Apache Spark with Scala - Hands On with Big Data!
- Offered byUDEMY
Apache Spark with Scala - Hands On with Big Data! at UDEMY Overview
Duration | 9 hours |
Total fee | ₹9,600 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Apache Spark with Scala - Hands On with Big Data! at UDEMY Highlights
- Compatible on Mobile and TV
- Earn a Cerificate on successful completion
- Get Full Lifetime Access
- Course Instructor: Sundog Education by Frank Kane
Apache Spark with Scala - Hands On with Big Data! at UDEMY Course details
- Software engineers who want to expand their skills into the world of big data processing on a cluster
- If you have no previous programming or scripting experience, you'll want to take an introductory programming course first.
- Frame big data analysis problems as Apache Spark scripts
- Develop distributed code using the Scala programming language
- Optimize Spark jobs through partitioning, caching, and other techniques
- Build, deploy, and run Spark scripts on Hadoop clusters
- Process continual streams of data with Spark Streaming
- Transform structured data using SparkSQL and DataFrames
- Traverse and analyze graph structures using GraphX
- New! Updated for Spark 2.3. Big data" analysis is a hot and highly valuable skill and this course will teach you the hottest technology in big data: Apache Spark . 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, and you'll be learning from an ex-engineer and senior manager from Amazon and IMDb. Spark works best when using the Scala programming language, and this course includes a crash-course in Scala to get you up to speed quickly. For those more familiar with Python however, a Python version of this class is also available: "Taming Big Data with Apache Spark and Python - Hands On". 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. Learn the concepts of Spark's Resilient Distributed Datastores Get a crash course in the Scala programming language Develop and run Spark jobs quickly using Scala 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 Practice using other Spark technologies, like Spark SQL, DataFrames, DataSets, 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. 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. Are all Marvel superheroes within a few degrees of being connected to SpiderMan? 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. 7.5 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. Enroll now, and enjoy the course! "I studied Spark for the first time using Frank's course "Apache Spark 2 with Scala - Hands On with Big Data!". It was a great starting point for me, gaining knowledge in Scala and most importantly practical examples of Spark applications. It gave me an understanding of all the relevant Spark core concepts, RDDs, Dataframes & Datasets, Spark Streaming, AWS EMR. Within a few months of completion, I used the knowledge gained from the course to propose in my current company to work primarily on Spark applications. Since then I have continued to work with Spark. I would highly recommend any of Franks courses as he simplifies concepts well and his teaching manner is easy to follow and continue with! " - Joey Faherty
Apache Spark with Scala - Hands On with Big Data! at UDEMY Curriculum
Getting Started
Tip: Apply for a Twitter Developer Account now!
Udemy 101: Getting the Most From This Course
Warning about Java 11 and Spark 3!
Introduction, and Getting Set Up
[Activity] Create a Histogram of Real Movie Ratings with Spark!
Scala Crash Course [Optional]
[Activity] Scala Basics, Part 1
[Exercise] Scala Basics, Part 2
[Exercise] Flow Control in Scala
[Exercise] Functions in Scala
[Exercise] Data Structures in Scala
Spark Basics and Simple Examples
What's New in Spark 3?
Introduction to Spark
The Resilient Distributed Dataset
Ratings Histogram Walkthrough
Spark Internals
Key / Value RDD's, and the Average Friends by Age example
[Activity] Running the Average Friends by Age Example
Filtering RDD's, and the Minimum Temperature by Location Example
[Activity] Running the Minimum Temperature Example, and Modifying it for Maximum
[Activity] Counting Word Occurrences using Flatmap()
[Activity] Improving the Word Count Script with Regular Expressions
[Activity] Sorting the Word Count Results
[Exercise] Find the Total Amount Spent by Customer
[Exercise] Check your Results, and Sort Them by Total Amount Spent
Check Your Results and Implementation Against Mine
Advanced Examples of Spark Programs
[Activity] Find the Most Popular Movie
[Activity] Use Broadcast Variables to Display Movie Names
[Activity] Find the Most Popular Superhero in a Social Graph
Superhero Degrees of Separation: Introducing Breadth-First Search
Superhero Degrees of Separation: Accumulators, and Implementing BFS in Spark
Superhero Degrees of Separation: Review the code, and run it!
Item-Based Collaborative Filtering in Spark, cache(), and persist()
[Activity] Running the Similar Movies Script using Spark's Cluster Manager
[Exercise] Improve the Quality of Similar Movies
Running Spark on a Cluster
[Activity] Using spark-submit to run Spark driver scripts
[Activity] Packaging driver scripts with SBT
Introducing Amazon Elastic MapReduce
Creating Similar Movies from One Million Ratings on EMR
Partitioning
Best Practices for Running on a Cluster
Troubleshooting, and Managing Dependencies
SparkSQL, DataFrames, and DataSets
Introduction to SparkSQL
[Activity] Using SparkSQL
[Activity] Using DataFrames and DataSets
[Activity] Using DataSets instead of RDD's
Machine Learning with MLLib
Introducing MLLib
If you have trouble running the following activity...
[Activity] Using MLLib to Produce Movie Recommendations
[Activity] Linear Regression with MLLib
[Activity] Using DataFrames with MLLib
Intro to Spark Streaming
Spark Streaming Overview
[Activity] Set up a Twitter Developer Account, and Stream Tweets
Structured Streaming
Intro to GraphX
GraphX, Pregel, and Breadth-First-Search with Pregel.
[Activity] Superhero Degrees of Separation using GraphX
You Made It! Where to Go from Here.
Learning More, and Career Tips
Bonus Lecture: More courses to explore!
Bonus Lecture: Discounts on my other "Big Data" / Data Science Courses.
Other courses offered by UDEMY
Student Forum
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