source of one of the main issues users see with Hadoop technology: expanded capacity requirements due to Hadoop storing three copies of each piece of data in case a DataNode fails or is taken offline. That failover setup is necessary because each NameNode that controls the copy and distribution process of data is a single point of failure. Other complaints point to the complicated technology stemming from Hadoop's Java framework.

Despite the hurdles with Hadoop technology, analysts and users say the benefits are worth it. To help you determine that for yourself, this guide will walk you through the basics of what Hadoop technology can achieve, lay out the main concerns about the technology, and outline how it works with storage and the cloud.

Getting started

1. Understanding the basics of Hadoop technology

Hadoop technology is not a single entity -- it consists of different open source products such as HDFS and MapReduce. While Hadoop software is free, some vendors also offer their own Hadoop distributions with support and maintenance add-ons. To find out how all the components work and what they can do, take a look at the links below.

Issues

2. Dealing with Hadoop pain points

Despite its popularity, criticism of Hadoop ranges from the requirement for a specialized skill set to several single points of failure in the Hadoop cluster. In the following links, you'll find explanations of these and other Hadoop issues, and learn how to confront them.

Storage

3. Understanding Hadoop technology and storage

Because Hadoop stores three copies of each piece of data, storage in a Hadoop cluster must be able to accommodate a large number of files. To support the Hadoop architecture, traditional storage systems may not always work. The links below explain how Hadoop clusters and HDFS work with various storage systems, including network-attached storage (NAS), SANs and object storage.

Cloud considerations

4. How Hadoop technology works with the cloud

Hadoop can be useful for analytics across cloud storage because of its parallel-processing capability. Because Hadoop can process data across many servers, large amounts of data stored in the cloud can be searched and analyzed at high speeds. From the links below, you'll learn how using Hadoop in the cloud works and how cloud storage can help address some common Hadoop problems.

Video

5. Experts discuss Hadoop technology

Now that you have a better understanding of how Hadoop technology works with big data, watch the videos below to get experts' takes on how well Hadoop works and the best ways to use it.