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 Amazon's 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 Spark's Resilient Distributed Datasets to process and analyze large data sets across many CPU's 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 that's 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 you've never written a computer program or a script before, this course isn't 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 you're 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 that's 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 you've never written a computer program or a script before, this course isn't 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 you're 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 Amazon's 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 Spark's Resilient Distributed Datasets to process and analyze large data sets across many CPU's
- 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 that's 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 you've never written a computer program or a script before, this course isn't 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 you're 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 that's 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 you've never written a computer program or a script before, this course isn't 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 you're 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.
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Learn the concepts of Spark’s DataFrames and Resilient Distributed Datastores
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Develop and run Spark jobs quickly using Python and pyspark
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Translate complex analysis problems into iterative or multi-stage Spark scripts
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Scale up to larger data sets using Amazon’s Elastic MapReduce service
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Understand how Hadoop YARN distributes Spark across computing clusters
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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!
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” 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: What's 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 RDD's, and the Average Friends by Age Example
Lecture 6: [Activity] Running the Average Friends by Age Example
Lecture 7: Filtering RDD's, 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 RDD's
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 Spark's 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 URL's
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
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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.
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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