Taming Big Data with Apache Spark and Python Hands On!

Taming Big Data with Apache Spark and Python – Hands On!, available at $119.99, has an average rating of 4.43, with 68 lectures, based on 16268 reviews, and has 101999 subscribers.

You will learn about Use DataFrames and Structured Streaming in Spark 3 Use the MLLib machine learning library to answer common data mining questions Understand how Spark Streaming lets your process continuous streams of data in real time Frame big data analysis problems as Spark problems Use Amazons Elastic MapReduce service to run your job on a cluster with Hadoop YARN Install and run Apache Spark on a desktop computer or on a cluster Use Sparks Resilient Distributed Datasets to process and analyze large data sets across many CPUs Implement iterative algorithms such as breadth-first-search using Spark Understand how Spark SQL lets you work with structured data Tune and troubleshoot large jobs running on a cluster Share information between nodes on a Spark cluster using broadcast variables and accumulators Understand how the GraphX library helps with network analysis problems This course is ideal for individuals who are People with some software development background who want to learn the hottest technology in big data analysis will want to check this out. This course focuses on Spark from a software development standpoint; we introduce some machine learning and data mining concepts along the way, but thats not the focus. If you want to learn how to use Spark to carve up huge datasets and extract meaning from them, then this course is for you. or If youve never written a computer program or a script before, this course isnt for you – yet. I suggest starting with a Python course first, if programming is new to you. or If your software development job involves, or will involve, processing large amounts of data, you need to know about Spark. or If youre training for a new career in data science or big data, Spark is an important part of it. It is particularly useful for People with some software development background who want to learn the hottest technology in big data analysis will want to check this out. This course focuses on Spark from a software development standpoint; we introduce some machine learning and data mining concepts along the way, but thats not the focus. If you want to learn how to use Spark to carve up huge datasets and extract meaning from them, then this course is for you. or If youve never written a computer program or a script before, this course isnt for you – yet. I suggest starting with a Python course first, if programming is new to you. or If your software development job involves, or will involve, processing large amounts of data, you need to know about Spark. or If youre training for a new career in data science or big data, Spark is an important part of it.

Enroll now: Taming Big Data with Apache Spark and Python – Hands On!

Summary

Title: Taming Big Data with Apache Spark and Python – Hands On!

Price: $119.99

Average Rating: 4.43

Number of Lectures: 68

Number of Published Lectures: 66

Number of Curriculum Items: 68

Number of Published Curriculum Objects: 66

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Use DataFrames and Structured Streaming in Spark 3

Use the MLLib machine learning library to answer common data mining questions

Understand how Spark Streaming lets your process continuous streams of data in real time

Frame big data analysis problems as Spark problems

Use Amazons Elastic MapReduce service to run your job on a cluster with Hadoop YARN

Install and run Apache Spark on a desktop computer or on a cluster

Use Sparks Resilient Distributed Datasets to process and analyze large data sets across many CPUs

Implement iterative algorithms such as breadth-first-search using Spark

Understand how Spark SQL lets you work with structured data

Tune and troubleshoot large jobs running on a cluster

Share information between nodes on a Spark cluster using broadcast variables and accumulators

Understand how the GraphX library helps with network analysis problems

Who Should Attend

People with some software development background who want to learn the hottest technology in big data analysis will want to check this out. This course focuses on Spark from a software development standpoint; we introduce some machine learning and data mining concepts along the way, but thats not the focus. If you want to learn how to use Spark to carve up huge datasets and extract meaning from them, then this course is for you.

If youve never written a computer program or a script before, this course isnt for you – yet. I suggest starting with a Python course first, if programming is new to you.

If your software development job involves, or will involve, processing large amounts of data, you need to know about Spark.

If youre training for a new career in data science or big data, Spark is an important part of it.

Target Audiences

People with some software development background who want to learn the hottest technology in big data analysis will want to check this out. This course focuses on Spark from a software development standpoint; we introduce some machine learning and data mining concepts along the way, but thats not the focus. If you want to learn how to use Spark to carve up huge datasets and extract meaning from them, then this course is for you.

If youve never written a computer program or a script before, this course isnt for you – yet. I suggest starting with a Python course first, if programming is new to you.

If your software development job involves, or will involve, processing large amounts of data, you need to know about Spark.

If youre training for a new career in data science or big data, Spark is an important part of it.

New! Updated for Spark 3, more hands-on exercises, and a stronger focus on DataFrames and Structured Streaming.

“Big data” analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Sparkand specifically PySpark. Employers including Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You’ll learn those same techniques, using your own Windows system right at home. It’s easier than you might think.

Learn and master the art of framing data analysis problems as Spark problems through over 20 hands-on examples, and then scale them up to run on cloud computing services in this course. You’ll be learning from an ex-engineer and senior manager from Amazon and IMDb.

Learn the concepts of Spark’s DataFrames and Resilient Distributed Datastores

Develop and run Spark jobs quickly using Python and pyspark

Translate complex analysis problems into iterative or multi-stage Spark scripts

Scale up to larger data sets using Amazon’s Elastic MapReduce service

Understand how Hadoop YARN distributes Spark across computing clusters

Learn about other Spark technologies, like Spark SQL, Spark Streaming, and GraphX

By the end of this course, you’ll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes. 

This course uses the familiar Python programming language; if you’d rather use Scala to get the best performance out of Spark, see my “Apache Spark with Scala – Hands On with Big Data” course instead.

We’ll have some fun along the way. You’ll get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you’ve got the basics under your belt, we’ll move to some more complex and interesting tasks. We’ll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We’ll analyze a social graph of superheroes, and learn who the most “popular” superhero is – and develop a system to find “degrees of separation” between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You’ll find the answer.

This course is very hands-on; you’ll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon’s Elastic MapReduce service. 7 hours of video content is included, with over 20 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX.

Wrangling big data with Apache Spark is an important skill in today’s technical world. Enroll now!

” I studied “Taming Big Data with Apache Spark and Python” with Frank Kane, and helped me build a great platform for Big Data as a Service for my company. I recommend the course!  ” – Cleuton Sampaio De Melo Jr.

Course Curriculum

Chapter 1: Getting Started with Spark

Lecture 1: Introduction

Lecture 2: How to Use This Course

Lecture 3: Udemy 101: Getting the Most From This Course

Lecture 4: Important note

Lecture 5: IMPORTANT! UPDATES TO SPARK SETUP STEPS

Lecture 6: [Activity]Getting Set Up: Installing Python, a JDK, Spark, and its Dependencies.

Lecture 7: Alternate MovieLens download location

Lecture 8: [Activity] Installing the MovieLens Movie Rating Dataset

Lecture 9: [Activity] Run your first Spark program! Ratings histogram example.

Chapter 2: Spark Basics and the RDD Interface

Lecture 1: Whats new in Spark 3?

Lecture 2: Introduction to Spark

Lecture 3: The Resilient Distributed Dataset (RDD)

Lecture 4: Ratings Histogram Walkthrough

Lecture 5: Key/Value RDDs, and the Average Friends by Age Example

Lecture 6: [Activity] Running the Average Friends by Age Example

Lecture 7: Filtering RDDs, and the Minimum Temperature by Location Example

Lecture 8: [Activity]Running the Minimum Temperature Example, and Modifying it for Maximums

Lecture 9: [Activity] Running the Maximum Temperature by Location Example

Lecture 10: [Activity] Counting Word Occurrences using flatmap()

Lecture 11: [Activity] Improving the Word Count Script with Regular Expressions

Lecture 12: [Activity] Sorting the Word Count Results

Lecture 13: [Exercise] Find the Total Amount Spent by Customer

Lecture 14: [Excercise] Check your Results, and Now Sort them by Total Amount Spent.

Lecture 15: Check Your Sorted Implementation and Results Against Mine.

Chapter 3: SparkSQL, DataFrames, and DataSets

Lecture 1: Introducing SparkSQL

Lecture 2: [Activity] Executing SQL commands and SQL-style functions on a DataFrame

Lecture 3: Using DataFrames instead of RDDs

Lecture 4: [Exercise] Friends by Age, with DataFrames

Lecture 5: Exercise Solution: Friends by Age, with DataFrames

Lecture 6: [Activity] Word Count, with DataFrames

Lecture 7: [Activity] Minimum Temperature, with DataFrames (using a custom schema)

Lecture 8: [Exercise] Implement Total Spent by Customer with DataFrames

Lecture 9: Exercise Solution: Total Spent by Customer, with DataFrames

Chapter 4: Advanced Examples of Spark Programs

Lecture 1: [Activity] Find the Most Popular Movie

Lecture 2: [Activity] Use Broadcast Variables to Display Movie Names Instead of ID Numbers

Lecture 3: Find the Most Popular Superhero in a Social Graph

Lecture 4: [Activity] Run the Script – Discover Who the Most Popular Superhero is!

Lecture 5: [Exercise] Find the Most Obscure Superheroes

Lecture 6: Exercise Solution: Most Obscure Superheroes

Lecture 7: Superhero Degrees of Separation: Introducing Breadth-First Search

Lecture 8: Superhero Degrees of Separation: Accumulators, and Implementing BFS in Spark

Lecture 9: [Activity] Superhero Degrees of Separation: Review the Code and Run it

Lecture 10: Item-Based Collaborative Filtering in Spark, cache(), and persist()

Lecture 11: [Activity] Running the Similar Movies Script using Sparks Cluster Manager

Lecture 12: [Exercise] Improve the Quality of Similar Movies

Chapter 5: Running Spark on a Cluster

Lecture 1: Introducing Elastic MapReduce

Lecture 2: [Activity] Setting up your AWS / Elastic MapReduce Account and Setting Up PuTTY

Lecture 3: Partitioning

Lecture 4: Create Similar Movies from One Million Ratings – Part 1

Lecture 5: [Activity] Create Similar Movies from One Million Ratings – Part 2

Lecture 6: Create Similar Movies from One Million Ratings – Part 3

Lecture 7: Troubleshooting Spark on a Cluster

Lecture 8: More Troubleshooting, and Managing Dependencies

Chapter 6: Machine Learning with Spark ML

Lecture 1: Introducing MLLib

Lecture 2: [Activity] Using Spark ML to Produce Movie Recommendations

Lecture 3: Analyzing the ALS Recommendations Results

Lecture 4: [Activity] Linear Regression with Spark ML

Lecture 5: [Exercise] Using Decision Trees in Spark ML to Predict Real Estate Prices

Lecture 6: Exercise Solution: Decision Trees with Spark

Chapter 7: Spark Streaming, Structured Streaming, and GraphX

Lecture 1: Spark Streaming

Lecture 2: [Activity] Structured Streaming in Python

Lecture 3: [Exercise] Use Windows with Structured Streaming to Track Most-Viewed URLs

Lecture 4: Exercise Solution: Using Structured Streaming with Windows

Lecture 5: GraphX

Chapter 8: You Made It! Where to Go from Here.

Lecture 1: Learning More about Spark and Data Science

Lecture 2: Bonus Lecture: More courses to explore!

Instructors

Sundog Education by Frank Kane
Join over 800K students learning ML, AI, AWS, and Data Eng.

Frank Kane
Ex-Amazon Sr. Engineer and Sr. Manager, CEO Sundog Education

Sundog Education Team
Sundog Education Team

Rating Distribution

1 stars: 168 votes

2 stars: 239 votes

3 stars: 1378 votes

4 stars: 5905 votes

5 stars: 8586 votes

Frequently Asked Questions

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

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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!