Apache Spark 2 for Beginners

Description

Key Features This book offers an easy introduction to the Spark framework published on the latest version of Apache Spark 2 Perform efficient data processing, machine learning and graph processing using various Spark components A practical guide aimed at beginners to get them up and running with Spark Book Description Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists. This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data processing. After that, we take a look at Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application. By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark. What you will learn Get to know the fundamentals of Spark 2 and the Spark programming model using Scala and Python Know how to use Spark SQL and DataFrames using Scala and Python Get an introduction to Spark programming using R Perform Spark data processing, charting, and plotting using Python Get acquainted with Spark stream processing using Scala and Python Be introduced to machine learning using Spark MLlib Get started with graph processing using the Spark GraphX Bring together all that you've learned and develop a complete Spark application About the Author Rajanarayanan Thottuvaikkatumana, Raj, is a seasoned technologist with more than 23 years of software development experience at various multinational companies. He has lived and worked in India, Singapore, and the USA, and is presently based out of the UK. His experience includes architecting, designing, and developing software applications. He has worked on various technologies including major databases, application development platforms, web technologies, and big data technologies. Since 2000, he has been working mainly in Java related technologies, and does heavy-duty server-side programming in Java and Scala. He has worked on very highly concurrent, highly distributed, and high transaction volume systems. Currently he is building a next generation Hadoop YARN-based data processing platform and an application suite built with Spark using Scala. Raj holds one master's degree in Mathematics, one master's degree in Computer Information Systems and has many certifications in ITIL and cloud computing to his credit. Raj is the author of Cassandra Design Patterns - Second Edition, published by Packt. When not working on the assignments his day job demands, Raj is an avid listener to classical music and watches a lot of tennis. Table of Contents Spark Fundamentals Spark Programming Model Spark SQL Spark Programming with R Spark Data Analysis with Python Spark Stream Processing Spark Machine Learning Spark Graph Processing Designing Spark Applications Cover Copyright Credits About the Author About the Reviewer www.PacktPub.com Table of Contents Preface Chapter 1: Spark Fundamentals An overview of Apache Hadoop Understanding Apache Spark Installing Spark on your machines Python installation R installation Spark installation Development tool installation Optional software installation IPython RStudio Apache Zeppelin References Summary Chapter 2: Spark Programming Model Functional programming with Spark Understanding Spark RDD Spark RDD is immutable Spark RDD is distributable Spark RDD lives in memory Spark RDD is strongly typed Data transformations and actions with RDDs Monitoring with Spark The basics of programming with Spark MapReduce Joins More actions Creating RDDs from files Understanding the Spark library stack Reference Summary Chapter 3: Spark SQL Understanding the structure of data Why Spark SQL? Anatomy of Spark SQL DataFrame programming Programming with SQL Programming with DataFrame API Understanding Aggregations in Spark SQL Understanding multi-datasource joining with SparkSQL Introducing datasets Understanding Data Catalogs References Summary Chapter 4: Spark Programming with R The need for SparkR Basics of the R language DataFrames in R and Spark Spark DataFrame programming with R Programming with SQL Programming with R DataFrame API Understanding aggregations in Spark R Understanding multi-datasource joins with SparkR References Summary Chapter 5: Spark Data Analysis with Python Charting and plotting libraries Setting up a dataset Data analysis use cases Charts and plots Histogram Density plot Bar chart Stacked bar chart Pie chart Donut chart Box plot Vertical bar chart Scatter plot Enhanced scatter plot Line graph References Summary Chapter 6: Spark Stream Processing Data stream processing Micro batch data processing Programming with DStreams A log event processor Getting ready with the Netcat server Organizing files Submitting the jobs to the Spark cluster Monitoring running applications Implementing the application in Scala Compiling and running the application Handling the output Implementing the application in Python Windowed data processing Counting the number of log event messages processed in Scala Counting the number of log event messages processed in Python More processing options Kafka stream processing Starting Zookeeper and Kafka Implementing the application in Scala Implementing the application in Python Spark Streaming jobs in production Implementing fault-tolerance in Spark Streaming data processing applications Structured streaming References Summary Chapter 7: Spark Machine Learning Understanding machine learning Why Spark for machine learning? Wine quality prediction Model persistence Wine classification Spam filtering Feature algorithms Finding synonyms References Summary Chapter 8: Spark Graph Processing Understanding graphs and their usage The Spark GraphX library GraphX overview Graph partitioning Graph processing Graph structure processing Tennis tournament analysis Applying the PageRank algorithm Connected component algorithm Understanding GraphFrames Understanding GraphFrames queries References Summary Chapter 9: Designing Spark Applications Lambda Architecture Microblogging with Lambda Architecture An overview of SfbMicroBlog Getting familiar with data Setting the data dictionary Implementing Lambda Architecture Batch layer Serving layer Speed layer Queries Working with Spark applications Coding style Setting up the source code Understanding data ingestion Generating purposed views and queries Understanding custom data processes References Summary Index