Building Automated Data Extraction Pipelines with Python


Data Extraction and Scraping Techniques Using Python

What you will learn

How to automate data extraction pipelines using Python

How to scrape data from e-commerce websites using Python

How to use Scrapy to build scalable and efficient web scrapers

How to use Requests to make HTTP requests to web servers

Scrape data with BeautifuSoup

Scrape data with Scrapy

Scrape e-commerce Data with Python

How to use Beautiful Soup to parse HTML

How to install and set up Python libraries for data extraction

How to use Python libraries for data extraction

Common use cases for automated data extraction

The importance of automated data extraction

Python 3.x installed on the computer

Description

In the age of Big Data, the ability to effectively extract, process, and analyze data from various sources has become increasingly important. This  course will guide you through the process of building automated data extraction pipelines using Python, a powerful and versatile programming language. You will learn how to harness Python’s vast ecosystem of libraries and tools to efficiently extract valuable information from websites, APIs, and other data sources, transforming raw data into actionable insights.

This  course is designed for data enthusiasts, analysts, engineers, and anyone interested in learning how to build data extraction pipelines using Python. By the end of this course, you will have developed a solid understanding of the fundamental concepts, tools, and best practices involved in building automated data extraction pipelines. You will also gain hands-on experience by working on a real-world project, applying the skills and knowledge acquired throughout the course. We will be using two popular Python Libraries called BeautifulSoup and Scrapy  f to build our  data pipelines.

Beautiful Soup is a popular Python library for web scraping that helps extract data from HTML and XML documents. It creates parse trees from the page source, allowing you to navigate and search the document’s structure easily.


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Beautiful Soup plays a crucial role in data extraction by simplifying the process of web scraping, offering robust parsing and efficient navigation capabilities, and providing compatibility with other popular Python libraries. Its ease of use, adaptability, and active community make it an indispensable tool for extracting valuable data from websites.

Scrapy is an open-source web crawling framework for Python, specifically designed for data extraction from websites. It provides a powerful, flexible, and high-performance solution to create and manage web spiders (also known as crawlers or bots) for various data extraction tasks.

Scrapy plays an essential role in data extraction by offering a comprehensive, high-performance, and flexible web scraping framework. Its robust crawling capabilities, built-in data extraction tools, customizability, and extensibility make it a powerful choice for data extraction tasks ranging from simple one-time extractions to complex, large-scale web scraping projects. Scrapy’s active community and extensive documentation further contribute to its importance in the field of data extraction.

English
language

Content

Introduction to Automated Data Extraction

Introduction
Understanding the importance of automated data extraction
Identifying use cases for automated data extraction
Web Scraping Overview
Introduction to Python libraries for data extraction

Setting up Your Data Extraction Environment

Installing Python on Windows
Installing Python on Mac OS
Updating Pip
Create and activate a virtual environment
Install Scrapy
Install Beautiful Soup
Note on Text Editors
Installing Visual Studio Code Text Editor
Best practices for data extraction pipelines

Building Basic Data Extraction Pipeline using BeautifulSoup

What we will extract
Writing Python script for basic data extraction – Part 1
Writing Python script for basic data extraction -Part 2
Prototyping the script – Part 1
Prototyping the script – Part 2
Prototyping the script – Part 3
Prototyping the script – Part 4
Prototyping the script – Part 5
Extracting data with the script

Building Basic Data Extraction Pipeline using Scrapy

Creating a Scrapy project
Components of a scrapy project
Scrapy architecture
Creating a spider : Part 1
Creating a spider : Part 2
Extracting data with scrapy shell : Part 1
Extracting data with scrapy shell : Part 2
Running the spider to extract data

Building Basic Data Extraction Pipeline for e-commerce

Create and activate a virtual environment
Install Python Packages
Creating a Python file
Creating Variables
Enabling Gmail Security
Creating Functions: Part 1
Creating Functions: Part 2
Creating Functions: Part 3
Extracting data with the Python Script