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Big Data and Web Scraping with PySpark, AWS, and Scala Practice Exam

Big Data and Web Scraping with PySpark, AWS, and Scala Practice Exam


About Big Data and Web Scraping with PySpark, AWS, and Scala Exam

The Big Data and Web Scraping with PySpark, AWS, and Scala Exam leverages powerful technologies for efficient data extraction and analysis. Web scraping extracts data from websites, which is then processed using PySpark on AWS for large-scale data processing and analysis. Scala can be used for complex data transformations and for building robust, scalable applications within the AWS ecosystem. This approach enables organizations to effectively handle massive datasets, gain valuable insights from unstructured web data, and build high-performance, distributed applications for data-driven decision-making.


Skills Required

Skills required for Big Data and Web Scraping with PySpark, AWS, and Scala exam include:

  • Core Programming: Python, Scala
  • Big Data: PySpark, AWS (EC2, EMR, S3, Glue)
  • Web Scraping: BeautifulSoup/Scrapy/Selenium, data extraction techniques
  • Data Engineering: Data cleaning, transformation, analysis, visualization
  • Cloud Computing: AWS fundamentals, Git
  • Soft Skills: Problem-solving, communication, collaboration


Knowledge Area

The Big Data and Web Scraping with PySpark, AWS, and Scala exam requires a comprehensive understanding of technologies and methodologies for extracting, processing, and analyzing large volumes of data from the web. It involves proficiency in Python, Scala, and the PySpark framework, along with practical experience utilizing AWS services for big data processing and storage. Key areas of expertise include:

  • Proficiency in extracting structured and unstructured data from websites using libraries like BeautifulSoup, Scrapy, and Selenium.
  • Expertise in using PySpark for data ingestion, transformation, cleaning, and analysis, including working with RDDs, DataFrames, and Spark SQL.
  • Familiarity with AWS services relevant to big data, such as EC2, EMR, S3, Glue, and Athena.
  • Understanding of Scala syntax, functional programming concepts, and its application in big data processing.
  • Proficiency in data exploration, analysis, and visualization using libraries like Matplotlib, Seaborn, and Plotly.
  • Understanding of cloud computing principles, including scalability, reliability, and security within the AWS environment.


Who should take the Exam?

The Big Data and Web Scraping with PySpark, AWS, and Scala exam is most suitable for individuals who:

  • Aspire to a career in data science, data engineering, or big data analytics.
  • Seek to enhance their skills in web scraping, data processing, and cloud computing.
  • Want to demonstrate their expertise in using PySpark, AWS, and Scala for big data projects.
  • Professionals looking to advance their careers by acquiring in-demand skills in the big data and web scraping domain.
  • Software engineers or data professionals who want to expand their skillset to include big data and cloud technologies.
  • Individuals interested in pursuing a career in data-driven fields such as data science, machine learning, and artificial intelligence.


Course Outline

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

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

1. Introduction

  • Importance of Data Scraping
  • Applications of Data Scraping
  • Instructor Introduction
  • Overview of the Course, Scraping Techniques, and Tools
  • Projects Overview

2. Python Requests

  • Introduction to Python Requests
  • Hands-On Practice
  • Extracting Quotes Manually
  • Quizzes and Solutions (Authors and Quotes)
  • Pagination Techniques
  • AJAX Requests

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)

4. CSS Selectors

  • Introduction to CSS Selectors
  • Hands-On Practice (Tags, Descendants, IDs, and Classes)
  • Quizzes and Solutions for Various Selectors

5. Scrapy Framework

  • Overview and Comparison with Requests
  • Getting Started with Scrapy
  • Building and Running Spiders
  • Response Handling (URLs, Status, and Headers)

6. Scrapy Project

  • Scraping the Hugo Boss Website
  • Understanding Site Structure
  • Writing CSS Selectors and Extracting Product Data
  • Pagination and Next Page Navigation

7. Selenium Framework

  • Introduction to Selenium and Webdriver Setup
  • Data Extraction Automation
  • Pagination and Exception Handling

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

9. Introduction

  • Why Learn Scala?
  • Scala Applications
  • Course and Projects Overview

10. Scala Overview

  • Setting Up Scala Locally and Online
  • Working with Variables, Arithmetic Operations, and Strings

11. Flow Control

  • Overview of Control Statements
  • If-Else and Nested Conditions
  • Logical Operators

12. Functions

  • Writing and Debugging Functions
  • Named Arguments and Code Modularity

13. Classes

  • Creating and Using Classes
  • Class Constructors and Functions
  • Project Implementation

14. Data Structures

  • Working with Lists and ListBuffers
  • Adding, Removing, and Accessing Data
  • Project Discussion and Architecture

15. 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

16. Introduction

  • Applications of PySpark
  • Course Overview and Project Details

17. Hadoop and Spark Ecosystem

  • Overview of Hadoop and Spark Architectures
  • Setting Up Spark Locally and on DataBricks

18. Spark RDDs

  • Creating and Manipulating RDDs
  • Using Map, FlatMap, and Filter Functions

19. Spark DataFrames (DFs)

  • Introduction to Spark DFs
  • Schema Management and Column Operations
  • Filtering and Selecting Data

20. Collaborative Filtering

  • Utility Matrix and Rating Systems
  • ALS Model Implementation
  • Hyperparameter Tuning and Evaluation

21. Spark Streaming

  • Setting Up Spark Streaming
  • Streaming Data Transformations and Aggregations

22. ETL Pipeline

  • Building an ETL Pipeline
  • Data Extraction, Transformation, and Loading
  • RDS Setup and Networking

23. 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)

24. Introduction

  • Why MongoDB?
  • Applications, Methodology, and Project Overview

25. SQL vs. NoSQL

  • Comparing SQL and NoSQL Schemas
  • Installing MongoDB and Setting Environment Variables

26. Basic Mongo Operations

  • Database and Collection Commands
  • Document Creation, Reading, Updating, and Deletion

27. Query and Update Operators

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

28. MongoDB Integrations

  • Connecting MongoDB with Node.js, Python, and Django
  • Performing CRUD Operations

29. Spark with MongoDB

  • Implementing ETL with MongoDB

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