Books: 5


Big Data Processing Made Easy
Learn to use Apache Pig to develop lightweight big data applications easily and quickly. This book shows you many optimization techniques and covers every context where Pig is used in big data analytics.Beginning Apache Pigshows you how Pig is easy to learn and requires relatively little time to develop more » big data applications.The book is divided into four parts: the complete features of Apache Pig; integration with other tools; how to solve complex business problems; and optimization of tools.You'll discover topics such as MapReduce and why it cannot meet every business need; the features of Pig Latin such as data types for each load, store, joins, groups, and ordering; how Pig workflows can be created; submitting Pig jobs using Hue; and working with Oozie. You'll also see how to extend the framework by writing UDFs and custom load, store, and filter functions. Finally you'll cover different optimization techniques such as gathering statistics about a Pig script, joining strategies, parallelism, and the role of data formats in good performance. What You Will Learn • Use all the features of Apache Pig • Integrate Apache Pig with other tools • Extend Apache Pig • Optimize Pig Latin code • Solve different use cases for Pig Latin Who This Book Is For All levels of IT professionals: architects, big data enthusiasts, engineers, developers, and big data administrators « less
A Comprehensive Guide to Machine Learning
This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data. This new paradigm of teaching Machine Learning will bring about a radical change in perception more » for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots. For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data. Who This Book is For: Data scientists, data science professionals and researchers in academia who want to understand the nuances of Machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark. What you will learn: 1. ML model building process flow2. Theoretical aspects of Machine Learning3. Industry based Case-Study4. Example based understanding of ML algorithm using R5. Building ML models using Apache Hadoop and Spark « less
This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. With Pig, you can batch-process data without having to create a full-fledged application—making it easy for you to experiment with new datasets. This fully updated more » edition of Programming Pig introduces new users to Pig, and provides experienced users with comprehensive coverage on key features such as the Pig Latin scripting language, the Grunt shell, and User Defined Functions (UDFs) for extending Pig. If you need to analyze terabytes of data, this book shows you how to do it efficiently with Pig. « less
Explore the Hadoop MapReduce v2 ecosystem to gain insights from very large datasets
Starting with installing Hadoop YARN, MapReduce, HDFS, and other Hadoop ecosystem components, with this book, you will soon learn about many exciting topics such as MapReduce patterns, using Hadoop to solve analytics, classifications, online marketing, recommendations, and data indexing and searching. more » You will learn how to take advantage of Hadoop ecosystem projects including Hive, HBase, Pig, Mahout, Nutch, and Giraph and be introduced to deploying in cloud environments. Finally, you will be able to apply the knowledge you have gained to your own real-world scenarios to achieve the best-possible results. « less
Hadoop is mostly written in Java, but that doesn't exclude the use of other programming languages with this distributed storage and processing framework, particularly Python. With this concise book, you'll learn how to use Python with the Hadoop Distributed File System (HDFS), MapReduce, the Apache Pig more » platform and Pig Latin script, and the Apache Spark cluster-computing framework. Authors Zachary Radtka and Donald Miner from the data science firm Miner & Kasch take you through the basic concepts behind Hadoop, MapReduce, Pig, and Spark. Then, through multiple examples and use cases, you'll learn how to work with these technologies by applying various Python tools. Use the Python library Snakebite to access HDFS programmatically from within Python applications Write MapReduce jobs in Python with mrjob, the Python MapReduce library Extend Pig Latin with user-defined functions (UDFs) in Python Use the Spark Python API (PySpark) to write Spark programs with Python Learn how to use the Luigi Python workflow scheduler to manage MapReduce jobs and Pig scripts « less