Web scraping has exploded in popularity as a way to quickly extract valuable data and insights from websites. By automating the collection of data at scale, businesses and researchers can obtain immense amounts of information in a fraction of the time it would take manually. Web scraped data fuels innovation in fields like ecommerce, finance, real estate, and social media.
However, web scraping is not a perfect process and comes with its own set of challenges and limitations. As the saying goes, "garbage in, garbage out" – the insights you derive are only as good as the data you collect. It‘s critical to understand the potential pitfalls and take steps to ensure the accuracy and integrity of your web scraped data.
In this post, we‘ll dive into 7 key web scraping limitations you need to be aware of and discuss strategies to mitigate them. By the end, you‘ll be equipped with the knowledge to extract higher quality web data and make better data-driven decisions.
1. Websites Frequently Change Structure
One of the biggest challenges in web scraping is that websites are constantly evolving. Web scrapers are built according to the structure of the pages they are extracting data from – the placement of elements, HTML tags, CSS selectors, etc. Even a small change, like an element being relocated, can completely break your carefully constructed crawlers and lead to missing or erroneous data.
This problem is magnified if you are scraping data from multiple websites. Monitoring and adjusting your scrapers to keep up with each website‘s updates can quickly become a huge maintenance burden. In general, scrapers may need to be reviewed and tweaked every few weeks to ensure they are extracting data correctly.
According to a study by Zyte (formerly Scrapinghub), 97% of web scrapers break within the first year due to website changes. On average, it takes about 10 hours of maintenance per month to keep scrapers running smoothly. For large scale scraping operations spanning hundreds or thousands of sites, this can add up to significant ongoing engineering costs.
Some strategies to handle changing website structures include:
- Using more flexible and resilient methods to identify target data, such as XPath or regular expressions
- Monitoring for changes and setting up alerts if scraped data deviates from expected patterns
- Keeping scraper code modular to more easily update individual extraction rules
- Considering using pre-built web scraping tools that manage these updates for you
As Andriy Bobitskiy from Apify explains, "Website changes are inevitable, so it‘s important to build scrapers with change in mind from the start. Using smart selectors, modularity, and auto-detection of changes can minimize the maintenance burden. Of course, all of this takes more upfront development work, so there‘s a trade-off to consider based on your specific project needs."
2. Complex, Dynamic Websites Are Hard to Scrape
In the early days of the web, most websites were fairly simple and straightforward to scrape. However, modern websites are much more dynamic and complex. Features like infinite scrolling, lazy loading, and content rendered by JavaScript can trip up traditional scraping methods.
Websites are also increasingly using anti-scraping techniques like CAPTCHAs and honeypot traps to block apparent bot activity. About 20% of websites have complex setups that make them very challenging to extract data from.
To scrape dynamic websites, more advanced techniques are required, such as:
- Headless browsers that can execute JavaScript and more fully render pages
- Detecting and responding to CAPTCHAs, either manually or through solving services
- Navigating pagination via clicking next page links or simulating scroll behavior
- Reverse engineering APIs that power dynamic content loading
The best web scraping tools support these more advanced capabilities to help you handle modern website setups. Just be prepared that complex sites will require more time and effort to robustly extract data from.
Gartner predicts that by 2025, 75% of organizations will be using AI-powered tools to extract insights from semi- and unstructured data like web content. These tools leverage machine learning to automatically identify patterns and adapt to changes, reducing the complexity of scraping dynamic sites.
3. Large-Scale Scraping is Difficult
Scraping a few hundred records from a site is very different than extracting millions of records spanning years of archives. Large-scale web scraping presents unique infrastructure and cost challenges.
Scrapers need to be able to handle:
- Executing complex scraping jobs that could take days or weeks to complete
- Storing and processing the huge volumes of extracted data
- Running scraping tasks in a distributed way across multiple machines
- Avoiding rate limits and IP bans from target sites despite aggressive crawling
Cloud-based web scraping platforms are built to address these needs by providing scalable infrastructure to support big scraping jobs. They can parallelize workloads across a large number of servers and include helpful features like job scheduling, monitoring, and notifications.
However, large scale web scraping can become quite expensive, so make sure the data you are collecting is actually valuable to justify the investment. You‘ll also need more robust data pipelines to process, clean, and analyze the high volume of data being generated.
A report by the Web Scraping API provider Scraper API found that 56% of companies are scraping data from over 100 websites, with 12% scraping from over 1000 sites. At this scale, dedicated scraping infrastructure and tools become essential to success.
4. Certain Content Can‘t Be Scraped
Web scrapers primarily work by parsing the HTML of web pages to identify and extract chunks of data. This means that certain types of content are just not accessible to standard web scraping methods, such as:
- Content within PDFs, images, and video files
- Text content generated as images to prevent scraping
- Scanned documents stored as images
- Data only accessible within mobile or desktop apps
- Information behind logins or paywalls
If you need data stored in these formats, you‘ll have to turn to other more specialized extraction methods and tools, such as:
- PDF parsing libraries
- Optical character recognition (OCR) to convert images to text
- Authorized APIs provided by the data source
- Partnering with companies that have negotiated access to paywalled datasets
Asier García Saiz, CTO at ScrapingBee, notes "While you can scrape some metadata about these unsupported content types, like titles and tags, the actual content remains inaccessible without using additional tools and techniques. In some cases, the data you need just may not be available through web scraping."
5. Risk of IP Bans
Aggressive web scraping from a single IP address is likely to be flagged as suspicious bot activity and get that IP banned from a website. IP rate limiting is one of the most common defenses websites use to prevent abuse and protect their servers from being overwhelmed by bots.
You can try to avoid IP bans by:
- Adding random delays and speed limits to your scrapers
- Rotating user agent strings to simulate different browsers
- Using proxy servers and frequently rotating your IP address
- Spoofing headers to mimic human browser behavior
However, major websites with sophisticated anti-bot systems are getting better at fingerprinting and detecting suspicious access patterns. Expect to engage in a continual cat-and-mouse game to keep your web scrapers running successfully against bot detection.
A 2020 survey of web scraping professionals by Oxylabs found that 52% had faced IP bans or CAPTCHAs when scraping, and 79% agreed that websites are getting better at detecting and blocking scrapers. As a result, 80% were using proxies and 43% were changing their crawler identities to avoid detection.
Toni Anicic from Oxylabs warns "Many websites, especially large ecommerce marketplaces and social networks, are very aggressive in blocking IPs engaging in apparent scraping activity. Trying to scrape these sites without proxies and other stealth measures is likely to get you banned almost immediately."
6. Potential Legal Issues
The legality of web scraping is a complex issue that depends on factors like the target website‘s terms of service, what exactly is being scraped, and how the data will be used. While scraping publicly available data for research is generally accepted, extracting private user data or copyrighted content to power a competing service could land you in legal trouble.
Some key legal considerations with web scraping:
- Check the website‘s robots.txt file to see if they explicitly prohibit scraping
- Review the website‘s terms of service for restrictions on automated access
- Ensure you are complying with GDPR and CCPA regulations if scraping personal data
- Consider how the scraped data will be used and if it could enable illegal activities
- Respect website owners‘ property rights and don‘t negatively impact their business
As web scraping has exploded, we are starting to see more legal action taken against harmful scraping activities. It‘s wise to consult with a lawyer if you are unsure about the legality of your web scraping project. You may be able to reduce risk by only scraping what you really need or by using an authorized API instead.
There have been several high-profile legal cases related to web scraping in recent years:
- In 2019, LinkedIn won a case against HiQ Labs, with the court ruling that scraping public LinkedIn profiles was a violation of the Computer Fraud and Abuse Act (CFAA).
- In 2020, Craigslist sued Instamotor for scraping its listings to power a competing service, alleging a variety of violations including breach of contract and copyright infringement.
- Also in 2020, French courts ruled that Wish.com‘s scraping of marketplace provider CDiscount‘s site was legal, as the information it extracted was publicly accessible.
As these cases illustrate, the legality of web scraping is still a gray area and decisions can vary based on jurisdiction and specific circumstances. Allen O‘Neill, CEO of DataHen, advises "It‘s always best to err on the side of caution and ensure you have a legitimate claim to any web data you scrape. Avoid scraping personally identifiable information and copyrighted content without permission."
7. Data Quality and Accuracy Issues
Even if you can successfully scrape a website without getting blocked, the data you extract could have serious quality and accuracy issues. There are a number of reasons why web scraped data may be unreliable:
- Websites contain broken links, error pages, and incomplete data
- Content is frequently changing and not always consistent across a site
- Anti-scraping techniques could be feeding bots fake data
- Deriving structured data from unstructured web pages is error-prone
- Scrapers may fail to fully render pages or correctly extract information
Data quality problems compound as you integrate data scraped from multiple inconsistent sources. Without rigorous data cleaning and validation efforts, your dataset could be full of gaps, duplicates, conflicts, and nonsensical values.
Some best practices to improve the quality of web scraped data include:
- Regularly monitoring and spot checking extracted data for issues
- Setting up test cases to verify scrapers are pulling data correctly
- Applying data normalization, deduplication, and anomaly detection in your data pipeline
- Comparing scraped data against authoritative or ground truth datasets
- Augmenting web scraped data with higher quality data from APIs and manual collection
Depending on your data accuracy needs, web scraping alone may not be sufficient. You‘ll likely need to combine it with other data collection methods and invest heavily in data cleaning and quality control.
HiQ Labs analyzed 11 million scraped LinkedIn profiles and found that 10-30% contained inaccurate or out-of-date information. In a similar analysis of 90,000 ecommerce product pages, Scrapinghub found a 12% inconsistency rate between scraped product names and prices and the actual values shown on the pages.
Sanaea Daruwalla, Head of QA at Zyte, explains "Data quality is a huge challenge with web scraping. The unstructured nature of web data, along with anti-scraping measures that can feed scrapers junk data, leads to high error rates. We‘ve found that on average, at least 10-20% of raw scraped data has quality issues that need to be resolved before it can be reliably used."
Bandwidth and Memory Limitations
In addition to the challenges already discussed, web scraping can also run into limitations related to bandwidth and memory usage at scale.
Downloading many large pages, especially those containing images, videos, and other media, can quickly max out your available bandwidth. And attempting to scrape too many pages in parallel can overwhelm your memory resources, leading to crashes or data loss.
Some tips to manage bandwidth and memory when web scraping:
- Focus on extracting only the essential data you need rather than pulling full page contents
- Avoid downloading media files unless absolutely necessary for your use case
- Implement code to check available memory and pause scraping when reaching defined thresholds
- Use queues and asynchronous processing to gracefully handle concurrency
- Leverage serverless and elastic computing resources that can scale bandwidth and memory as needed
Proxy and web scraping service providers are increasingly offering dedicated bandwidth and flexible computing plans to support these needs.
Here‘s what Nick Larsen, Technical Support Engineer at Oxylabs, had to say on the issue: "We frequently see customers underestimating the bandwidth and memory requirements to power their large scale web scraping projects. Trying to run complex scrapers on limited hardware is a recipe for crashes and data loss. It‘s important to do capacity planning upfront and ensure your scraping infrastructure can comfortably handle your target data volumes."
The Future of Web Scraping Limitations
As web scraping continues to grow in adoption and importance, we can expect these key limitations to evolve in the coming years.
On one hand, websites will likely invest in even more sophisticated anti-bot measures as they seek to protect their data and user experiences. We may see wider adoption of scraping protection services like Cloudflare Bot Management, DataDome, and PerimeterX Bot Defender, which use machine learning to detect and block advanced scraping tools.
At the same time, web scraping technologies and services will continue to adapt with new countermeasures and capabilities. Likely areas of innovation include:
- AI-powered scrapers that can automatically detect and adapt to website changes
- More lifelike scraper bots that perfectly mimic human users to avoid detection
- Improved proxy networks and IP rotation techniques to circumvent bans
- Distributed scraping infrastructures to improve reliability and scale
- Automated data quality monitoring and cleansing solutions
It‘s hard to predict exactly how this arms race between web scrapers and anti-bot defenses will play out. But one thing is clear – as data becomes ever more valuable, organizations will continue to push the boundaries of what‘s possible with web scraping technology.
According to Verified Market Research, the global web scraping services market is expected to grow from $1.28 billion in 2021 to $6.49 billion by 2028, a CAGR of 26.3%. This indicates significant ongoing investment in overcoming today‘s web scraping limitations and challenges.
As Gabor Koncz, CEO of Apify, puts it, "Web scraping is still a relatively young field and the tools and best practices are rapidly evolving. While there are certainly limitations to be aware of, we‘ve seen again and again that innovative solutions arise to keep pace with the anti-scraping efforts. I‘m excited to see what the next generation of web scraping technologies will be able to achieve."
Wrapping Up
Web scraping is an incredibly powerful tool for collecting web data at scale, but it does come with notable limitations. From frequently changing website structures to IP blocking to data quality issues, web scraping projects can quickly run into challenges.
Being aware of these limitations from the start will help you plan your web scraping approach more effectively. Using advanced techniques like headless browsers and IP rotation can improve your success rate. Paying for specialized tools and high quality proxy services can make the process much smoother. And allocating sufficient time for data cleaning is critical to deriving accurate insights.
Not every data collection problem is best solved by web scraping. For more reliable and efficient data access, check whether your target websites offer APIs or data feeds, or consider partnering with companies that specialize in web data aggregation. The right approach depends on your specific needs and resources.
With the proper precautions and expectations, web scraping remains an essential arrow in the quiver of any data-driven organization. Just remember that the value is not in the data itself, but in your ability to collect accurate data and translate it into meaningful action.