90% Off Apache Spark 2 with Scala – Hands On with Big Data! | Online Course

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In this course, you’ll learn dive right in with 20+ hands-on examples of analyzing large data sets with Apache Spark, on your desktop or on Hadoop! Enrolling in Course Apache Spark 2 with Scala – Hands On with Big Data! by Frank Kane.

Course: Apache Spark 2 with Scala – Hands On with Big Data!
Author: Frank Kane (A former engineer and the senior manager from Amazon and IMDb, as well as a Data Miner and Software Engineer, holds 17 issued patents in the fields of distributed computing, data mining, and machine learning)
Description: Dive right in with 20+ hands-on examples of analyzing large data sets with Apache Spark, on your desktop or on Hadoop!
Skill Level All Levels
Content: 7.5 hours on-demand video
Lessons: 52 lectures
Languages: English, captions
Includes: Lifetime access
Categories: Business / Data & Analytics
There is no risk: 30 day money back guarantee!
Learn Anywhere Available: iPhone, iPad, Tablets, iOS and Android
Recognition Certificate: Course Certificate of Completion.

Take the course, Apache Spark 2 with Scala – Hands On with Big Data. Here are some of the things you’ll be able to do after taking this course:
Students will develop distributed code using the Scala programming language
Students will optimize Spark jobs through partitioning, caching, and other techniques
Students will build, deploy, and run Spark scripts on Hadoop clusters
Students will process continual streams of data with Spark Streaming
Students will transform structured data using SparkSQL and DataFrames
Students will transform structured data using SparkSQL and DataFrames
Students will traverse and analyze graph structures using GraphX
And more…

Who this course is for:
This course is intended for 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.

Enroll in This Online Course Now!
 


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