History
When PromptCloud started operations back in 2009, only few businesses at the cutting edge of technology knew what web scraping was. We had to use a 5th grader version to explain the solution which went like this- “We are like Google for a few websites, but we provide data in a clean format like a CSV or JSON.” Sometimes, we’d also end up explaining what CSV, XML and JSON were and found ourselves more often than not, educating our customers as to why excel was not the right format to consume such volumes of data on a regular basis. That was when we did a lot of educational content around what DaaS (Data as a Service) was, and the difference between web scraping and web crawling. Many others followed suit and the rest is history. This particular blog on the difference between crawling and scraping ended up becoming the most visited page on our website, despite its raw casual tone.
We only had the horizontal crawling solution then, which was a simple DaaS platform, and even then we had customers from across industries- Automotive, eCommerce, Travel, amongst many others. We used to be amused by some of the use cases we’d come across, things we hadn’t even imagined web scraping would solve for. It’d be an understatement to say that a lot of our value added services, including developing the API to deliver the data feeds, was a response to customer needs as opposed to us being the visionaries.
Fast forward 15 years, a lot has changed while some of the basics still remain. There’s no more education needed on why a business needs alternative data, or what web scraping is. Earlier, only 2% of the websites on the internet didn’t want themselves crawled, now that number has clearly gone up as more and more domains employ anti-bot technologies. Our top FAQ earlier was if web scraping was legal, whereas now more businesses understand how to do it ethically. The use cases too have been evolving quickly, keeping pace with the other technological advancements and internet penetration as we see it.
The Present
Let’s take a look at where we are right now against the backdrop of what we experienced in the past.
1. More Businesses Recognize the Need for Data
The demand for a solid web scraping service continues to grow because businesses need real-time insights to stay ahead. We have witnessed the needle move from nice to have to a must-have. And as competition gets fiercer, companies see web scraping as a game-changer rather than just another tool. It’s interesting to note that the needs have grown mostly in the eCommerce space, and not so much in the other industries we’d earlier serve.
2. The Scale of Data Needs Has Changed
It’s not just about needing data—it’s about needing a lot of it. Companies don’t just want a snapshot; they want real-time, constantly updating datasets that help them stay ahead of trends. Take the use case of labor market analytics for instance. In order to be able to derive meaningful insights on how jobs are trending, a few thousand jobs wouldn’t provide statistically significant data. You need atleast a few hundred thousand job postings from a particular category to draw out a pattern on which skills are trending, what are the hotspot locations for a particular job title, and so on. This shift means businesses are looking for complex web scraping solutions that can handle massive amounts of data efficiently and in real-time.
3. Trends Shape the Kind of Data Businesses Seek
What businesses need from web scraping evolves with trends. The two big ones that seem to be shaping the scraping landscape right now are quick commerce and social media. With the proliferation of brands ranging from Beauty and Personal Care to FMCG, combined with the promise of 10-min delivery apps, especially in India, it has become imperative to monitor the digital shelf. Same is the case with Social media with the advent of Instagram and other popular channels. More brands rely on social media as a primary channel to track consumer sentiment and emerging trends.
4. More Robust Systems for Data Ingestion
Back then, if a customer came up with a requirement of crawling 200 websites OR where millions of data points had to be delivered on a daily basis, our first question would be- is this a spam requirement? Because the systems weren’t sophisticated enough to handle such volumes of data, and something or the other would break. Now most businesses we work with have built powerful data pipelines, real-time processing systems, and cloud storage solutions that make ingestion seamless. This means they get to focus more on insights than worrying about how to handle the data.
5. Public Data Is Becoming Less Accessible
Web scraping isn’t as simple as it used to be. More and more websites are locking their data behind paywalls, login requirements, and bot-detection systems. That’s forced the industry to get creative with complex web scraping methods that can legally and efficiently work around these barriers. AI-driven tools have become essential in keeping up with these ever-tightening restrictions. We usually price our crawling projects based on the complexity of sources ranging from simple, medium and complex, and we’ve seen more and more websites fall under the complex category over the last couple of years.
6. Experience Matters More Than Ever
With data demand booming, new players are popping up claiming they can scrape anything and everything. But here’s the thing—experience matters. As a corollary to the above point, web scraping isn’t just about pulling data; it’s about handling dynamic websites, managing large-scale operations, and ensuring data accuracy. An experienced web scraping provider has spent years troubleshooting issues, fine-tuning processes, and building solutions that actually work at scale.
7. AI Is Revolutionizing Web Scraping
While a large portion of the data pipeline was earlier automated, we’ve had some breakthroughs in the configuration stages of the pipeline. The possibilities with using AI for various phases of the data pipeline is endless- accurate extraction can become easier, crawlers can be trained to identify website changes and fix themselves automatically, structuring of data can become simpler. Machine learning is also helping businesses go beyond raw data—offering insights, classifications, and analytics that make scraped data even more valuable. All this to say that AI has revolutionized this industry in a good way, enhancing the capabilities beyond scraping and alleviating the pains of gaining insights from the piles of data gathered.
Road Ahead
Web scraping has come a long way in the last 15 years, and it’s still evolving. With data becoming more critical than ever, businesses need partners who get it—who understand the intricacies of complex web scraping and have the experience to navigate its challenges. Whether it’s ensuring top-notch data quality, handling website restrictions, or using AI to make scraping smarter, the right approach makes all the difference.
One thing’s for sure: the demand for structured, actionable data isn’t slowing down anytime soon. The only question is—are you ready for what’s next?
FAQs
1. Is web scraping legal?
Web scraping legality depends on how and what data is being scraped. Publicly available data is generally permissible, but scraping private or protected data without consent can lead to legal issues. It’s always best to follow ethical and legal guidelines. Read this blog to know more.
2. Why do businesses rely on an experienced web scraping provider?
Handling large-scale, dynamic websites requires expertise. An experienced provider ensures accuracy, compliance, and efficiency while navigating technical challenges like CAPTCHA bypassing, IP rotation, and website structure changes.
3. How has AI changed web scraping?
AI has enhanced web scraping by automating data extraction, predicting website changes, and improving accuracy. AI-driven solutions help businesses get more refined and meaningful data beyond simple scraping.
4. What industries benefit the most from web scraping?
Industries like e-commerce, finance, real estate, healthcare, and social media analytics rely heavily on web scraping to gain competitive insights, track market trends, and enhance decision-making.
5. How do companies handle massive amounts of scraped data?
Modern businesses use cloud storage, real-time data pipelines, and structured processing frameworks to ingest, clean, and analyze large datasets efficiently.