Big Data Analytics with Spark: A Practitioner’s Guide to Using Spark for Large Scale Data Analysis is published by Apress on December 25, 2015. This book has 277 pages in English, ISBN-10 1484209656, ISBN-13 978-1484209653. PDF, EPUB is available for download below.
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.
What you’ll learn
- Write Spark applications in Scala for processing and analyzing large-scale data
- Interactively analyze large-scale data with Spark SQL using just SQL and HiveQL
- Process high-velocity stream data with Spark Streaming
- Develop machine learning applications with MLlib and Spark ML
- Analyze graph-oriented data and implement graph algorithms with GraphX
- Deploy Spark with the Standalone cluster manger, YARN, or Mesos
- Monitor Spark applications
Who this book is for
Big Data Analytics with Spark is for data scientists, business analysts, data architects, and data analysts looking for a better and faster tool for large-scale data analysis. It is also for software engineers and developers building Big Data products.