Big Data and Web Scraping with PySpark, AWS, and Scala Online Course

Big Data and Web Scraping with PySpark, AWS, and Scala Online Course

About the Big Data and Web Scraping with PySpark, AWS, and Scala Online Course

This online course on Big Data and Web Scraping with PySpark, AWS, and Scala is divided into four parts:

• Part 1 focuses on Scala skills, covering core concepts and concluding with MapReduce and ETL pipelines using Spark from AWS S3 to AWS RDS, including six mini-projects and a Scala Spark project.
• Part 2 explores PySpark for data analysis, covering Spark RDDs, DataFrames, Spark SQL queries, transformations, and actions. You will also learn about the Spark and Hadoop ecosystems, their architecture, and how to integrate Spark with various AWS services.
• Part 3 delves into data scraping and mining, teaching key concepts like browser execution, server communication, synchronous/asynchronous operations, and tools like Python’s requests module for scraping.
• Part 4 introduces MongoDB and NoSQL databases. You will learn basic MongoDB operations and explore query, project, and update operators. The section concludes with two projects: a CRUD application using Django and MongoDB, and an ETL pipeline using PySpark to dump data into MongoDB.

By the end of this course, you'll be equipped to apply these technologies to real-world data challenges.

Key Benefits

• This course offers a thorough progression from beginner to advanced levels, covering the essential concepts and techniques in data scraping and mining using Python.
• Each concept is explained in detail, accompanied by hands-on examples in Python, Scrapy, Scala, PySpark, and MongoDB, ensuring a clear understanding of real-world applications.
• Gain expertise in handling large datasets by mastering Big Data technologies like PySpark, and learn how to leverage AWS services for scalable data processing and storage.

Target Audience

This course is for beginners interested in developing intelligent solutions, working with real-world data, and bridging theory with practical application. It is ideal for data scientists, machine learning professionals, and dropshipping entrepreneurs. While a foundational understanding of programming, HTML tags, Python, SQL, and Node.js is recommended, no prior experience in data scraping or Scala is necessary to enroll.

Learning Objectives

• Gain hands-on experience in designing and implementing ETL pipelines using Spark to seamlessly transfer data from AWS S3 to AWS RDS, optimizing data workflows and ensuring efficient data integration.
• Dive deep into the Spark and Hadoop ecosystems, exploring their applications, underlying architecture, and how they work together to process large-scale data efficiently.
• Master collaborative filtering techniques in PySpark, a powerful method used for building recommendation systems, enabling you to analyze and predict user preferences.
• Understand the critical difference between synchronous and asynchronous data requests, and learn how to implement them effectively for optimized data scraping and processing.
• Develop a solid understanding of MongoDB’s core operations, including CRUD (Create, Read, Update, Delete), and gain expertise in using query, projection, and update operators for efficient data manipulation and retrieval.
• Learn how to build and deploy robust APIs for performing CRUD operations on MongoDB using Django, enhancing your ability to manage and interact with NoSQL databases in real-world applications.

Course Topics

The Big Data and Web Scraping with PySpark, AWS, and Scala Online Course covers the following topics - 

Part 1: Data Scraping and Mining for Beginners to Pro with Python

1.1 Introduction

○ Importance of Data Scraping

○ Applications of Data Scraping

○ Instructor Introduction

○ Overview of the Course, Scraping Techniques, and Tools

○ Projects Overview

1.2 Python Requests

○ Introduction to Python Requests

○ Hands-On Practice

○ Extracting Quotes Manually

○ Quizzes and Solutions (Authors and Quotes)

○ Pagination Techniques

○ AJAX Requests

1.3 Beautiful Soup (BS4)

○ Introduction to BS4

○ Data Extraction Techniques

○ Attributes of Tags and Multi-Valued Attributes

○ Quizzes and Solutions (Requests vs. BS4, Author Names)

1.4 CSS Selectors

○ Introduction to CSS Selectors

○ Hands-On Practice (Tags, Descendants, IDs, and Classes)

○ Quizzes and Solutions for Various Selectors

1.5 Scrapy Framework

○ Overview and Comparison with Requests

○ Getting Started with Scrapy

○ Building and Running Spiders

○ Response Handling (URLs, Status, and Headers)

1.6 Scrapy Project

○ Scraping the Hugo Boss Website

○ Understanding Site Structure

○ Writing CSS Selectors and Extracting Product Data

○ Pagination and Next Page Navigation

1.7 Selenium Framework

○ Introduction to Selenium and Webdriver Setup

○ Data Extraction Automation

○ Pagination and Exception Handling

1.8 Selenium Project

○ Building a Translation Project

○ Automating Cookie Management and Language Settings

○ Sending Text for Translation and Downloading Outputs

Part 2: Scala and Spark - Master Big Data with Scala and Spark

2.1 Introduction

○ Why Learn Scala?

○ Scala Applications

○ Course and Projects Overview

2.2 Scala Overview

○ Setting Up Scala Locally and Online

○ Working with Variables, Arithmetic Operations, and Strings

○ Quizzes and Solutions

2.3 Flow Control

○ Overview of Control Statements

○ If-Else and Nested Conditions

○ Logical Operators

2.4 Functions

○ Writing and Debugging Functions

○ Named Arguments and Code Modularity

2.5 Classes

○ Creating and Using Classes

○ Class Constructors and Functions

○ Project Implementation

2.6 Data Structures

○ Working with Lists and ListBuffers

○ Adding, Removing, and Accessing Data

○ Project Discussion and Architecture

2.7 Scala and Spark Project

○ Introduction to Spark and Hadoop Ecosystem

○ Spark Architecture and Ecosystem

○ Setting Up DataBricks and Running Spark RDDs

Part 3: PySpark and AWS - Master Big Data with PySpark and AWS

3.1 Introduction

○ Applications of PySpark

○ Course Overview and Project Details

3.2 Hadoop and Spark Ecosystem

○ Overview of Hadoop and Spark Architectures

○ Setting Up Spark Locally and on DataBricks

3.3 Spark RDDs

○ Creating and Manipulating RDDs

○ Using Map, FlatMap, and Filter Functions

○ Quizzes and Solutions

3.4 Spark DataFrames (DFs)

○ Introduction to Spark DFs

○ Schema Management and Column Operations

○ Filtering and Selecting Data

3.5 Collaborative Filtering

○ Utility Matrix and Rating Systems

○ ALS Model Implementation

○ Hyperparameter Tuning and Evaluation

3.6 Spark Streaming

○ Setting Up Spark Streaming

○ Streaming Data Transformations and Aggregations

3.7 ETL Pipeline

○ Building an ETL Pipeline

○ Data Extraction, Transformation, and Loading

○ RDS Setup and Networking

3.8 Change Data Capture Project

○ Project Introduction and Architecture

○ Setting Up RDS MySQL and S3 Bucket

○ Using DMS for Data Replication

Part 4: MongoDB - Mastering MongoDB for Beginners (Theory and Projects)

4.1 Introduction

○ Why MongoDB?

○ Applications, Methodology, and Project Overview

4.2 SQL vs. NoSQL

○ Comparing SQL and NoSQL Schemas

○ Installing MongoDB and Setting Environment Variables

4.3 Basic Mongo Operations

○ Database and Collection Commands

○ Document Creation, Reading, Updating, and Deletion

○ Quizzes and Solutions

4.4 Query and Update Operators

○ Using Operators like $eq, $gt, $lt, $set, and $unset

4.5 MongoDB Integrations

○ Connecting MongoDB with Node.js, Python, and Django

○ Performing CRUD Operations

4.6 Spark with MongoDB

○ Setting Up Spark for MongoDB Integration

○ Implementing ETL with MongoDB