A Big Data Hadoop and Spark project for absolute beginners

A Big Data Hadoop and Spark project for absolute beginners
.MP4 | Video: 1280×720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 1.38 GB
Duration: 4 hours | Genre: eLearning Video | Language: English
Learn Hadoop, Spark, Dataproc , AWS S3 Data Lake, Glue, Athena services and Machine Learning using a real world use case.

What you’ll learn

Big Data , Hadoop and Spark from scratch. You will also learn how to use free cloud tools to get started with Hadoop and Spark programming in minutes. Additionally you will find two bonus projects on AWS data lake solution and Machine Learning Classification model

Requirements

Students should have some programming background and some knowledge of SQL queries.

Description

A bank is launching a new credit card and wants to identify prospects it can target in its marketing campaign.

It has received prospect data from various internal and 3rd party sources. The data has various issues such as missing or unknown values in certain fields.The data needs to be cleansed before any kind of analysis can be done.

Since the data is in huge volume with billions of records, the bank has asked you to use Big Data Hadoop and Spark technology to cleanse, transform and analyze this data.

What you will learn :

Big Data, Hadoop concepts

How to create a free Hadoop and Spark cluster using Google Dataproc

Hadoop hands-on – HDFS, Hive

Why there was a need for Spark

Python basics

PySpark RDD – hands-on

PySpark SQL, DataFrame – hands-on

Project work using PySpark and Hive

Google Colab environment

Bonus project – Applying spark transformation on data stored in AWS S3 using Glue and viewing data using Athena

Bonus project – Build your first Machine Learning model using Python, Scikit-learn to predict whether a customer will buy or not.

Prerequisites :

Some basic programming skills

Some knowledge of SQL queries

Who this course is for:

Beginners who want to learn Big Data or experienced people who want to transition to a Big Data role

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