Big data has become the lifeblood of modern business, fueling everything from personalized marketing to predictive maintenance to fraud detection. But before data scientists can work their magic, organizations first need to collect and harness massive volumes of raw data from diverse sources.
One of the most valuable, but often overlooked, sources of big data is the web itself. The internet contains a vast trove of information on every imaginable topic – from news articles and social media posts to product reviews and government datasets. By systematically crawling and scraping this web data, companies can gain unique insights and intelligence not found in internal databases.
In fact, web crawling and scraping have become essential tools for data-driven organizations. According to a recent survey by Oxylabs, 67% of data professionals use web scraping for big data applications, and 79% plan to increase their web scraping budgets in the coming year.[^1]
As a web crawling and scraping expert, I‘ve seen firsthand the power and potential of web data to transform businesses. However, collecting web data at scale is no simple feat. It requires careful planning, robust infrastructure, and adherence to ethical and legal scraping practices.
In this guide, I‘ll walk you through the 5 key steps to collect big data from the web, sharing my hard-earned insights and expertise along the way.
Step 1: Identify Data Sources and Scraping Targets
The first step in any web data collection project is to identify which websites and pages you want to target for scraping. This depends on your specific use case and data requirements.
Some common sources of web data include:
- News and media sites
- E-commerce product pages and reviews
- Social media profiles and posts
- Forums and discussion boards
- Government registries and public records
- Company websites and job listings
The key is to find websites that contain relevant, high-quality data for your needs. Tools like Alexa and Similarweb can help identify top sites in particular categories or industries.
Once you have a list of target sites, you need to analyze their structure and content to determine the specific data points you want to extract, such as:
- Article titles, dates, authors, and text
- Product names, prices, descriptions, and images
- Usernames, locations, bios, and follower counts
- Addresses, contact info, business categories, and ratings
- Linked URLs and metadata tags
In general, the more specific and structured the target data, the easier it will be to scrape. But even unstructured data like freeform text and images can be collected and processed using advanced techniques like natural language processing (NLP) and computer vision.
Step 2: Set Up Crawling and Scraping Infrastructure
With your data sources and targets identified, the next step is to set up the infrastructure for crawling and scraping web data at scale. This typically involves three main components:
Crawler – A program that systematically discovers and downloads web pages by following links from a seed URL. Popular open-source crawlers include Apache Nutch, Scrapy, and Heritrix.
Scraper – A program that extracts structured data from web pages by parsing the underlying HTML or XML code. Scraping can be done using libraries like Beautiful Soup (Python) or Cheerio (Node.js), or with visual point-and-click tools.
Data Pipeline – Infrastructure for storing, processing, and analyzing the collected web data. This may include databases like MongoDB for raw HTML, distributed processing frameworks like Spark, and data integration platforms.
When building your crawling and scraping infrastructure, there are several key challenges and considerations to keep in mind:
Scale – Crawling and scraping large websites with millions of pages requires distributed computing resources and efficient algorithms. Tools like Hadoop and Kafka can help scale out.
Performance – Scrapers need to be fast and reliable to extract data from pages in real-time. Techniques like async I/O, caching, and incremental parsing can significantly boost performance.
Stealth – Most websites have strict terms of service against automated scraping, and will quickly block any IP address making too many requests. Crawlers need to be configured with delays, throttling, and IP rotation to mimic human browsing patterns. Headless browsers like Puppeteer can help avoid detection.
Robustness – Websites are constantly changing, A/B testing, and personalizing content that can break scrapers. Building resilient scrapers requires handling edge cases, retries, and dynamic content like lazy-loading and infinite scrolling.
Data Quality – Raw web data is often inconsistent and unreliable. Scrapers need data validation, outlier detection, and error handling steps to ensure data quality and integrity.
By carefully designing and testing your crawling and scraping setup, you can build a scalable, reliable infrastructure for collecting high-quality web data.
Step 3: Collect and Integrate Web Data
With your infrastructure in place, you can start collecting web data at scale. The key steps are:
Crawling – Configure your crawler with the seed URLs, link patterns, and constraints (e.g. maximum depth, domain scope). The crawler will recursively follow links and download raw HTML pages.
Scraping – For each downloaded page, execute the scraper to extract structured data fields using CSS or XPath selectors. More advanced scrapers may also render JavaScript, fill out forms, or click buttons to access dynamic content.
Post-Processing – Clean, normalize, and validate the extracted data. Resolve duplicates, fix encoding issues, and remove irrelevant or sensitive information.
Data Integration – Load the extracted web data into your data pipeline for storage and analysis. This may involve transforming and merging it with other internal or external datasets.
There are several best practices to follow when collecting web data:
- Respect robots.txt files that indicate which pages are off-limits for crawling
- Throttle requests to a reasonable rate to avoid overloading servers
- Cache frequently crawled pages to reduce network traffic
- Use async and parallel processing to speed up scraping throughput
- Implement fault tolerance and error handling to recover from failures
- Monitor and alert on data quality issues like schema changes or anomalies
By automating these steps with a robust crawler and scraper setup, you can continuously collect fresh web data with minimal manual effort.
Step 4: Clean and Process Web Data
Raw web data is messy and noisy by nature. Before it can be analyzed, it needs to be cleaned and processed into a structured, consistent format. This involves tasks like:
- Data Parsing – Extracting text, numbers, dates, and other entities from scraped HTML using regular expressions or NLP
- Data Normalization – Converting values to consistent types, units, and scales (e.g. metric vs imperial, full names vs handles)
- Deduplication – Identifying and resolving records that refer to the same entity based on unique keys or fuzzy matching
- Entity Linking – Matching mentions and references to known entities in a knowledge base or taxonomy
- Sentiment Analysis – Classifying the emotion and opinion of text as positive, negative, or neutral
To give a concrete example, let‘s say we want to scrape data from the business reviews site Yelp. Listings pages contain the business name, address, star rating, review count, and snippet of the top review. Our scraper might extract the following raw fields:
name,address,rating,review_count,snippet
"The Cozy Cafe","135 W Main St, New York, NY 10001",4.5,481,"The Cozy Cafe is far and away ..."After parsing the HTML, we might normalize the address into separate components, deduplicate records with the same name, and extract the sentiment of the snippet:
{
"name": "The Cozy Cafe",
"street": "135 W Main St",
"city": "New York",
"state": "NY",
"postcode": "10001",
"rating": 4.5,
"review_count": 481,
"snippet": "The Cozy Cafe is far and away ...",
"sentiment": "positive"
}By cleaning and enriching the raw scraped data, we make it easier to analyze and derive insights downstream.
Step 5: Validate and Analyze Web Data
The final step is to validate the quality and integrity of your web data before using it for analysis. Even with robust scrapers and pipelines, there are many potential sources of data quality issues, such as:
- Stale data – Content that is out of date or hasn‘t been refreshed frequently enough
- Missing data – Records with empty or null values for key fields
- Inconsistent data – Values that use different formats, conventions, or meanings
- Outliers – Extreme or unexpected values that could be errors or anomalies
- Duplicates – Redundant or overlapping records from different scrapes or sources
To catch and fix these issues, it‘s important to profile and test your web data using both automated validation rules and manual spot checks. This can involve calculating summary statistics, plotting distributions, and comparing samples against ground truth data.
Once validated, web data can be a powerful input for various big data analytics and AI use cases, such as:
- Market Research – Analyzing consumer sentiment, trends, and competitors from web reviews and social media posts
- Lead Generation – Building targeted lists of sales prospects from company websites and directories
- Fraud Detection – Identifying fake reviews, counterfeit products, and scams across online marketplaces
- Content Personalization – Recommending articles, offers, and content based on user interests and interactions
- Risk Monitoring – Tracking news stories and regulatory filings for early warning of potential credit or PR risks
For example, the hedge fund Forefront Analytics uses web scraping to collect alternative data on e-commerce trends, corporate events, and geopolitical risks to inform its investment decisions. By analyzing millions of product reviews, news articles, and company filings, the firm can stay a step ahead of traditional market indicators.[^2]
There are countless other examples of companies leveraging web data for competitive advantage. Travel sites like Kayak and Booking.com scrape airline and hotel prices to offer comprehensive price comparisons. E-commerce aggregators like Google Shopping and Stylight scrape product details and inventory across retailers. And business intelligence firms like Craftar and Thinknum scrape everything from job listings to web traffic to generate industry insights and benchmarks.
The Future of Web Data Collection
As the web continues to grow and evolve, so do the opportunities and challenges of collecting web data at scale. On one hand, the explosion of web content and the rise of APIs have made more data available than ever before. On the other hand, tightening regulations like GDPR and increasing anti-bot measures have made scraping more difficult and risky.
To stay ahead of the curve, web data collectors will need to adopt new techniques and technologies like:
- Headless Browsers – Next-generation scraping tools that render JavaScript and emulate human behavior to avoid detection
- Computer Vision – Parsing visual content like images, videos, and PDFs using deep learning models
- NLP – Extracting entities, relations, and sentiments from unstructured text using transformers and knowledge graphs
- Low-Code Tools – No-code scraping and crawler platforms that empower non-technical users to collect web data
- Edge Computing – Decentralizing scraping infrastructure across global edge nodes for better performance and stealth
- Web3 – Exploring decentralized approaches to web data collection using blockchain, P2P networks, and tokenization
Ultimately, the future of web data collection will require a balance of technical innovation and responsible practices. As Aleksandras Šulženko of Oxylabs puts it:
"Web scraping is a powerful tool for unlocking insights from the world‘s largest dataset – the internet. But with great power comes great responsibility. As an industry, we need to lead the way in establishing ethical norms and technical standards for scraping that respect user privacy, data ownership, and the stability of the web ecosystem as a whole."[^3]
By following the steps and best practices outlined in this guide, and staying at the forefront of web data collection trends, you‘ll be well-equipped to harness the full potential of big data from the web for your organization.
[^1]: Oxylabs (2022), "The Growing Importance of Web Data for Business," https://oxylabs.io/press/growing-importance-of-web-data-for-business [^2]: IAPP (2022), "Hedge Fund Data Scraping: Use Cases and Compliance Risks," https://iapp.org/news/a/hedge-fund-data-scraping-use-cases-and-compliance-risks/ [^3]: Oxylabs (2023), "The Ethics of Web Scraping: An Industry Perspective," https://oxylabs.io/blog/web-scraping-ethics