Mastering Apache Spark 2.x

Key Features

• An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities.
• Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark.
• Master the art of real-time processing with the help of Apache Spark 2.x

Book Description

Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark s functionality and implement your data flows and machine/deep learning programs on top of the platform.

The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x.

You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets.

You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks.

What you will learn

• Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J
• Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming
• Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames
• Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud
• Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames
• Learn how specific parameter settings affect overall performance of an Apache Spark cluster
• Leverage Scala, R and python for your data science projects

About the Author

Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics. His current research focus is on scalable machine learning on Apache Spark. He is a contributor to various open source projects and works as an associate professor for artificial intelligence at Swiss University of Applied Sciences, Berne. He is a member of the IBM Technical Expert Council and the IBM Academy of Technology, IBM's leading brains trust.

Table of Contents

1. A first taste and what s new in ApacheSpark V2
2. Apache Spark SQL
3. The Catalyst Optimizer
4. Project Tungsten
5. Apache Spark Streaming
6. Structured Streaming
7. Apache Spark MLlib
8. Apache SparkML
9. Apache SystemML
10. DeepLearning on Apache Spark with DeepLearning4J, ApacheSystemML,H2O
11. Apache Spark GraphX
12. ApacheSpark GraphFrames
13. ApacheSpark with Jupyter Noteboks on IBM DataScience Experience
14. ApacheSpark on Kubernetes

Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics. His current research focus is on scalable machine learning on Apache Spark. He is a contributor to various open source projects and works as an associate professor for artificial intelligence at Swiss University of Applied Sciences, Berne. He is a member of the IBM Technical Expert Council and the IBM Academy of Technology, IBM's leading brains trust.