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. Big Data Analytics with R and Hadoop is a tutorial style book that focuses on all the powerful big data tasks that can be achieved by integrating R and Hadoop.
This book is ideal for R developers who are looking for a way to perform big data analytics with Hadoop. This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages.
It would be helpful if readers have basic knowledge of R. Let Hadoop For Dummies help harness the power of your data and rein in the information overload Big data has become big business, and companies and organizations of all sizes are struggling to find ways to retrieve valuable information from their massive data sets with becoming overwhelmed.
Enter Hadoop and this easy-to-understand For Dummies guide. Hadoop For Dummies helps readers understand the value of big data, make a business case for using Hadoop, navigate the Hadoop ecosystem, and build and manage Hadoop applications and clusters. Explains the origins of Hadoop, its economic benefits, and its functionality and practical applications Helps you find your way around the Hadoop ecosystem, program MapReduce, utilize design patterns, and get your Hadoop cluster up and running quickly and easily Details how to use Hadoop applications for data mining, web analytics and personalization, large-scale text processing, data science, and problem-solving Shows you how to improve the value of your Hadoop cluster, maximize your investment in Hadoop, and avoid common pitfalls when building your Hadoop cluster From programmers challenged with building and maintaining affordable, scaleable data systems to administrators who must deal with huge volumes of information effectively and efficiently, this how-to has something to help you with Hadoop.
It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. The book begins with an overview of big data and Apache Hadoop.
Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems. The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring.
Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster. Existing Hadoop users who want to get up to speed with the new features introduced in Hadoop 3 will also benefit from this book.
Having knowledge of Java programming will be an added advantage. Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. This is a Packt Instant How-to guide, which provides concise and clear recipes for getting started with Hadoop. This book is for big data enthusiasts and would-be Hadoop programmers.
It is also meant for Java programmers who either have not worked with Hadoop at all, or who know Hadoop and MapReduce but are not sure how to deepen their understanding. Get ready to unlock the power of your data. The course starts by covering basic commands used by big data developers on a daily basis.
Then, you'll focus on HDFS architecture and command lines that a developer uses frequently. Next, you'll use Flume to import data from other ecosystems into the Hadoop ecosystem, which plays a crucial role in the data available for storage and analysis using MapReduce. Here you'll also learn to load, transform, and store data in Pig relation.
Finally, you'll dive into Hive functionality and learn to load, update, delete content in Hive. By the end of the course, you'll have gained enough knowledge to work with big data using Hadoop. So, grab the course and handle big data sets with ease. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects.
Now, its data processing has been completely overhauled: Apache Hadoop YARN provides resource management at data center scale and easier ways to create distributed applications that process petabytes of data.
YARN project founder Arun Murthy and project lead Vinod Kumar Vavilapalli demonstrate how YARN increases scalability and cluster utilization, enables new programming models and services, and opens new options beyond Java and batch processing. They walk you through the entire YARN project lifecycle, from installation through deployment.
Hadoop bought capabilities to store massive amount of data in distributed environment and provide the way to process them effectively. It's a distributed data processing system which support distributed file systems and it offers a way to parallelize and execute programs on a cluster of machines.
It could be installed on cluster with using large number of commodities hardware which intern optimized the overall solution costs. This book is a concise guide on getting started with Hadoop and Hive. It provides overall understanding on Hadoop and how it works and same time provide the sample code to speed up development with very minimum effort.
It will explain the logic, code, and configurations needed to build a successful, distributed, concurrent application, as well as the reason behind those decisions The book has been written considering for beginner and intermediate developer who want to get introduce in Hadoop. Table of Contents 1. Big Data 2. Hadoop 3. Getting Started with Hadoop 5. MapReduce 7. YARN 8. Hive 9. Getting Started with Hive.
Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase.
It explains how big is 'Big Data' and why everybody is trying to implement this into their IT project. It includes research work on various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends.
Big Data and Hadoop have taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience and would be a reference to help readers. Skip to content. Hadoop The Definitive Guide. Hadoop Beginner s Guide. Hadoop Beginner s Guide Book Review:. Hadoop in Action. Hadoop in Action Book Review:. Programming Pig. Hadoop: The Definitive Guide is the most thorough book available on the subject. Download online E Book.
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