Book Image

Mastering Scala Machine Learning

By : Alex Kozlov
Book Image

Mastering Scala Machine Learning

By: Alex Kozlov

Overview of this book

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Table of Contents (17 chapters)
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
Preface
10
Advanced Model Monitoring
Index

Hive and Impala


One of the design considerations for a new framework is always the compatibility with the old frameworks. For better or worse, most data analysts still work with SQL. The roots of the SQL go to an influential relational modeling paper (Codd, Edgar F (June 1970). A Relational Model of Data for Large Shared Data Banks. Communications of the ACM (Association for Computing Machinery) 13 (6): 377–87). All modern databases implement one or another version of SQL.

While the relational model was influential and important for bringing the database performance, particularly for Online Transaction Processing (OLTP) to the competitive levels, the significance of normalization for analytic workloads, where one needs to perform aggregations, and for situations where relations themselves change and are subject to analysis, is less critical. This section will cover the extensions of standard SQL language for analysis engines traditionally used for big data analytics: Hive and Impala. Both...