November 09 2020

Apache Spark with Scala useful for Databricks Certification

Apache Spark with Scala useful for Databricks Certification
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 1.97 GB | Duration: 5h 34m

An overview of the architecture of Apache Spark.


What you'll learn

Apache Spark ( Spark Core, Spark SQL, Spark RDD and Spark DataFrame)

Databricks Certification syllabus included in the Course

Work with Apache Spark's primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets.

Develop Apache Spark 3.0 applications using RDD transformations and actions and Spark SQL.

Analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding about Spark SQL.

Requirements

Some programming experience is required and Scala fundamental knowledge is also required , 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.

Any flavor of Operating System is fine.

Description

Apache Spark with Scala useful for Databricks Certification(Unofficial)

Apache Spark with Scala its a Crash Course for Databricks Certification Enthusiast (Unofficial) for bners

"Big data" analysis is a hot and highly valuable skill - and this course will teach you the hottest technology in big dаta: Apache Spark. Employers including , eBay, NASA, Yahoo, and many more. All are using Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You'll learn those same techniques, using your own Operating system right at home.

So, What are we going to cover in this course then?

Learn and master the art of framing data analysis problems as Spark problems through over 30+ hands-on examples, and then execute them up to run on Databricks cloud computing services (Free Service) in this course. Well, the course is covering topics which are included for certification:

1) Spark Architecture Components

Driver,

Core/Slots/Threads,

Executor

Partitions

2) Spark Execution

Jobs

Tasks

Stages

3) Spark Concepts

Caching,

DataFrame Transformations vs. Actions, Shuffling

Partitioning, Wide vs. Narrow Transformations

4) DataFrames API

DataFrameReader

DataFrameWriter

DataFrame [Dataset]

5) Row & Column (DataFrame)

6) Spark SQL Functions

In order to get started with the course And to do that you're going to have to set up your environment.

So, the first thing you're going to need is a web browser that can be (Google Chrome or Firefox, or Safari, or Microsoft Edge (Latest version)) on Windows, Linux, and macOS desktop

This is completely Hands-on Learning with the Databricks environment.

Who this course is for:

Apache Spark Bners, Bner Apache Spark Developer, Bigdata Eeers or Developers, Software Developer, Machine Learning Eeer, Data Scientist



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