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Professional Hadoop von Antony, Benoy (eBook)

  • Erscheinungsdatum: 03.05.2016
  • Verlag: Wrox
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Professional Hadoop

The professional's one-stop guide to this open-source, Java-based big data framework Professional Hadoop is the complete reference and resource for experienced developers looking to employ Apache Hadoop in real-world settings. Written by an expert team of certified Hadoop developers, committers, and Summit speakers, this book details every key aspect of Hadoop technology to enable optimal processing of large data sets. Designed expressly for the professional developer, this book skips over the basics of database development to get you acquainted with the framework's processes and capabilities right away. The discussion covers each key Hadoop component individually, culminating in a sample application that brings all of the pieces together to illustrate the cooperation and interplay that make Hadoop a major big data solution. Coverage includes everything from storage and security to computing and user experience, with expert guidance on integrating other software and more. Hadoop is quickly reaching significant market usage, and more and more developers are being called upon to develop big data solutions using the Hadoop framework. This book covers the process from beginning to end, providing a crash course for professionals needing to learn and apply Hadoop quickly. Configure storage, UE, and in-memory computing Integrate Hadoop with other programs including Kafka and Storm Master the fundamentals of Apache Big Top and Ignite Build robust data security with expert tips and advice
Hadoop's popularity is largely due to its accessibility. Open-source and written in Java, the framework offers almost no barrier to entry for experienced database developers already familiar with the skills and requirements real-world programming entails. Professional Hadoop gives you the practical information and framework-specific skills you need quickly.


    Format: ePUB
    Kopierschutz: AdobeDRM
    Seitenzahl: 216
    Erscheinungsdatum: 03.05.2016
    Sprache: Englisch
    ISBN: 9781119267201
    Verlag: Wrox
    Größe: 5829 kBytes
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Professional Hadoop

Hadoop Introduction


The components of Hadoop
The roles of HDFS, MapReduce, YARN, ZooKeeper, and Hive
Hadoop's integration with other systems
Data integration and Hadoop
Hadoop is an essential tool for managing big data. This tool fills a rising need for businesses managing large data stores, or data lakes as Hadoop refers to them. The biggest need in business, when it comes to data, is the ability to scale. Technology and business are driving organizations to gather more and more data, which increases the need to manage it efficiently. This chapter examines the Hadoop Stack, as well as all of the associated components that can be used with Hadoop.

In building the Hadoop Stack, each component plays an important role in the platform. The stack starts with the essential requirements contained in the Hadoop Common, which is a collection of common utilities and libraries that support other Hadoop modules. Like any stack, these supportive files are a necessary requirement for a successful implementation. The well-known file system, the Hadoop Distributed File System or HDFS, is at the heart of Hadoop, but it won't threaten your budget. To narrow your perspective on a set of data, you can use the programming logic contained within MapReduce, which provides massive scalability across many servers in a Hadoop cluster. For resource management, you can consider adding Hadoop YARN, the distributed operating system for your big data apps, to your stack.

ZooKeeper, another Hadoop Stack component, enables distributed processes to coordinate with each other through a shared hierarchical name space of data registers, known as znodes. Every znode is identified by a path, with path elements separated by a slash (/).

There are other systems that can integrate with Hadoop and benefit from its infrastructure. Although Hadoop is not considered a Relational Database Management System (RDBMS), it can be used along with systems like Oracle, MySQL, and SQL Server. Each of these systems has developed connector-type components that are processed using Hadoop's framework. We will review a few of these components in this chapter and illustrate how they interact with Hadoop.
Business Analytics and Big Data

Business Analytics is the study of data through statistical and operational analysis. Hadoop allows you to conduct operational analysis on its data stores. These results allow organizations and companies to make better business decisions that are beneficial to the organization.

To understand this further, let's build a big data profile. Because of the amount of data involved, the data can be distributed across storage and compute nodes, which benefits from using Hadoop. Because it is distributed and not centralized, it lacks the characteristics of an RDBMS. This allows you to use large data stores and an assortment of data types with Hadoop.

For example, let's consider a large data store like Google, Bing, or Twitter. All of these data stores can grow exponentially based on activity, such as queries and a large user base. Hadoop's components can help you process these large data stores.

A business, such as Google, can use Hadoop to manipulate, manage, and produce meaningful results from their data stores. The traditional tools commonly used for Business Analytics are not designed to work with or analyze extremely large datasets, but Hadoop is a solution that fits these business models.
The Components of Hadoop

The Hadoop Common is the foundation of Hadoop, because it contains the primary services and basic processes, such as the abstraction of the underlying operating system and its filesystem. Hadoop Common also contains the necessary Java Archive (JAR) files and scripts required to start Hadoop. The Hadoop Common package even prov

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