Web crawling has been there since the time search engines were developed as a means to index the web pages and make them searchable. Apart from that, hobbyists, individuals with professional requirements and companies have always been in need of web data in a structured format for various use cases.
However, the majority of the business requirements increased with the growth of e-commerce, online travel booking sites, job boards and other online platforms that dealt with the structured listing of different products and services. At present, the latest data under the scanner is social media data. And everyone, be it the immigration office or the big banks, want to analyze the public discussion on Facebook and Twitter to gain a better understanding of customers and make decisions. However, extracting such data can be highly complex technically and frequently not feasible owing to legal barriers.
In the last few years, web scraping is not simply confined to extracting text data, there is a growing demand for scraping images and videos to extract available features.
At the same time, scraping multiple webpages concurrently or updating the scraped data at regular intervals simply cannot be undertaken in a DIY project. This is why when companies need data to be scraped, they must have a dedicated team or use an enterprise-grade solution.
The data needs of companies are changing. With the advent of new forms of data, such as social media, data that needs to be stored in new forms of data structures like graphs, the web scraping landscape is also witnessing a massive change. As highlighted earlier, today, videos, audio, as well as pictures are scraped and often, they need to be sorted and stored in groups so that they can be used in a pluggable format.
Since the internet is growing at a rapid pace, the chances of inconsistency in data have increased by many folds and there’s a high chance of issues with data cleanliness when you are scraping high volume data from multiple sources. Hence, data cleaning, normalization and in-built mechanism for data integration have become highly sought after factors. One of the most important is identifying outliers in a data-set and validating them manually. Removal of duplicate data is yet another key factor. In case you are scraping from more than one source, it is vital that data from one source backs up another and there are no inconsistencies.
Along with the cleaning of data, data delivery is another problem faced by companies when trying to integrate a data-feed with the business workflow. Today businesses need data-stream in the form of APIs, or they need the data in a cloud-storage container like AWS S3, from where they can be easily accessed as and when required. All these, in the end, become a part of the scraping and delivery flow.
Cab aggregators are using tech to get you a cab whenever you need one. Everything from groceries to food is being delivered right at your home through tech. Tech is enabling dynamic pricing on everything from flight tickets to the seats at Wimbledon.
But then, the core business of most companies do not involve any tech, and for companies that do not have a separate technical team or web-scraping team, hiring new individuals and creating a web-scraping team to take care of the company’s data needs may prove to be a daunting task.
Also, even if a company has a solid tech team, the common issues associated with web scraping (from the data infrastructure and error handling to the proxy rotation, deduplication, and normation) will take a considerable amount of time to get handled with perfection.
There has always existed an NIH syndrome among organizations, that has made them refuse solutions created by other companies. However, when it comes to web-scraping it is better to take the help of people who are already in the domain and have streamlined the process to tackle the nuances of acquiring clean web data from websites at scale.
The web-scraping landscape has come a long way since its initial days of copying text from webpages. Today there exist solutions that would scrape data from multiple web-pages and ensure a continuous data-stream for your company’s needs. Data is being offered in the form of DaaS (Data as a service), where you can ask for the data points you require and get them delivered in the delivery-method you require.
In such a scenario, you would not need to worry about aspects like infrastructure, maintenance or changes required if the website you need data from undergoes cosmetic changes. You would only be paying for the amount of data you consume, and nothing else.
One of the pioneers in the web-scraping ecosystem, PromptCloud offers a highly customized DaaS solution with multiple additional services. We also run JobsPikr, which is a service that can provide you with a continuous job feed using filters such as location, keywords, job positions, industry, and more.
Our team at PromptCloud was one of the first to identify the pain-points that companies go through when trying to integrate scraped data into their business processes. Companies were even willing to leave data on the table out of fear of the time it would take to get the data or to plug it in the existing system.
This is why we converted the entire work into a simple platform where you could order data just like you order food online, in CrawlBoard. In the latest version of our DaaS platform, you can start a project or add new sites (that are to be scraped) with just one click. For reporting issues, there’s an integrated ticketing system and payment processing for the invoices. Site-specific graphs and visualizations are available along with the upcoming crawl schedules and important details. Quick invoicing and a simple UI make it easier for non-tech business teams to use CrawlBoard with ease.
The future of web-crawling is both complex and simple. Sounds all wrong? Well, let me explain. Due to the advent of new technologies every other day, web-pages may be rendered very differently tomorrow as compared to today, and in such a scenario writing new DIY code every day due to changes in websites might not be a solution.
The good news is that just like companies have decided to depend on Amazon AWS for their infrastructure needs, they can depend on teams like ours to help out with their data needs. Since we work with the biggest names in the industry in their bid to procure clean data, we know the hardships involved and can help you so that you need not undertake them on your quest to gather clean data from the web. After all, no one would want to reinvent the wheel, would they?