Big data is becoming an integral part of solving the real world problems. Data on the Web is
crucial for solving these problems. But it is not in a structured format which makes it unusable
for business use. That is where web scraping comes into action. Web scraping helps to extract structured data from the web.
Web scraping is not as simple as it sounds. Companies like us which works in this domain addresses a lot of challenges in delivering data. Here are some thoughts on those challenges.
Scalability: The ability to scale without breaking the existing systems is a challenge that every
web scraping company addresses. At Datahut, we work mostly with startups. A common scenario we see is the sudden scaling of scraping, from less than ten web sites to 50+. This usually happens when they close a round of investment or their MVP made sense. You can’t scale things by adding some servers. If you need a scalable system, it should be designed to scale. Period!
Quality: We live in the era of Data-driven decision making. The quality of data can seriously affect the accuracy of decisions. Data quality problems cost Billions of dollars each year globally. Resolving data quality problems are often the biggest effort in a data delivery. The only way to solve this problem is to have the right technology built in. A little manual intervention is also required because we can’t blindly trust technology.
Accessibility: Businesses ranging from startup in a garage and big corporations need access to data. Providing access to Data at a cost that makes sense for their business is a challenging problem. Using open source technologies is a way to cut down costs. Netflix charges as little as $8 per month for its service because everything is built on open-source software. We use open source technologies like Scrapy in the same spirit to help our customers to get data at affordable prices.
Have some thoughts? Let’s discuss, please comment below.