Streaming Analytics
Description
“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
What will you learn
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Define and understand the basics oops concepts and REPL in scala programming
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Obtain the output by performing the various spark operations using spark shell.
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Perform and analyse with RDD functions to load and save data
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Implement the spark streaming data in real time
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Extract the data by spark SQL and visualize to analyse through GraphX library in spark
Requirements
- Some prior programming or scripting experience is required. A crash course in Scala is included, but you need to know the fundamentals of programming in order to pick it up.
- You will need a desktop PC and an Internet connection. The course is created with Windows in mind, but users comfortable with MacOS or Linux can use the same tools.
- The software needed for this course is freely available, and I'll walk you through downloading and installing it.
Lessons
- 36 Lessons
- 37:22:08 Hours
- Syllabus
- Introduction to Big Data Analytics I00:49:59
- Introduction to Big Data Analytics II00:59:41
- Fundamentals Architecture of Hadoop and Ecosystems00:57:23
- Linux Basic Commands00:52:44
- Hadoop Commands Basic00:56:46
- Hadoop Map Reduce00:50:45
- Haddop WordCount MapReduce01:03:38
- Assignment I
- Part 1 Revision01:29:59
- Spark Eco System00:50:33
- Introduction to Scala00:34:05
- Scala REPL00:57:02
- Scala REPL Collection00:50:04
- Scala REPL Arrary Collection00:33:10
- Running Spark in IDE Environment Setup01:22:12
- Transformation Functions and SBT Layout01:27:59
- SBT Word Count00:28:08
- Transformation Persistence and Spark Submit01:30:02
- Spark Submit Word Count01:29:45
- Transformation Functions and SBT Layout
- Introduction to SparkSQL01:18:11
- Spark Aggregate Function in Structured Data01:11:07
- Spark Broadcast and Accumulator Variables01:26:54
- WordCount Average of Key Values01:26:58
- WordCount and Swap Key Value01:26:27
- Word Count - Saved document with Streaming Data01:28:17
- Load Json and Parqute File01:29:01
- SparkSQL Commands01:20:29
- SparkSQL Join in Spark Shell and IDE 01:27:47
- Introduction to Spark MLlib00:57:51
- Introduction to Spark MLlib continued00:28:52
- Spark MLlib Part 101:31:09
- Spark MLlib Part 200:57:51
- Introduction to Spark Streaming01:27:54
- Streaming Network Word Count01:19:25
About instructor
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