Best Tech Stack for Web Scraping in 2023
Best Web Scraping Tech Stack of 2023; The Ultimate List of Modern Tools and Technologies
In the information age, fast data collection is highly in demand, so web scraping tools and technologies are becoming increasingly popular, especially when it comes to scraping websites and marketplaces and collecting large amounts of data from various sites, such as social media platforms, news channels, financial institutions for obtaining financial data, and official websites.
From a data engineer's perspective, it's important to understand what goes into the process of collecting data from websites and which tools and methods are best suited for different scenarios.
To choose the most appropriate tools and methods for web scraping, you first need to understand the basic minimum procedures of web scraping.
Web Scraping Life Cycle;
Any web scraping work consists of three stages
- Studying web elements and content architecture
Every web page consists of many elements containing content (text, tables, images, video). Therefore, studying the structure of a web page and reaching a specific element with the desired content is an important task, and there are many tools for this.
Any good browser provides developer tools for inspecting web pages; at this stage, it's best to use Google Chrome developer tools.
- Accessing and retrieving data
After studying the site structure and identifying the necessary elements to be extracted, the next stage is to access the site's content and parse it using automated software scripts (Python, JS, PHP, Java, etc.).
- Uploading data to storage
After parsing the response page provided by the server, we need to save this data.
There are many tools and formats for saving data; the most commonly used are SQL, CSV, JSON, SQLite, and YML. The nature of the content coming from the site determines the storage format. For image data, we need to upload them in PNG or JPG format to disk storage. For text data, we can use a simple TXT format. If the data is tabular, we can transform it and dump it into a SQL database like SQL, SQLite, or PostgreSQL, or simply save the data in CSV or XLSX format.
For nested hierarchical content, we can export data in JSON or YML formats.
After reviewing the basic procedures of web scraping, now we will discuss the main web scraping stacks currently available and see which stack provides the best solution for our needs. I will talk about technologies that are too important to ignore. It is not necessary to master all these technologies; the main thing is to know about all the possibilities before making a choice for a particular project.
Ultimate Web Scraping Stack
First of all, it is necessary to understand how web scraping technology stacks are divided and what components make up a full stack. We can safely divide the stack into these 5 components. Each pillar is important and necessary for the others to work at scale.
Programming Languages and Libraries for Web Scraping
At a fundamental level, every programming language can interact with a server and request data, but the complexity of web scraping varies from language to language,
Some languages provide very efficient frameworks and pipelines to facilitate the web scraping process; for example, Python and JavaScript are the main trends in web scraping today.
Web Scraping Stack in Python
Python as a scripting language is very well-known today because it is very strong in data processing and software development. Its nature as a dynamically typed high-level language, a rich set of web scraping frameworks (Selenium, Scrapy, Beautiful Soup, etc.) and the powerful n-dimensional arrays provided by NumPy and dataframes provided by Pandas make it a robust programming language for scraping and processing data at scale.
If you work in a data-driven ecosystem, then Python as a programming language for web scraping can certainly be the best choice.
Requests (HTTP Client) and BeautifulSoup
Requests is a wonderful Python library for interacting with and obtaining responses from any website, allowing you to make HTTP calls quickly and easily. BeautifulSoup, called bs4, is a well-known web response parser available in Python.
For simple static sites, such as Wikipedia, where the page consists of simple HTML content, requests and BeautifulSoup are sufficient for data collection. It is very easy to get started without spending time studying documentation.
For content processing and data transformation, no language compares to Python; its powerful NumPy and Pandas packages allow working with any tabular data and easily writing ETL pipelines. That's why Python is so popular in web scraping.
Scrapy; a Reliable Python Framework:
For larger-scale web scraping, Python has a very well-known framework called Scrapy. It is a very powerful framework because it efficiently handles all stages of web scraping.
For handling crawling and data retrieval, it provides a Spider class, and for data processing, it provides pipelines that allow developing specialized data storage solutions.
Scrapy contains very robust middleware, and every request from the spider passes through middleware to the downloader, and similarly, data from the downloader passes through middleware to the pipelines, allowing efficient storage.
As a programmer, you write crawlers using the Spider class; the rest of the work is done by Scrapy itself. For working with images or streaming data, you may need to change the pipeline configuration, after which Scrapy will handle files and folder hierarchy very efficiently.
IP proxying, unique URL filtering, and request throttling to speed up web scraping are the main features provided by the Scrapy framework.
If you need to collect millions of images and text data from classified sites like eBay, Scrapy is the best choice.
Using Scrapy, you simply need to write spiders according to the structure of the target site, and the rest of the work is done by Scrapy.
Browser Automation Tools for Web Scraping in Python
Selenium - Web Scraping Automation Tool
For dynamically loaded websites where data is loaded onto the web page at runtime, we cannot use requests or the Scrapy framework to retrieve this content directly, because JavaScript must be executed to load the data, which is not possible with HTML parsing scripts. Therefore, we need a solution that allows us to interact with the web page and make requests by clicking on specific elements or buttons on the page.
In Python, there is a library called Selenium for interacting with web pages on the browser side. It is a web testing automation tool that can easily be used for scraping purposes.
If you put Selenium in a loop, web scraping becomes much slower because you have to load each web page in the browser and interact with elements manually. But the good thing is that it works. For many sites where data is loaded dynamically, we have no other choice but Selenium in Python.
JavaScript Stack for Web Scraping
Before the release of Node.js, JavaScript was only a browser language, but now it is everywhere.
Its asynchronous functional programming paradigm makes it very efficient for interacting with websites and retrieving data compared to other programming languages like Python, Java, C#, etc.
Let's look at what tools we have in the JavaScript web scraping stack and how they work.
Request-Promise, CheerioJs:
Similar to Python, Node.js has Request-Promise and CheerioJs modules, with which we can make requests to web servers and parse response pages to retrieve target information.
For static websites where data is loaded into the HTML page, we can use these two libraries to easily perform scraping.
Puppeteer for Scraping Dynamically Loaded Websites
To handle dynamically loaded sites and scrape data, the Puppeteer package is used. The asynchronous nature of Node.js makes it a better choice for scraping data from dynamically loaded sites.
Puppeteer is easy to install and use for JavaScript developers, unlike Selenium. Puppeteer was developed by the Google Chrome team for automation, so it has more capabilities for controlling Google Chrome compared to Selenium.
Proxy Servers and Anti-Bot Tools
With the advancement of web development, web servers have become very smart. Detecting frequent requests from the same IP address and blocking them is very common. Performing many requests from the same IP address can lead to our system's IP being blocked by the web server. To avoid this, we need to use IP proxying techniques. There are many proxy services.
Depending on the usage conditions, different providers and services are recommended, such as data center proxies, residential proxies, mobile proxies.
To set up a proxy server, you have two options
- Set up a proxy server on your own: If you have the configuration skills, this gives you the most control, as you can configure it to suit your business needs.
- Use a proxy server provider: The simpler option is to outsource this work to a specialized company that specializes in this area.
Bypassing Captcha
Captcha bypass is another obstacle in web scraping. There are many web services that rely on human labor to bypass captchas.
Honeypot Traps
Sometimes web servers set up honeypot traps. These traps are actually web links that are open to scraping bots but invisible to real users. If a bot is not very well optimized, it can be detected and blocked by the server.
Databases and Storage for Web Scraping Data
After we have collected data using our favorite web scraping tools, the data storage process is the final stage of web scraping. Choosing a good data storage solution nowadays is critical and depends on the nature of the data we are collecting.
For small-scale data scraping, we can dump data into CSV, XLSX, TXT, or JSON files. But for large amounts of data where many read and write queries need to be performed, we can choose a good SQL server like Oracle SQL, MySQL, MS SQL. If we need to serve data to multiple applications over the network, we should choose cloud storage services.
Google Cloud Storage:
Google Cloud provides GCP storage where we can write our data just like on a local machine. If our data is large-scale and tabular, we can use SQL storage from Google Cloud or BigQuery tables.
AWS Storage:
AWS is known for its tools and APIs for cloud development and integration and data processing at scale. For handling raw and unstructured data, we can use the data lake provided by AWS, then use ETL tools like AWS Glue to reorganize the data and Athena for analytics.
AWS also provides a SQL server that we can use to dump tabular data.
There are many cases where we simply want to store data in storage for archival. In that case, data can grow quickly and become expensive to store. Amazon S3 becomes easier and cheaper in this case.
Databases for Web Scraping
In our time, many new and non-traditional databases are becoming very popular, but every data engineer, when it comes to a database, thinks of relational databases and SQL.
The big four relational SQL databases remain Oracle, MySQL, Microsoft SQL Server, and PostgreSQL.
Although MySQL is the most popular database, it is also worth noting that MySQL has some limitations, so if you are dealing with very large data collection projects, this option may not be the best.
Document-Based Database for Web Scraping; MongoDB
Using MongoDB eliminates the need to normalize data for insertion into a database; you can store heterogeneous and non-relational data without significant performance issues. Additionally, MongoDB can handle very large volumes of data. For web scraping, MongoDB Atlas allows easy storage of scraped data without setting up a local database.