By Garry Turkington
Design and enforce information processing, lifecycle administration, and analytic workflows with the state-of-the-art toolbox of Hadoop 2
About This Book
- Construct state of the art purposes utilizing higher-level interfaces and instruments past the conventional MapReduce approach
- Use the original good points of Hadoop 2 to version and research Twitter's worldwide move of consumer generated data
- Develop a prototype on a neighborhood cluster and installation to the cloud (Amazon net Services)
Who This e-book Is For
If you're a approach or software developer drawn to studying how one can remedy useful difficulties utilizing the Hadoop framework, then this publication is perfect for you. you're anticipated to be acquainted with the Unix/Linux command-line interface and feature a few adventure with the Java programming language. Familiarity with Hadoop will be a plus.
This e-book introduces you to the area of establishing data-processing functions with the wide range of instruments supported through Hadoop 2. beginning with the center elements of the framework―HDFS and YARN―this ebook will consultant you thru the way to construct functions utilizing various approaches.
You will learn the way YARN thoroughly alterations the connection among MapReduce and Hadoop and permits the latter to aid extra diverse processing ways and a broader array of purposes. those comprise real-time processing with Apache Samza and iterative computation with Apache Spark. subsequent up, we talk about Apache Pig and the dataflow information version it presents. you will find easy methods to use Pig to investigate a Twitter dataset.
With this publication, it is possible for you to to make your lifestyles more straightforward through the use of instruments equivalent to Apache Hive, Apache Oozie, Hadoop Streaming, Apache Crunch, and Kite SDK. The final a part of this booklet discusses the most likely destiny course of significant Hadoop parts and the way to become involved with the Hadoop community.
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Additional info for Learning Hadoop 2
In ancient times, before the term "big data" came into the picture (which equates to maybe a decade ago), there were few options to process datasets of sizes in terabytes and beyond. Some commercial databases could, with very specific and expensive hardware setups, be scaled to this level, but the expertise and capital expenditure required made it an option for only the largest organizations. Alternatively, one could build a custom system aimed at the specific problem at hand. This suffered from some of the same problems (expertise and cost) and added the risk inherent in any cutting-edge system.
He architects distributed systems to process product catalogue data. Prior to building high-throughput systems at Amazon, he was working on the entire software stack, both as a systems-level developer at Ericsson and IBM as well as an application developer at Manhattan Associates. He maintains a strong interest in bulk data processing, data streaming, and service-oriented software architectures. Jakob Homan has been involved with big data and the Apache Hadoop ecosystem for more than 5 years. He is a Hadoop committer as well as a committer for the Apache Giraph, Spark, Kafka, and Tajo projects, and is a PMC member.
I would also like to thank Garry, Gabriele, and the folks at Packt Publishing for the opportunity to review this manuscript and for their patience and understanding, as my free time was consumed when writing my dissertation. Davide Setti, after graduating in physics from the University of Trento, joined the SoNet research unit at the Fondazione Bruno Kessler in Trento, where he applied large-scale data analysis techniques to understand people's behaviors in social networks and large collaborative projects such as Wikipedia.