Web scraping is emerging as a must-have technique for businesses, researchers, and developers. This technique is for smart professionals who believe in the power of data. To mine knowledge, these experts professionally extract data in a huge volume from web sources. It helps in adding a competitive edge. Moreover, the driven knowledge can help in pricing analysis, content aggregation, or academic research.
According to a recent report by Apify, the global web scraping software market was valued at around USD 1.01 billion in 2024 and is projected to reach USD 2.49 billion by 2032. Another industry study highlights that over 80% of top online retailers scrape competitor data daily to stay ahead in pricing and product strategy.
Though beneficial, multiple risks or challenges make its execution difficult. And these complexities are not just legal but ethical also, which makes practitioners puzzled.
This post will introduce you to some smart strategies & best practices that can help in overcoming these challenges.
Top Web Scraping Problems & Their Solutions
Here come some common challenges that interfere with smooth extraction.
1. Website Structure Changes Frequently
With inflating scraping needs, the IT infrastructure demands upgrades. So, you need to navigate multiple requests coming from hundreds of domains with a meticulous architectural plan. In such cases, data extraction and processing help becomes essential to handle vast datasets efficiently and maintain scraper performance as the scale increases.
How to Overcome
- Rely on scraping libraries such as Scrapy and BeautifulSoup that can adapt easily.
- Prefer dynamic CSS selectors and XPath expressions with fallbacks.
- You may prefer a monitoring tool to notify whenever a website design changes.
2. CAPTCHA and Bot Protection
Many websites protect themselves using CAPTCHA and Cloudflare or Perimeter-like anti-bot services so automated access can be denied. These tools can identify suspicious bot attacks and hence, block them via CAPTCHA, JavaScript puzzles, or throttling responses.
How to Overcome:
- These web extraction problems can be navigated by harnessing headless browsers like Selenium because they behave like a human being.
- You may leverage rotating user agents and IPs through proxy pools or residential IPs to befool them.
- Introduce machine learning CAPTCHA solvers or services like 2Captcha leveraging legal permissions (if you have).
3. IP Blocking and Rate Limiting
A website suffocates when it notices unusual traffic flocking in unnatural patterns. You can detect them by tracking IP address, which would be coming from the same IP in a short span. This testing will prevent unusual traffic from blocking your necessary emails.
How to Overcome:
- You may rotate proxy by using services like BrightData, Smartproxy, etc.
- Shuffle delays between requests to behave like a human while blocking IPs.
- Cape requests or leverage backoff strategies when you encounter HTTP 429 or 403 errors.
4. Legal and Ethical Considerations
Though scripting to scrape data is challenging, maintaining compliance is way more difficult. It is simply because you can be trapped in litigation for violating a site’s terms of service, intellectual property rights, or data protection regulations like GDPR.
How to Overcome:
- Prioritise detecting the website’s robots.txt file and privacy or terms of use.
- Never violate personally identifiable information (PII) or login-required pages.
- Always avoid unethical scraping practices by exceeding the request rate, identifying your bots, and leaving the site uninterruptedly performing.
- Please consult with a legal expert if you are unfamiliar with the regulatory frameworks that protect data.
5. Dynamic Content and JavaScript Rendering
JavaScript is majorly used to load content dynamically. Traditional scraping tools like BeautifulSoup fail to access this content because it cannot be detected in the initial HTML response.
How to Overcome:
- Switch to headless browsers like Puppeteer or Selenium so the way a browser renders can be imitated.
- Use API sniffing to detect whether the data is fetched from a backend API, but not directly.
- Prefer reverse engineering mobile apps to harness easier APIs for loading data.
6. Duplicate or Inconsistent Data
Scraping a huge volume of data? Dupes and inconsistencies due to pagination, infinite scrolling, and improper session handling can interfere with smooth scraping.
How to Overcome:
- Prefer hash functions or UUIDs or data-deduplication methods.
- Avoid duplicate URLs or IDs by designing scrapers to red flag.
- Using Python libraries like Pandas for extracting data? Clean and normalise your data during and post this process.
7. Maintaining Scraper Scalability
With inflating scraping needs, the IT infrastructure demands upgrades. So, you need to navigate multiple requests coming from hundreds of domains with a meticulous architectural plan.
How to Overcome:
- Multiple cloud storages and systems like AWS Lambda and Apache Kafka are there to handle multiple scraping tasks simultaneously.
- Leverage advanced database management systems that are scalable, such as MongoDB or PostgreSQL.
- Keep an eye on the health of your system with tools like Grafana.
8. Anti-Scraping Lawsuits and Precedents
Did you learn about the legal battle of LinkedIn in the US? It clearly raised legal risks associated with data scraping from public profiles, even if you haven’t logged in.
How to Overcome:
- Scrape such data that are available as public or from open-data sources.
- Do not even try to extract data from websites that explicitly ban scraping.
- Always stay tuned with scraping laws in different continents or countries to abide by the latest regulatory frameworks.
9. High Maintenance Costs
Do you think that extraction is just to write codes and run to automate it? Well, you need to remember that it requires frequent monitoring because web layout can be changed at any time, or IP can be restricted. Perhaps new compliance requirements can raise a concern to maintain.
How to Overcome:
- Focus on a scalable or editable scraping tool, which must be tested and updated.
- Rely on AI or advanced scripts to test whether the script is seamlessly working to find flaws in this process.
- If you want a professional to take this responsibility, consider outsourcing scraping service providers to leverage their expertise within a limited budget.
10. Balancing Ethics and Business Goals
Like unethical issues, ethical data extraction concerns like content ownership, bandwidth, consumption and user privacy can become roadblocks. But at the same time, you must monitor pricing, competitors’ strategies, and new aggregation.
How to Overcome:
- Emphasise scraping for non-invasive or valuable purposes like SEO analysis, research or studies.
- Avoid content that is highly sensitive, user-generated, or copyrighted.
Conclusion
Web scraping reveals unlimited possibilities for various industries. Businesses can immediately and easily discover real-time insights to automation. But it is not an easy task because of evolving regulations, advanced anti-bots, and ethical limits. These challenges can be encountered with insightful and proven solutions.