This book is your gateway to mastering machine learning with Apache Spark 2.x. Through detailed hands-on recipes, you'll delve into building scalable ML models, optimizing big data processes, and enhancing project efficiency. Gain practical knowledge and explore real-world applications of recommendations, clustering, analytics, and more with Spark's powerful capabilities. What this Book will help me do Understand how to integrate Scala and Spark for effective machine learning development. Learn to create scalable recommendation engines using Spark. Master the development of clustering systems to organize unlabelled data at scale. Explore Spark libraries to implement efficient text analytics and search engines. Optimize large-scale data operations, tackling high-dimensional issues with Spark. Author(s) The team of authors brings expertise in machine learning, data science, and Spark technologies. Their combined industry experience and academic knowledge ensure the book is grounded in practical applications while offering theoretical insights. With clear explanations and a step-by-step approach, they aim to simplify complex concepts for developers and data scientists. Who is it for? This book is crafted for Scala developers familiar with machine learning concepts but seeking practical applications with Spark. If you have been implementing models but want to scale them and leverage Spark's robust ecosystem, this guide will serve you well. It is ideal for professionals seeking to deepen their skills in Spark and data science.

This book is a step-by-step guide for learning how to use Spark for different types of big-data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, MLlib, and Spark ML. Big Data Analytics with Spark shows you how to use Spark and leverage its easy-to-use features to increase your productivity. You learn to perform fast data analysis using its in-memory caching and advanced execution engine, employ in-memory computing capabilities for building high-performance machine learning and low-latency interactive analytics applications, and much more. Moreover, the book shows you how to use Spark as a single integrated platform for a variety of data processing tasks, including ETL pipelines, BI, live data stream processing, graph analytics, and machine learning. The book also includes a chapter on Scala, the hottest functional programming language, and the language that underlies Spark. You’ll learn the basics of functional programming in Scala, so that you can write Spark applications in it. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, such as HDFS, Avro, Parquet, Kafka, Cassandra, HBase, Mesos, and so on. It also provides an introduction to machine learning and graph concepts. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to have is some programming knowledge in any language.