Learning Apache Spark 2.0

Key Features

• Exclusive guide that covers how to get up and running with fast data processing using Apache Spark
• Explore and exploit various possibilities with Apache Spark using real-world use cases in this book
• Want to perform efficient data processing at real time? This book will be your one-stop solution.

Book Description

Spark juggernaut keeps on rolling and getting more and more momentum each day. Spark provides key capabilities in the form of Spark SQL, Spark Streaming, Spark ML and Graph X all accessible via Java, Scala, Python and R. Deploying the key capabilities is crucial whether it is on a Standalone framework or as a part of existing Hadoop installation and configuring with Yarn and Mesos.

The next part of the journey after installation is using key components, APIs, Clustering, machine learning APIs, data pipelines, parallel programming. It is important to understand why each framework component is key, how widely it is being used, its stability and pertinent use cases.

Once we understand the individual components, we will take a couple of real life advanced analytics examples such as Building a Recommendation system , Predicting customer churn and so on.

The objective of these real life examples is to give the reader confidence of using Spark for real-world problems.

What you will learn

• Get an overview of big data analytics and its importance for organizations and data professionals
• Delve into Spark to see how it is different from existing processing platforms
• Understand the intricacies of various file formats, and how to process them with Apache Spark.
• Realize how to deploy Spark with YARN, MESOS or a Stand-alone cluster manager.
• Learn the concepts of Spark SQL, SchemaRDD, Caching and working with Hive and Parquet file formats
• Understand the architecture of Spark MLLib while discussing some of the off-the-shelf algorithms that come with Spark.
• Introduce yourself to the deployment and usage of SparkR.
• Walk through the importance of Graph computation and the graph processing systems available in the market
• Check the real world example of Spark by building a recommendation engine with Spark using ALS.
• Use a Telco data set, to predict customer churn using Random Forests.

About the Author

Muhammad Asif Abbasi has worked in the industry for over 15 years in a variety of roles from engineering solutions to selling solutions and everything in between. Asif is currently working with SAS a market leader in Analytic Solutions as a Principal Business Solutions Manager for the Global Technologies Practice. Based in London, Asif has vast experience in consulting for major organizations and industries across the globe, and running proof-of-concepts across various industries including but not limited to telecommunications, manufacturing, retail, finance, services, utilities and government. Asif is an Oracle Certified Java EE 5 Enterprise architect, Teradata Certified Master, PMP, Hortonworks Hadoop Certified developer, and administrator. Asif also holds a Master's degree in Computer Science and Business Administration.

Table of Contents

1. Architecture and Installation
2. Transformations and Actions with Spark RDDs
3. ETL with Spark
4. Spark SQL
5. Spark Streaming
6. Machine Learning with Spark
7. GraphX
8. Operating in Clustered Mode
9. Building a Recommendation System
10. Customer Churn Prediction
11. There's More with Spark