Apache Spark 2.0 with Java -Learn Spark from a Big Data Guru

Apache Spark 2.0 with Java -Learn Spark from a Big Data Guru, available at $69.99, has an average rating of 4.53, with 60 lectures, based on 3261 reviews, and has 22660 subscribers.

You will learn about 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. This course is ideal for individuals who are Anyone who want to fully understand how Apache Spark technology works and learn how Apache Spark is being used in the field. or Software engineers who want to develop Apache Spark 2.0 applications using Spark Core and Spark SQL. or Data scientists or data engineers who want to advance their career by improving their big data processing skills. It is particularly useful for Anyone who want to fully understand how Apache Spark technology works and learn how Apache Spark is being used in the field. or Software engineers who want to develop Apache Spark 2.0 applications using Spark Core and Spark SQL. or Data scientists or data engineers who want to advance their career by improving their big data processing skills.

Enroll now: Apache Spark 2.0 with Java -Learn Spark from a Big Data Guru

Summary

Title: Apache Spark 2.0 with Java -Learn Spark from a Big Data Guru

Price: $69.99

Average Rating: 4.53

Number of Lectures: 60

Number of Published Lectures: 60

Number of Curriculum Items: 60

Number of Published Curriculum Objects: 60

Original Price: $199.99

Quality Status: approved

Status: Live

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.

Who Should Attend

Anyone who want to fully understand how Apache Spark technology works and learn how Apache Spark is being used in the field.
Software engineers who want to develop Apache Spark 2.0 applications using Spark Core and Spark SQL.
Data scientists or data engineers who want to advance their career by improving their big data processing skills.

Target Audiences

Anyone who want to fully understand how Apache Spark technology works and learn how Apache Spark is being used in the field.
Software engineers who want to develop Apache Spark 2.0 applications using Spark Core and Spark SQL.
Data scientists or data engineers who want to advance their career by improving their big data processing skills.

What is this course about:

This course covers all the fundamentals about Apache Spark with Java and teaches you everything you need to know about developing Spark applications with Java. 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 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.

What will you learn from this lecture:

In particularly, you will learn:

An overview of the architecture of Apache Spark.
Develop Apache Spark 2.0 applications with Java using RDD transformations and actions and Spark SQL.
Work with Apache Spark’s primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets.
Deep dive into advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs.
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 of Spark SQL.
Share information across different nodes on an Apache Spark cluster by broadcast variables and accumulators.
Best practices of working with Apache Spark in the field.
Big data ecosystem overview.

Why shall we learn Apache Spark:

Apache Spark gives us unlimited ability to build cutting-edge applications. It is also one of the most compelling technologies of the last decade in terms of its disruption to the big data world.

Spark provides in-memory cluster computing which greatly boosts the speed of iterative algorithms and interactive data mining tasks.

Apache Spark is the next-generation processing engine for big data.

Tons of companies are adapting Apache Spark to extract meaning from massive data sets, today you have access to that same big data technology right on your desktop.

Apache Spark is becoming a must tool for big data engineers and data scientists.

About the author:

Since 2015, James has been helping his company to adapt Apache Spark for building their big data processing pipeline and data analytics applications.

James’ company has gained massive benefits by adapting Apache Spark in production. In this course, he is going to share with you his years of knowledge and best practices of working with Spark in the real field.

Why choosing this course?

This course is very hands-on, James has put lots effort to provide you with not only the theory but also real-life examples of developing Spark applications that you can try out on your own laptop.

James has uploaded all the source code to Github and you will be able to follow along with either Windows, MAC OS or Linux.

In the end of this course, James is confident that you will gain in-depth knowledge about Spark and general big data analysis and data manipulation skills. You’ll be able to develop Spark application that analyzes Gigabytes scale of data both on your laptop, and in the cloud using Amazon’s Elastic MapReduce service!

30-day Money-back Guarantee!

You will get 30-day money-back guarantee from Udemy for this course.

If not satisfied simply ask for a refund within 30 days. You will get a full refund. No questions whatsoever asked.

Are you ready to take your big data analysis skills and career to the next level, take this course now!

You will go from zero to Spark hero in 4 hours.

Course Curriculum

Chapter 1: Get Started with Apache Spark

Lecture 1: Course Overview

Lecture 2: How to Take this Course and How to Get Support

Lecture 3: Text Lecture: How to Take this Course and How to Get Support

Lecture 4: Introduction to Spark

Lecture 5: Sides

Lecture 6: Java 9 Warning

Lecture 7: Install Java and Git

Lecture 8: Source Code

Lecture 9: Set up Spark project with IntelliJ IDEA

Lecture 10: Set up Spark project with Eclipse

Lecture 11: Text lecture: Set up Spark project with Eclipse

Lecture 12: Run our first Spark job

Lecture 13: Trouble shooting: running Hadoop on Windows

Chapter 2: RDD

Lecture 1: RDD Basics

Lecture 2: Create RDDs

Lecture 3: Text Lecture: Create RDDs

Lecture 4: Map and Filter Transformation

Lecture 5: Solution to Airports by Latitude Problem

Lecture 6: FlatMap Transformation

Lecture 7: Text Lectures: flatMap Transformation

Lecture 8: Set Operation

Lecture 9: Sampling With Replacement and Sampling Without Replacement

Lecture 10: Solution for the Same Hosts Problem

Lecture 11: Actions

Lecture 12: Solution to Sum of Numbers Problem

Lecture 13: Important Aspects about RDD

Lecture 14: Summary of RDD Operations

Lecture 15: Caching and Persistence

Chapter 3: Spark Architecture and Components

Lecture 1: Spark Architecture

Lecture 2: Spark Components

Chapter 4: Pair RDD

Lecture 1: Introduction to Pair RDD

Lecture 2: Create Pair RDDs

Lecture 3: Filter and MapValue Transformations on Pair RDD

Lecture 4: Reduce By Key Aggregation

Lecture 5: Sample solution for the Average House problem

Lecture 6: Group By Key Transformation

Lecture 7: Sort By Key Transformation

Lecture 8: Sample Solution for the Sorted Word Count Problem

Lecture 9: Data Partitioning

Lecture 10: Join Operations

Lecture 11: Extra Learning Material: How are Big Companies using Apache Spark

Chapter 5: Advanced Spark Topic

Lecture 1: Accumulators

Lecture 2: Text Lecture: Accumulators

Lecture 3: Solution to StackOverflow Survey Follow-up Problem

Lecture 4: Broadcast Variables

Chapter 6: Spark SQL

Lecture 1: Introduction to Spark SQL

Lecture 2: Spark SQL in Action

Lecture 3: Spark SQL practice: House Price Problem

Lecture 4: Spark SQL Joins

Lecture 5: Strongly Typed Dataset

Lecture 6: Use Dataset or RDD

Lecture 7: Dataset and RDD Conversion

Lecture 8: Performance Tuning of Spark SQL

Lecture 9: Extra Learning Material: Avoid These Mistakes While Writing Apache Spark Program

Chapter 7: Running Spark in a Cluster

Lecture 1: Introduction to Running Spark in a Cluster

Lecture 2: Package Spark Application and Use spark-submit

Lecture 3: Run Spark Application on Amazon EMR (Elastic MapReduce) cluster

Chapter 8: Additional Learning Materials

Lecture 1: Future Learning

Lecture 2: Text Lecture: Future Learning

Lecture 3: Coupons to Our Other Courses

Instructors

Tao W.
Software engineer
James Lee
Silicon Valley Software Engineer
Level Up
Your Professional Learning Partner
Jiarui Zhou
Instructor at Udemy, top 10% in Euclid Contest

Rating Distribution

1 stars: 37 votes
2 stars: 51 votes
3 stars: 285 votes
4 stars: 1024 votes
5 stars: 1863 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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