Apache Spark with Scala [Video]

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Apache Spark with Scala [Video]

Frank Kane

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Get to grips with the fundamentals of Apache Spark for real-time Big Data processing
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Video Details

ISBN 139781787129849
Course Length7 hours 4 minutes

Video Description

With the rise in popularity of the term ‘Big Data’, there is an increasing need to process large amounts of data in real-time, with maximum efficiency. This has led to Apache Spark gaining popularity in the Big Data market very quickly. The Spark ecosystem allows you to process large streams of data in real-time. As Spark is built on Scala, knowledge of both has become vital for data scientists and data analysts today. This comprehensive 7 hour course will empower you to build efficient Spark applications to fulfill your Big Data needs.You will start with quickly understanding the basics of Scala and proceed to set up the development environment for Apache Spark and Scala for Big Data processing. You will understand the different modules of Spark like Spark SQL, Spark Streaming and GraphX, along with when and how to use them. While doing so, you will build practical, real-world Spark applications in Scala and see how you can deploy them on the cloud. You will also learn how to perform machine learning in real time using Spark’s MLlib module. Finally, you will learn how to run Spark on Hadoop clusters along with best practices and troubleshooting techniques.With over 20 carefully selected examples and abundant explanation to explain even the most difficult concepts, this course will ensure your success in taming your Big Data challenges using Spark with Scala.

Table of Contents

Getting Started
Introduction, and Getting Set Up
[Activity] Create a Histogram of Real Movie Ratings with Spark!
Scala Crash Course
[Activity] Scala Basics, Part 1
[Exercise] Scala Basics, Part 2
[Exercise] Flow Control in Scala
[Exercise] Functions in Scala
[Exercise] Data Structures in Scala
Spark Basics and Simple Examples
Introduction to Spark
The Resilient Distributed Dataset
Ratings Histogram Walkthrough
Spark Internals
Key/Value RDDs and the Average Friends by Age example
[Activity] Running the Average Friends by Age Example
Filtering RDDs and the Minimum Temperature by Location Example
[Activity] Running the Minimum Temperature Example and Modifying It for Maximum Temperature
[Activity] Counting Word Occurrences Using flatmap()
[Activity] Improving the Word Count Script with Regular Expressions
[Activity] Sorting the Word Count Results
[Exercise] Finding the Total Amount Spent by Customer
[Exercise] Check your Results, and Sort Them by Total Amount Spent
Check Your Results and Implementation against Mine
Advanced Examples of Spark Programs
[Activity] Find the Most Popular Movie
[Activity] Use Broadcast Variables to Display Movie Names
[Activity] Find the Most Popular Superhero in a Social Graph
Superhero Degrees of Separation – Introducing Breadth-First Search
Superhero Degrees of Separation – Accumulators and Implementing BFS in Spark
Superhero Degrees of Separation – Review the Code, and Run It!
Item-Based Collaborative Filtering in Spark, cache(), and persist()
[Activity] Running the Similar Movies Script using Spark's Cluster Manager
[Exercise] Improve the Quality of Similar Movies
Running Spark on a Cluster
[Activity] Using spark-submit to Run Spark Driver Scripts
[Activity] Packaging Driver Scripts with SBT
Introducing Amazon Elastic MapReduce
Creating Similar Movies from One Million Ratings on EMR
Partitioning
Best Practices for Running on a Cluster
Troubleshooting and Managing Dependencies
SparkSQL, DataFrames, and DataSets
Introduction to SparkSQL
[Activity] Using SparkSQL
[Activity] Using DataFrames and DataSets
[Activity] Using DataSets Instead of RDDs
Machine Learning with MLLib
Introducing MLLib
[Activity] Using MLLib to Produce Movie Recommendations
[Activity] Linear Regression with MLLib
[Activity] Using DataFrames with MLLib
Intro to Spark Streaming
Spark Streaming Overview
[Activity] Set Up a Twitter Developer Account, and Stream Tweets
Structured Streaming
Intro to GraphX
GraphX, Pregel, and breadth-first search with Pregel.
[Activity] Superhero Degrees of Separation using GraphX
You Made It! Where to Go from Here?
Learning More, and Career Tips

What You Will Learn

  • Frame your Big Data problems as Apache Spark jobs
  • Set up the development environment for Scala and Apache Spark
  • Develop efficient Spark applications using Scala
  • Build and deploy Spark jobs on Hadoop clusters
  • Process real-time streams of data using Spark Streaming
  • Query your structured data using SparkSQL and work with the DataSets API
  • Analyze and process graph structures using Spark’s GraphX module

Authors

Table of Contents

Getting Started
Introduction, and Getting Set Up
[Activity] Create a Histogram of Real Movie Ratings with Spark!
Scala Crash Course
[Activity] Scala Basics, Part 1
[Exercise] Scala Basics, Part 2
[Exercise] Flow Control in Scala
[Exercise] Functions in Scala
[Exercise] Data Structures in Scala
Spark Basics and Simple Examples
Introduction to Spark
The Resilient Distributed Dataset
Ratings Histogram Walkthrough
Spark Internals
Key/Value RDDs and the Average Friends by Age example
[Activity] Running the Average Friends by Age Example
Filtering RDDs and the Minimum Temperature by Location Example
[Activity] Running the Minimum Temperature Example and Modifying It for Maximum Temperature
[Activity] Counting Word Occurrences Using flatmap()
[Activity] Improving the Word Count Script with Regular Expressions
[Activity] Sorting the Word Count Results
[Exercise] Finding the Total Amount Spent by Customer
[Exercise] Check your Results, and Sort Them by Total Amount Spent
Check Your Results and Implementation against Mine
Advanced Examples of Spark Programs
[Activity] Find the Most Popular Movie
[Activity] Use Broadcast Variables to Display Movie Names
[Activity] Find the Most Popular Superhero in a Social Graph
Superhero Degrees of Separation – Introducing Breadth-First Search
Superhero Degrees of Separation – Accumulators and Implementing BFS in Spark
Superhero Degrees of Separation – Review the Code, and Run It!
Item-Based Collaborative Filtering in Spark, cache(), and persist()
[Activity] Running the Similar Movies Script using Spark's Cluster Manager
[Exercise] Improve the Quality of Similar Movies
Running Spark on a Cluster
[Activity] Using spark-submit to Run Spark Driver Scripts
[Activity] Packaging Driver Scripts with SBT
Introducing Amazon Elastic MapReduce
Creating Similar Movies from One Million Ratings on EMR
Partitioning
Best Practices for Running on a Cluster
Troubleshooting and Managing Dependencies
SparkSQL, DataFrames, and DataSets
Introduction to SparkSQL
[Activity] Using SparkSQL
[Activity] Using DataFrames and DataSets
[Activity] Using DataSets Instead of RDDs
Machine Learning with MLLib
Introducing MLLib
[Activity] Using MLLib to Produce Movie Recommendations
[Activity] Linear Regression with MLLib
[Activity] Using DataFrames with MLLib
Intro to Spark Streaming
Spark Streaming Overview
[Activity] Set Up a Twitter Developer Account, and Stream Tweets
Structured Streaming
Intro to GraphX
GraphX, Pregel, and breadth-first search with Pregel.
[Activity] Superhero Degrees of Separation using GraphX
You Made It! Where to Go from Here?
Learning More, and Career Tips

Video Details

ISBN 139781787129849
Course Length7 hours 4 minutes
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From 1 reviews

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