Hadoop Fundamentals
Packt Publishing Limited (Verlag)
978-1-83882-761-8 (ISBN)
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About This Book
* Deploy a robust system for massive and parallelized data processing and storage
*Build applications by implementing algorithms that process big data
*Explore key tools for managing Hadoop big data ecosystems effectively
Who This Book Is For
This book is for you if you're a data scientist or programmer with a background in business intelligence, data warehousing, data modeling, or machine learning and want to learn about the world of distributed data processing.
What You Will Learn
* Insert data into Hadoop Distributed File System (HDFS)
*Compute algorithms with MapReduce
*Delete and transport data to test Erasure and Balancer
*Build a YARN application for the MapReduce workflow
*Create Resilient Distributed Data System (RDD) analytics for Twitter tags and visualize your data using Python
*Configure multiple permission cases to see how Sqoop to Hive secured access works
In Detail
Apache Hadoop is an open source distributed processing framework that processes, manages, and stores big data for applications.
Hadoop Fundamentals begins by covering how distributed file systems in Hadoop work and how they are managed with YARN, a Hadoop management layer. You'll understand the MapReduce paradigm, which is the basic paradigm of data processing and analytics in parallelized systems. You'll then delve into Apache Spark, a super-fast cluster computing technology that extends the Hadoop MapReduce functionality to efficiently perform a variety of computations. As you advance, you'll explore data resources in the Hadoop ecosystem made by the big data community and enterprise users to find out what lies beyond MapReduce computations. This Hadoop book also takes you through frameworks such as Flame, Sqoop, Hive, and HBase for ingesting and warehousing different types of data. Finally, you'll learn all about the solutions that Hadoop systems implement to solve security issues.
By the end of this book, you'll have understood what big data is and have the skills necessary to work with Hadoop systems.
Manisha Malhotra is a Ph.D. scholar and completed Big Data Engineering Program from BITS, Pilani and UpGrad. With an objective to contribute to the field of Big Data, I am a dedicated professional and a keen learner with hands-on experience in various Big Data tools like Spark, Pig, Hive, Kafka, MapReduce, etc. and with the passion towards Technology, I have been working as an Instructor with companies like Cognizant, Edureka, UpGrad, Teach Tech Services and Aegis Educations etcetera. Comfortable in both LVC and classroom training environment, with more than 9 years of experience in the industry, I am moving towards my mission of bridging the gap between the education system and industry. Daniel Penalva is an experienced data modeler and model researcher. Having worked through his Ph.D. with financial market data analysis, dimensionality reduction, statistical methods such resampling, neural networks modeling, ODE learning, and others, he acquired a very distinguished data processing and management knowledge. Vijay Garg has around 11 yrs of IT experience and from past around 5 years completely working on Big-Data/Hadoop and digital technologies. He worked on almost all the flavors of Big-Data as well as some additional cutting edge technologies in digital era like MapReduce, PIG, Hive, Sqoop, Flume, Spark, Scala, Hbase, Kafka, EK stack, SolR etc. In his free time he try to design complex architecture by using digital technologies/tools in order to solve any complex problem in current world.
| Erscheint lt. Verlag | 30.8.2019 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| ISBN-10 | 1-83882-761-7 / 1838827617 |
| ISBN-13 | 978-1-83882-761-8 / 9781838827618 |
| Zustand | Neuware |
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