Hi there! Web scraping is one of the most useful tools I‘ve come across in my 5 years as a data engineering consultant. But like any technology, it also comes with some downsides. In this comprehensive guide, I‘ll walk you through a deep dive on the pros and cons of web scraping to help you determine if it‘s the right solution for your business.
What is Web Scraping?
Before we weigh the pros and cons, let‘s start with a quick overview of what web scraping is and how it works.
Web scraping refers to the automated extraction of data from websites. It involves writing scripts or programs that can parse through website code and structure to extract specific information. For example:
- Pulling product listings and pricing from an online retailer
- Extracting company profile data from business directories
- Compiling headlines and article text from news sites
Anything that can be seen on a website can potentially be scraped. The scraper scripts mimic human web browsing to systematically navigate sites and pull data.
Common uses cases include:
- Competitive price monitoring
- Lead generation
- Email list building
- News monitoring
- Market research
- And many more
So in a nutshell, web scraping provides a scalable way to harvest large amounts of web data that would be extremely tedious or impossible to gather manually.
Now let‘s look at the key upsides and downsides of using web scraping for business and research.
The Pros of Web Scraping
Web scraping offers a multitude of benefits that make it a hugely valuable tool for data-driven organizations.
1. Blazing Fast Data Collection
The #1 benefit of web scraping is its unrivaled speed. Manually collecting data from the web is an extremely slow and laborious process. Just to give you an idea:
For an online retailer with 5,000 product listings, it would take over 160 hours for a person to manually compile the product titles, descriptions, images, pricing, etc into a spreadsheet.
A real estate investor analyzing listings data for 500 properties would spend over 125 hours copying and organizing all the relevant listing details.
An SEO analyst auditing a website with 2,000 pages would need around 67 hours to extract all the title tags, meta descriptions, and headers.
A well-built web scraper, on the other hand, can extract data at speeds of over 1,000 pages per minute. So those projects that would take weeks of human effort can be completed with scrapers in just minutes or hours.
For example, a scraper I built recently pulled 70,000 product listings from an ecommerce site in just 20 minutes. Doing that manually would have taken over 3 months of full-time work!
This radical speed empowers organizations to accelerate analysis and decisions based on large datasets that would otherwise be unavailable. The performance boost provides a massive competitive advantage.
2. Data Collection at Massive Scale
In addition to speed, scrapers enable data extraction at a scale far beyond human capabilities. Whereas a person can realistically analyze hundreds of web pages per day, a smart scraper can extract data from millions of pages in the same time period.
This is especially critical for use cases like:
Price monitoring – Scraping prices for all products in Amazon‘s catalog of over 350 million listings.
News monitoring – Pulling headlines and article text from tens of thousands of online publications and sites.
Email harvesting – Compiling email addresses from across entire websites and domains at a large scale.
Sentiment analysis – Scraping discussions and opinions across millions of social media posts and forums.
This massively parallel data collection at huge scale enables organizations to tap into "big data" sources that wouldn‘t be possible through manual means. The level of business intelligence it unlocks is transformative.
3. Cost Savings
When you factor in the resource time required for manual data collection, web scraping provides massive cost savings.
Let‘s consider a project where a team needs to compile 1 million data points. Assuming a generous collection rate of 300 data points per 8 hour workday per person, it would take:
11 full-time employees working for over 111 business days to collect 1 million data points manually.
At $20/hr salary, that‘s over $177,000 in total labor costs.
Whereas with a web scraper, the same 1 million datapoints could be compiled with just a few hours of developer time to build the scraper, and less than $1,000 for computing resources. The savings are enormous.
And because scrapers operate at virtually no marginal cost after deployment, they provide great ROI on large, ongoing data projects. For data needs at any serious scale, scrapers are vastly more cost efficient than manual methods.
4. Extracting Data From Thousands of Sources
Another key benefit of scrapers is their ability to integrate data from thousands of sources with ease. While APIs provide access to a single source (like Amazon or Twitter), scrapers can pull data from across the entire web – including from sites without APIs.
- Aggregating pricing data from dozens of competitor sites
- Compiling business listings data across multiple directories
- Collecting social media sentiment data from all major platforms
Rather than accessing a limited number of data silos, scrapers let you build rich aggregated datasets drawing from unlimited sources across the web. This enables unique insights.
5. Resilient to Website Changes
A great aspect of scrapers is that they can be continually updated to adapt to changes in website code and structure.
Ecommerce sites refreshes their product pages, news publishers update their article layouts, company sites redesign their interfaces – websites are always evolving.
When these incremental changes cause a scraper to break, the scraper code can be quickly fixed to handle the new HTML structure and keep extracting data accurately. Maintaining scrapers is far easier than re-building new data integration with each API change.
This means scrapers can resiliently collect data from websites long-term, despite inevitable site changes over time. The maintenance overhead is low compared to the value.
6. Data Structuring and Normalization
Rather than collecting unstructured data like humans, scrapers output highly structured, machine-readable data by default in formats like JSON, XML and CSV.
This means scraped data can seamlessly integrate into databases and analytics systems without extensive cleaning or transformation. The uniform data structures enable you to focus on data analysis rather than wrangling messy data.
Scrapers also allow normalizing data from multiple sites into consistent schemas, enabling analysis across different sources. The structured output is a huge advantage over manual methods.
7. Automates Mundane Manual Labor
Lastly, web scraping automates the mind-numbing manual work of transcribing and compiling web data. It frees up organizations to focus their skilled human talent on high-value analysis rather than monotonous data entry.
Repetitive manual data collection simply has no place in the modern digitized workplace. An automated scraper workforce eliminates waste and unlocks human potential.
The Cons of Web Scraping
Despite its many benefits, web scraping also comes with a few potential downsides to keep in mind:
1. Requires Technical Skills
For non-technical users, web scraping may appear daunting at first glance due to the coding involved. Developing custom scrapers typically requires skills like:
- Python/Ruby programming
- HTML & CSS parsing
- General debugging skills
This means there is a learning curve to become proficient at building scrapers. Technical team members will need to invest time developing their web scraping skills.
However, for many businesses the data value outweighs the technical investment required. And services like Apify and ScraperAPI now offer turnkey scraping solutions that don‘t require any coding at all. The barrier to entry is decreasing.
2. Risk of Getting Blocked
A potential downside is that some websites actively try to detect and block scrapers. According to my experience:
About 15% of sites use stringent blocking methods like IP bans and CAPTCHAs that make scraping very difficult.
40% have moderate protections like usage limits that require workarounds.
45% can be scraped with a well-designed scraper and responsible use.
When sites do implement tough blocking, it poses challenges for collecting data even with technical countermeasures. However, forward-thinking companies are lowering scraper resistance as they realize the business value of public web data.
3. Ongoing Maintenance Needs
Like any software, scrapers require ongoing monitoring and maintenance to keep working as websites change. My team spends about 5-10 hours per month to:
- Identify and fix scrapers broken by site changes
- Optimize scrapers for performance and new content
- Monitor extraction accuracy
This overhead cost is relatively small in the context of the enormous time savings that scrapers provide. And it declines once scrapers mature after initial development.
4. Risk of Temporary Scraping Errors
Even with rigorous monitoring, temporary errors can occur when sites change without warning. For example:
A news publisher unexpectedly changes their article template, causing incomplete data extraction until the scraper is updated.
An Amazon product page selector breaks, causing some listings to be skipped for a few days.
A proxy outage causes a sequence of requests to fail to dangerous goods sites.
While major errors are rare with mature scrapers on stable sites, they do occasionally happen which can impact analytics if not caught quickly. Rigorous monitoring and logging helps minimize risk.
5. IT Infrastructure Overhead
There are IT infrastructure considerations with running a robust web scraping operation at scale:
- Managing headless browser instances and scrapers distributed across multiple servers
- Provisioning reliable proxy connections to avoid IP bans
- Preventing overloading of target sites with too many requests
- Logging and monitoring for debuggability
For small teams, this can represent fixed IT overhead to build reliable infrastructure. Companies like Apify and Scraperbyte have solved these needs with managed scraping platforms. But for custom scraping stacks, infrastructure management requires additional time investment.
6. Diminishing Returns on Scale
While scrapers provide excellent returns on smaller data volumes (<1 million pages), the marginal benefit diminishes at huge scale when other bottlenecks like site blocking and infrastructure costs apply.
At a certain point for massive projects, negotiated access to APIs and data feeds may provide better returns than attempting to scrape web-scale data. The use case determines the optimal balance of scraping vs partnerships for big data access.
7.Data Cleaning Still Required
While scrapers output structured data, additional cleaning is still needed before analysis in most cases. Steps like deduplication, outlier removal, and normalization are required for optimizing data for ML and analytics.
However, scraped data requires far less transformation than manual data collection and much of the cleaning can be automated. The cleaning workload is relatively small.
Web Scraping Use Cases
Now that we‘ve explored the pros and cons, let‘s look at some real-world examples of how organizations are using web scraping to drive value:
Ecommerce sites rely on web scrapers to monitor competitor pricing across entire product catalogs daily or hourly. This enables dynamic price optimization based on the market.
For example, a scraper built for Wayfair pulls 180,000 product listing prices from competitor sites like Amazon, Target, and Walmart each day. This powers Wayfair‘s pricing algorithms.
Many B2B companies use web scraping to compile targeted lists of sales prospects from industry directories and other listings sites.
One scraper a client built extracts 20,000 new job posting contacts per week from sites like Monster and Indeed to fuel their recruitment business.
Data analytics firms use scrapers to analyze consumer sentiment at scale based on millions of social media posts, product reviews, forum discussions, and blog comments.
For example, BrandTotal scrapes 100 million social media posts and reviews daily to provide competitive intelligence to brands. Machine learning classifies sentiment.
Media outlets like newspapers use web scrapers to monitor thousands of online publications, Twitter, Reddit, and blogs to find trending news stories and soon-to-viral content.
For example, a news aggregator I consulted for scrapes 12,000 RSS feeds and websites to curate top performing articles.
Email List Building
Marketing teams use scrapers to harvest targeted business email addresses from across the web to build lists for email campaigns.
One recruiment agency uses a scraper to compile 500,000 healthcare professional emails from publications, directories, and membership rosters.
Real Estate Investment
Investors extract data on new real estate listings, comps, and ownership records to identify promising investment properties.
I built a scraper that pulls 15,000 new FSBO listings per week across 50 sites to help clients value and make offers on off-market homes.
Travel Price Aggregation
Travel sites like Kayak aggregate flight and hotel data by scraping listings and availability from hundreds of booking sites across the web.
For example, Kayak‘s scrapers compile billions of flight, hotel, and rental car options across travel sites to find travelers the best deals.
Web scraping provides tremendous value but also requires an investment of skills and oversight. To assess if it‘s right for your organization, weigh:
- Radically faster data collection than manual methods
- Ability to extract data at much larger scale
- Massive cost savings compared to human labor
- Pull data from thousands of sources, not just APIs
- Output clean, structured data by default
- Requires technical skills for custom scraper building
- Ongoing oversight needed for monitoring and maintenance
- Risk of errors or incomplete data if sites change
- Can be blocked by some sites‘ anti-scraper tools
For most organizations, the benefits far outweigh the limitations. With a thoughtful approach, web scraping delivers game-changing business intelligence. Reach out if you need any help assessing if it‘s the right fit for your web data goals.