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

Learn analyzing large data sets with Apache Spark by 10+ hands-on examples. Take your big data skills to the next level.

Created byTao W., James Lee, Level Up, Jiarui Zhou

Last updated 9/2018

English

What you'll 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.

Course content

8 sections • 60 lectures • 3h 26m total length

Course Overview4:08
How to Take this Course and How to Get Support1:24
Learn how to take this course and get support through a GitHub repository with up-to-date copy-and-paste commands, via the Q&A page and Facebook group Learning Big Data and Level Up.
Text Lecture: How to Take this Course and How to Get Support0:02
Introduction to Spark2:21
Sides0:04
Java 9 Warning0:03
Install Java and Git4:11
Source Code0:06
Set up Spark project with IntelliJ IDEA7:22
Set up Spark project with Eclipse2:04
Set up a Spark project in Eclipse by importing a Gradle project from the Spark tutorial repository and synchronizing Gradle, then run the wordCount example.
Text lecture: Set up Spark project with Eclipse0:07
Run our first Spark job2:43
Trouble shooting: running Hadoop on Windows0:34

RDD Basics2:39
Create RDDs2:26
Text Lecture: Create RDDs0:03
Map and Filter Transformation8:38
Solution to Airports by Latitude Problem1:31
Load airport data, parse latitude, filter latitudes above 40, map to airport names and latitudes, and save the results to an output file using Spark.
FlatMap Transformation6:27
Explore flatMap transformation in Spark, producing multiple outputs per input via an iterator, and compare it with map in a wordcount example, including lambda usage.
Text Lectures: flatMap Transformation0:01
Set Operation7:37
Explore Spark set operations on RDDs, including sample and distinct, plus union, intersection, subtract, and Cartesian, with emphasis on shuffling, duplicates, and practical log-file examples.
Sampling With Replacement and Sampling Without Replacement0:02
Solution for the Same Hosts Problem0:30
Actions8:06
Explore actions on RDDs in Apache Spark and how they return final results or persist data, including collect, count, take, save as text file, and reduce.
Solution to Sum of Numbers Problem1:43
Important Aspects about RDD1:28
Rdds are distributed across a cluster and partitioned for parallel processing. They are immutable and resilient, deterministic functions of their input that enable automatic recovery by Spark when nodes fail.
Summary of RDD Operations2:23
Caching and Persistence5:09
Spark persistence caches an RDD in memory or disk across actions, using storage levels (memory only, memory and disk, serialized or deserialized) to balance speed and memory, with LRU eviction.

Introduction to Pair RDD1:32
Create Pair RDDs3:54
Filter and MapValue Transformations on Pair RDD4:52
Reduce By Key Aggregation5:14
Explore reduce by key aggregation on paired RDDs to count word frequencies, using a (word, 1) map and a reducing function to sum values across keys.
Sample solution for the Average House problem3:16
Compute the average house price per number of bedrooms using Spark's reduceByKey on a paired RDD. Use an average count structure with total price and count to derive averages.
Group By Key Transformation4:42
Sort By Key Transformation2:49
Sample Solution for the Sorted Word Count Problem2:00
Demonstrates a Spark-based solution for the sorted word count problem by flipping key-value pairs, sorting by count in descending order, and flipping back to word-count pairs.
Data Partitioning4:12
Leverage hash partitioning with a partitioner to keep the same keys on the same node, reduce shuffle, and persist results to avoid re-partitioning during joins and reduce by key.
Join Operations4:56
Extra Learning Material: How are Big Companies using Apache Spark0:08

Introduction to Spark SQL3:48
Spark SQL in Action14:43
Spark SQL practice: House Price Problem1:52
Spark SQL Joins6:20
Learn spark sql joins, including inner, left outer, right outer, and left semi joins, and how the catalyst optimizer improves join performance when mapping makerspace data by postcode to regions.
Strongly Typed Dataset8:31
Use Dataset or RDD2:56
Dataset and RDD Conversion2:58
Performance Tuning of Spark SQL2:44
Extra Learning Material: Avoid These Mistakes While Writing Apache Spark Program0:02

Introduction to Running Spark in a Cluster4:09
Explore how Spark runs in cluster mode with a driver and executors, managed by a cluster manager. Learn to submit applications with spark-submit across standalone, YARN, and Mesos clusters.
Package Spark Application and Use spark-submit8:08
Export your spark application to a jar with all dependencies using Gradle, then submit it to a local spark cluster via spark-submit in standalone mode.
Run Spark Application on Amazon EMR (Elastic MapReduce) cluster13:31

Requirements

A computer running Windows, OSX or Linux
Previous Java programming skills
Java 8 experience is preferred but NOT required

Description

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.

Who this course is for:

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.

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

What you'll learn

Explore related topics

Course content

Get Started with Apache Spark13 lectures • 25min

RDD15 lectures • 49min

Spark Architecture and Components2 lectures • 8min

Pair RDD11 lectures • 38min

Advanced Spark Topic4 lectures • 14min

Spark SQL9 lectures • 44min

Running Spark in a Cluster3 lectures • 26min

Additional Learning Materials3 lectures • 4min

Requirements

Description

Who this course is for: