Apache Spark with Scala - Learn Spark from a Big Data Guru [Video]

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Apache Spark with Scala - Learn Spark from a Big Data Guru [Video]

James Lee , Tao W
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Learn Apache Spark and Scala by 12+ hands-on examples of analyzing big data
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Video Details

ISBN 139781789134537
Course Length3 hours and 16 minutes

Video Description

This course covers all the fundamentals of Apache Spark with Scala and teaches you everything you need to know about developing Spark applications with Scala. At the end of this course, you will gain in-depth knowledge about Apache Spark and general big data analysis and manipulations skills to help your company to adapt Apache Spark for building a big data processing pipeline and data analytics applications. This course covers 10+ hands-on big data examples. You will learn valuable knowledge about how to frame data analysis problems as Spark problems. Together we will learn examples such as aggregating NASA Apache web logs from different sources; we will explore the price trend by looking at the real estate data in California; we will write Spark applications to find out the median salary of developers in different countries through the Stack Overflow survey data; we will develop a system to analyze how maker spaces are distributed across different regions in the United Kingdom, and much, much more. This course is taught in Scala. Scala is the next generation programming language for functional programming that is growing in popularity and it is one of the most widely used languages in the industry to write Spark programs. Let's learn how to write Spark programs with Scala to model big data problems today!

Style and Approach

This course covers 10+ hands-on big data examples involving Apache Spark. You will learn valuable knowledge about how to frame data analysis problems as Scala Spark problems.

Table of Contents

Get Started with Apache Spark
Course Overview
Introduction to Spark
Install Java and Git
Set up Spark project with IntelliJ IDEA
Run our first Apache Spark job
Trouble Shooting: Run our first Apache Spark job
RDD
RDD Basics in Apache Spark
Create RDDs
Map and Filter Transformation in Apache Spark
Solution to Airports by Latitude Problem
FlatMap Transformation in Apache Spark
Set Operation in Apache Spark
Solution for the Same Hosts Problem
Actions in Apache Spark
Solution to Sum of Numbers Problem
Important Aspects about RDD
Summary of RDD Operations in Apache Spark
Caching and Persistence in Apache Spark
Spark Architecture and Components
Spark Architecture
Spark Components
Pair RDD in Apache Spark
Introduction to Pair RDD in Spark
Create Pair RDDs in Spark
Filter and MapValue Transformations on Pair RDD
Reduce By Key Aggregation in Apache Spark
Sample solution for the Average House problem
GroupBy Key Transformation in Spark
SortBy Key Transformation in Spark
Sample Solution for the Sorted Word Count Problem
Data Partitioning in Apache Spark
Join Operations in Spark
Advanced Spark Topic
Accumulators
Solution to StackOverflow Survey Follow-up Problem
Broadcast Variables
Apache Spark SQL
Introduction to Apache Spark SQL
Spark SQL in Action
Spark SQL practice: House Price Problem
Spark SQL Joins
Strongly Typed Dataset
Use Dataset or RDD
Dataset and RDD Conversion
Performance Tuning of Spark SQL
Running Spark in a Cluster
Introduction to Running Spark in a Cluster
Package Spark Application and Use spark-submit
Run Spark Application on Amazon EMR (Elastic MapReduce) cluster

What You Will Learn

  • An overview of the architecture of Apache Spark.
  • Work with Apache Spark's primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets.
  • Develop Apache Spark 2.0 applications using RDD transformations and actions and Spark SQL.
  • Scale up Spark applications on a Hadoop YARN cluster through Amazon's Elastic MapReduce service.
  • Analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding about Spark SQL.
  • Share information across different nodes on a Apache Spark cluster by broadcast variables and accumulators.
  • Advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs.
  • Best practices of working with Apache Spark in the field.

Authors

Table of Contents

Get Started with Apache Spark
Course Overview
Introduction to Spark
Install Java and Git
Set up Spark project with IntelliJ IDEA
Run our first Apache Spark job
Trouble Shooting: Run our first Apache Spark job
RDD
RDD Basics in Apache Spark
Create RDDs
Map and Filter Transformation in Apache Spark
Solution to Airports by Latitude Problem
FlatMap Transformation in Apache Spark
Set Operation in Apache Spark
Solution for the Same Hosts Problem
Actions in Apache Spark
Solution to Sum of Numbers Problem
Important Aspects about RDD
Summary of RDD Operations in Apache Spark
Caching and Persistence in Apache Spark
Spark Architecture and Components
Spark Architecture
Spark Components
Pair RDD in Apache Spark
Introduction to Pair RDD in Spark
Create Pair RDDs in Spark
Filter and MapValue Transformations on Pair RDD
Reduce By Key Aggregation in Apache Spark
Sample solution for the Average House problem
GroupBy Key Transformation in Spark
SortBy Key Transformation in Spark
Sample Solution for the Sorted Word Count Problem
Data Partitioning in Apache Spark
Join Operations in Spark
Advanced Spark Topic
Accumulators
Solution to StackOverflow Survey Follow-up Problem
Broadcast Variables
Apache Spark SQL
Introduction to Apache Spark SQL
Spark SQL in Action
Spark SQL practice: House Price Problem
Spark SQL Joins
Strongly Typed Dataset
Use Dataset or RDD
Dataset and RDD Conversion
Performance Tuning of Spark SQL
Running Spark in a Cluster
Introduction to Running Spark in a Cluster
Package Spark Application and Use spark-submit
Run Spark Application on Amazon EMR (Elastic MapReduce) cluster

Video Details

ISBN 139781789134537
Course Length3 hours and 16 minutes
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