The Ultimate Guide to Extracting Web Data at Scale

Web crawling, also known as web scraping, has become an essential skill in today‘s data-driven business world. The ability to automatically extract data from websites and databases at scale enables companies to gain valuable insights, inform decisions and automate processes.

According to a recent study by Opimas Research, the web scraping industry generated $1.28 billion in revenue in 2021 and is projected to grow at a 9.6% rate annually to reach $2.1 billion by 2026. A separate survey of over 3000 data professionals found that 57% use web scraping regularly in their work.

Web Crawling Use Cases and Benefits

Some of the top applications for web crawling include:

• Price Intelligence: Monitoring competitors‘ prices on e-commerce sites to inform dynamic pricing
• Lead Generation: Scraping contact information like emails and phone numbers for sales outreach
• Market Research: Analyzing customer reviews and product details across different sites
• Search Engine Indexing: Discovering new webpages to add to search results
• Web Archiving: Saving webpage content and data for historical record keeping
• Job Listings: Aggregating job postings from multiple boards and company sites
• Social Media Monitoring: Tracking brand mentions and sentiment across social platforms
• Real Estate: Extracting property details and listings from sites like Zillow and Redfin

Web scraping provides several key benefits over trying to obtain data manually:

• Scale: Scraping can extract data from thousands of pages in the time it would take a human to copy data from a few dozen
• Cost: Scraping is much cheaper than paying people to manually find and input data
• Accuracy: Scrapers don‘t get tired and make mistakes like humans doing repetitive copy/pasting
• Speed: Scrapers can work 24/7 and complete huge data extraction tasks in a fraction of the time
• Timeliness: Scrapers can continuously monitor sites to extract new data as soon it‘s published

According to HiQ Labs vs LinkedIn, scraping publicly accessible data is legal and the courts have ruled in favor of scrapers in several high-profile cases. However, it‘s important to respect a site‘s terms of service and robots.txt to avoid crossing ethical boundaries.

How Web Crawlers Work

At a high level, web crawlers work as follows:

1. Start with an initial set of URLs called ‘seeds‘
2. For each URL, fetch the page content using HTTP requests
3. Parse the HTML to extract links to additional pages
4. Add those URLs to a queue and recursively crawl them
5. Extract the desired data from each page using patterns or machine learning
6. Save extracted data to files or databases for analysis

Here‘s a simple example of crawling page links in Python using BeautifulSoup:

import requests
from bs4 import BeautifulSoup

def crawl(url):
    r = requests.get(url)
    soup = BeautifulSoup(r.text)    

    for link in soup.find_all(‘a‘):
        link_url = link.get(‘href‘)
        print(link_url)
        crawl(link_url)

crawl(‘https://example.com‘)        

Some important considerations when building crawlers:

• Respect robots.txt and sites‘ terms of service
• Use delay between requests to avoid overloading servers
• Rotate user agents and IP addresses to avoid blocking
• Handle sessions, logins and CAPTCHAs as needed
• Use a URL frontier and revisit strategy to crawl efficiently

Database Crawling

While most crawling focuses on publicly accessible web pages, an often more powerful approach is to extract data directly from the backend databases powering websites and applications.

Database crawling can provide access to more comprehensive structured data that isn‘t always exposed in the frontend web pages. An analysis by Import.io found database crawling can yield 30-60% more data fields compared to webpage scraping alone.

However, database crawling requires direct access to the underlying database servers and comes with additional challenges:

• Need cooperation from the database owner for credentials
• Have to understand the schema and table structures
• Queries can be more expensive than HTTP requests
• Need to handle pagination and incremental extraction
• Schema changes can break extraction pipelines

Some popular open source tools for database crawling include:

• Scrapy: A Python framework for crawling relational DBs and NoSQL stores
• Apache Nutch: A Java-based crawler that can integrate with Hadoop and Cassandra
• StormCrawler: A collection of modules to build scalable crawlers with Apache Storm

On the commercial side, vendors like Diffbot, Apify and Bright Data offer managed database crawling services with features like automated schema inference, scheduling, and throttling.

The Future of Web Crawling

As the web continues to evolve, several emerging trends will shape the future of web crawling:

• JavaScript Rendering: With more sites using client-side frameworks like React and Angular, crawlers increasingly need to execute JavaScript to extract content vs just parsing static HTML. Headless browsers like Puppeteer are becoming essential crawling tools.

• Machine Learning Extraction: Advances in visual ML and NLP models enable automatically classifying page content and extracting semantic entities without brittle rules or regexes. Approaches like topic modeling, NER and OCR unlock new unstructured data.

• Structured Data Formats: Adoption of HTML formats like JSON-LD and schema.org provide more semantic structure for crawlers to extract entities. The markup standardization efforts by Schema.org have led to 10M+ sites publishing crawlable structured data.

• Cloud Crawling Services: The major cloud providers now offer managed web crawling services like Amazon Web Crawler, Azure Web Crawler and Google Cloud Web Crawler to simplify large-scale scraping and integrate with their data and AI platforms.

The International Web Crawling and Data Extraction Summit in 2020 identified several other areas of future research and innovation:

• Adversarial crawling to bypass anti-bot measures
• Multimedia crawling for images, audio, and video
• IoT and edge crawling for sensors and devices
• Blockchain oracle services for smart contract input
• Collaborative distributed crawling networks

Conclusion

Web crawling and database crawling have become essential tools for extracting web data at scale to power insights and automation. While they require significant technical skills to implement robustly, the competitive advantages they provide make them well worth the effort.

As the web moves towards more structured data formats and ML-powered extraction, the future is bright for crawling. Existing tools will continue to evolve to handle JavaScript and new frameworks, while cloud services will make powerful crawling accessible to more organizations.

However, with great power also comes responsibility. As crawling becomes more prevalent, it‘s crucial to use it ethically, respect site owners, and avoid abuse. Ultimately, crawling should aim to derive insights from data to benefit society.