by Frank Kane | Released September 2016 | ISBN: 9781787129931

More than 15 hands-on examples to help you analyze large data sets with Apache SparkAbout This VideoUnderstand how Spark can be distributed across computing clustersDevelop and run Spark jobs efficiently using PythonA hands-on tutorial with over 15 real-world examples teaching you Big Data processing with Spark In DetailApache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis. This course will be your companion to learn Apache Spark in a hands-on manner. Start with understanding how to set up Spark on a single system or on a cluster. From analyzing large data sets using Spark RDD, to developing and running effective Spark jobs quickly using Python, this course will teach you everything. Packed with over 15 interactive, fun-filled examples relevant to the real-world, the course will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. Show and hide more Publisher resources Download Example Code

1. Chapter 1 : Getting Started with Spark
   • Introduction 00:02:16
   • How to Use This Course 00:01:41
   • Getting Set Up – Installing Python, a JDK, Spark, and its Dependencies 00:14:53
   • Installing the MovieLens Movie Rating Dataset 00:03:35
   • Run Your First Spark Program – Ratings Histogram Example 00:04:53
2. Chapter 2 : Spark Basics and Simple Examples
   • Introduction to Spark 00:10:12
   • The Resilient Distributed Dataset (RDD) Z 00:12:17
   • Ratings Histogram Walkthrough 00:13:34
   • Key/Value RDDs and the Average Friends by Age Example 00:16:13
   • Running the Average Friends by Age Example 00:05:39
   • Filtering RDDs and the Minimum Temperature by Location Example 00:08:10
   • Running the Minimum Temperature Example and Modifying It for Maximums 00:05:09
   • Running the Maximum Temperature by Location Example 00:03:22
   • Counting Word Occurrences Using flatmap() 00:07:28
   • Improving the Word Count Script with Regular Expressions 00:04:45
   • Sorting the Word Count Results 00:07:45
   • Find the Total Amount Spent by Customer 00:04:01
   • Check Your Results and Sort Them by Total Amount Spent 00:05:08
   • Check Your Sorted Implementation and Results Against Mine 00:03:19
3. Chapter 3 : Advanced Examples of Spark Programs
   • Find the Most Popular Movie 00:05:53
   • Use Broadcast Variables to Display Movie Names Instead of ID Numbers 00:08:24
   • Find the Most Popular Superhero in a Social Graph 00:04:29
   • Run the Script – Discover Who the Most Popular Superhero is! 00:06:00
   • Superhero Degrees of Separation – Introducing Breadth-First Search 00:07:54
   • Superhero Degrees of Separation – Accumulators and Implementing BFS in Spark 00:06:45
   • Superhero Degrees of Separation – Review the Code and Run it 00:09:14
   • Item-Based Collaborative Filtering in Spark, cache(), and persist() 00:10:13
   • Running the Similar Movies Script Using Spark's Cluster Manager 00:10:55
   • Improve the Quality of Similar Movies 00:02:58
4. Chapter 4 : Running Spark on a Cluster
   • Introducing Elastic MapReduce 00:05:08
   • Setting Up Your AWS / Elastic MapReduce Account and PuTTY 00:09:56
   • Partitioning 00:04:22
   • Create Similar Movies from One Million Ratings – Part 1 00:05:12
   • Create Similar Movies from One Million Ratings – Part 2 00:11:28
   • Create Similar Movies from One Million Ratings – Part 3 00:03:29
   • Troubleshooting Spark on a Cluster 00:03:43
   • More Troubleshooting and Managing Dependencies 00:05:48
5. Chapter 5 : SparkSQL, DataFrames, and DataSets
   • Introducing SparkSQL 00:06:08
   • Executing SQL Commands and SQL-Style Functions on a DataFrame 00:08:17
   • Using DataFrames Instead of RDDs 00:05:53
6. Chapter 6 : Other Spark Technologies and Libraries
   • Introducing MLLib 00:08:10
   • Using MLLib to Produce Movie Recommendations 00:02:57
   • Analyzing the ALS Recommendations Results 00:04:53
   • Using DataFrames with MLLib 00:07:32
   • Spark Streaming and GraphX 00:07:36
7. Chapter 7 : You Made It! Where to Go from Here
   • Learning More about Spark and Data Science 00:04:09
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