Learn Spark and Hadoop Overnight on GCP

Learn Spark and Hadoop Overnight on GCP, available at $39.99, has an average rating of 3.7, with 41 lectures, based on 21 reviews, and has 1456 subscribers.

You will learn about For E-Commerce Data Load and Operation Setting Up Hadoop and Spark Up and Running With Spark on GCP This course is ideal for individuals who are Hadoop Learners or Hadoop Developers It is particularly useful for Hadoop Learners or Hadoop Developers.

Enroll now: Learn Spark and Hadoop Overnight on GCP

Summary

Title: Learn Spark and Hadoop Overnight on GCP

Price: $39.99

Average Rating: 3.7

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

For E-Commerce Data Load and Operation Setting Up Hadoop and Spark
Up and Running With Spark on GCP

Who Should Attend

Hadoop Learners
Hadoop Developers

Target Audiences

Hadoop Learners
Hadoop Developers

This is a comprehensive hands on course on Spark Hadoop

In this course we focused on Big Data and open source solutions around that.�
We require these tools for our E-commerce end of Project CORE (Create your Own Recommendation Engine) is one of its kind of project to learn technology End-to-End
We will explore Hadoop one of the prominent Big Data solution
We will look Why part and How part of it and its ecosystem, its Architecture and basic inner working and will also spin our first Hadoop under 2 min in Google Cloud
This particular course we are going to use in Project CORE which is comprehensive project on hands on technologies. In Project CORE you will learn more about Building you own system on Big Data, Spark, Machine Learning, SAPUI5, Angular4, D3JS, SAP® HANA®
With this Course you will get a brief understanding on Apache Spark™, Which is a fast and general engine for large-scale data processing.�
Spark is used in Project CORE to manage Big data with HDFS file system, We are storing 1.5 million records of books in spark and implementing collaborative filtering algorithm.�
Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python and R shells.�
Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.�
Runs Everywhere – Spark runs on Hadoop, Mesos, standalone, or in the cloud. �

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to E-Commerce Data Load and Operation Setting Up Hadoop & Spark

Lecture 2: Data Explosion and Reduction in Storage Cost

Lecture 3: When Data is Referred as Big Data

Lecture 4: Computer Science Behind Big Data Processing

Lecture 5: Hot and Cold Data

Lecture 6: Hadoop Architecture

Lecture 7: Hadoop Cluster Data Operation

Lecture 8: High Availability and Replication for Enterprise Part 1

Lecture 9: High Availability and Replication for Enterprise Part 2

Lecture 10: GCP Dataproc and Modern Big Data Lifecycle .mp4

Lecture 11: Data Load into HDFS or Storage Bucket

Lecture 12: Configuring and Running Hadoop in GCP with Dataproc

Lecture 13: SSH Inside the Master Node and HDFS Files System

Lecture 14: Summary – Part 1

Chapter 2: Up and Running With Spark on GCP

Lecture 1: Introduction to Up and Running With Spark on GCP – Part 2

Lecture 2: SSH to Google VM Instance from Local Machine

Lecture 3: Processing With Spark

Lecture 4: Spark Inner Working – Part 1

Lecture 5: Spark Inner Working – Part 2

Lecture 6: Running Spark on Google® Cloud Platform

Lecture 7: Launching Jupyter Notebook in Spark

Lecture 8: A Simple Introduction to RDD

Lecture 9: RDD Architecture in Spark

Lecture 10: RDD Transformation and Actions: Map and Collect Respectively – Part 1

Lecture 11: RDD Transformation and Actions: Map and Collect Respectively – Part 2

Lecture 12: RDD Transformation and Actions – Filter and Collect Respectively

Lecture 13: RDD Transformation and Actions – Filters and Collect

Lecture 14: RDD Transformation and Actions – Collect and Reduce

Lecture 15: RDD Advance Transformation and Actions – Flatmaps

Lecture 16: RDD Advance Transformation and Actions – FlatMaps Example

Lecture 17: RDD Advance Transformation and Actions – groupByKey and reduceByKey Basics

Lecture 18: RDD Advance Transformation and Actions – groupByKey and reduceByKey Example

Lecture 19: RDD Advance Transformation and Actions – groupByKey and reduceByKey Example

Lecture 20: RDD Caching, Persistence and Summary

Lecture 21: Why Dataframe and Basics of Dataframe

Lecture 22: Installing Faker to Generate Random Data for Examples

Lecture 23: Creating Random User Data With Faker and Creating Dataframe

Lecture 24: Working with DataFrame and Understanding Functionalities – Part 1

Lecture 25: Working with DataFrame and Understanding Functionalities – Part 2

Lecture 26: Working with DataFrame and Understanding Functionalities – Part 3

Lecture 27: Working with DataFrame and Understanding Functionalities – Part 4

Instructors

CS PRO
Computer Science for Professional

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 5 votes
4 stars: 10 votes
5 stars: 5 votes

Frequently Asked Questions

How long do I have access to the course materials?

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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