Table of Contents
show
Most brick and mortar businesses have taken to the web today. For any business that goes digital, data is of utmost importance. A lot of this data is used for making business decisions. From deciding the prices of goods and services to getting an idea of the competitors, uses are manyfold. Most of this data used by businesses is scraped from the web. A large percentage of these businesses are not, however, tech companies. And there is a constant conundrum between whether to use web scraping tools, set up an in-house web scraping team, or use a DaaS solution.
Web Scraping Software and Tools
By saying that these companies are not typically tech companies, what we mean is that they might not have an in-house support-team for such technologies. Outsourcing could be a better solution, helping them keep the optimum cost of creating and maintaining such requirements. Whenever there is a requirement for scraping data, these companies usually take to no-code solutions and tools that come with higher cost and, more importantly, certain restrictions.
The first problem is that once companies commit to a specific web scraping software, they are tied to it for at least a year, if not more, due to the service agreement. Even if there are issues while scraping new websites, or if some websites using new tech stacks cannot be scraped, or if some other bottlenecks are identified, you are stuck with the same software because you have signed up for it.
Another important issue here is that when you decide to use a specific web-scraping tool for gathering data for your business requirements, you would typically choose some people from your business team to used to learn how to use these tools, and run them on various websites. While these tools do not require coding, they do have a learning curve, and unlocking all the features may require some experience with the tool. Changing the tool frequently or even yearly may prove to be a major hassle for the business due to the re-learning process involved.
Having your business team or a part of it devote its time to data scraping may also have other ill effects. Debugging issues, changing the configuration to scrape new websites, handling changes in UI of websites. And more may take up a lot of time for the business team and this, in turn. This will reduce their efficiency in the actual aim, that is, growing the core business. Other requirements, such as cleaning the data, plugging the data into the business workflow, and creating visualizations from the data would also add to the workload of the business team with time. When you use a web scraping tool, you are the one in charge of maintaining the quality of data and keeping it error-free. This would become challenging as you scrape data from tens of websites.
The Challenges Involved in Building your Web Scraping Team
As for companies that do have their tech teams. Such as eCommerce businesses that build and maintain their websites, handling a web scraping system would add to the responsibilities of the tech-team. Building a system that scrapes data from multiple webpages at frequent intervals in itself is a difficult task. Setting it up on cloud services, maintaining the system. Debugging it when issues crop up, and adding code to handle newer websites and technologies, can prove to be a massive overhead that may affect your product’s release cycles.
Most importantly, having a tech team is not the same as having an in-house web scraping team. Most tech teams involved in website or software development consist of backend and front-end engineers. To have some of these developers build you a web scraping engine. You would require developers with prior experience in scraping data from multiple webpages and cleaning and cataloging unstructured data. Since web scraping is popular only in a few languages, such as Python, you will need developers who are experts in the language. In case you want to host your web scraping solution in the cloud. The developers will also need experience with cloud services such as AWS and typically should have built a data processing workflow earlier.
Hiring new members as part of your tech-team to take care of the web scraping requirements is possible but not efficient when it comes to the cost standpoint. You may not always need heavy maintenance of the scraping service. You may or may not add the same number of websites to your scraping list every month. Hiring new software developers and building a web scraping team makes sense only if your business revolves around web-scraping. Otherwise, putting in the time and money into building a dedicated team may not be the best fit for your business.
The Pros and the Cons of Scraping In-House
When scraping in the house, the most important factors taken into consideration are:
a). Fixed Cost: No matter what your volume of data-scraping is, you would always be having a fixed cost. This may be because you have subscribed to a web-scraping tool that has a fixed yearly or monthly charge. Because you need to pay the salary of developers who are working on and maintaining your web scraping engine.
b). Infrastructure: Most web scraping systems need to run all the time, or run at a fixed interval, so that you have a fresh data feed at all times. Such systems usually need to be deployed on the cloud. Since hosting it on a laptop or a PC can lead to errors and issues. This means your team should be able to adapt with one of the cloud providers, like AWS or GCP. Also, cloud services not only need hosting but also debugged or upgraded as and when required. You would also need to keep a check on your cloud charges and make changes to your architecture from time to time to keep those charges down.
c). Maintaining The Code: No matter which one you are using, an in-house team, a software tool, or a self-built web scraping engine, errors are bound to occur, web pages that were being scraped already, are bound to have UI changes. All these will need handling by the team in charge from time to time.
At the same time, there may be a few pros as well:
a). If your business revolves around scraped data. Say you curate scraped data to provide meaningful information to customers. Or if you scrape data in real-time to produce some insights; in that case, you may go for a self-built web scraping engine.
b). In case your requirements for web scraping are sparse, and not directly connected to your business requirements; then, you may have a software developer scrape some data for you from time to time.
c). If you already have a mature team that is working on cloud infrastructure. And has previous working experience with web scraping technologies. You may go for an in-house solution after weighing the costs in both cases.
DaaS Could Be The Right Solution
When it comes to DaaS (Data-as-a-Service) solutions. The biggest benefit for the companies is that they only pay for the data that they need. There are no fixed charges. Also, you can add websites to your list by clicking a few buttons. Or have changes in the existing websites handled automatically.
Unless you are scraping massive quantities of data. At regular intervals and your business itself is based on data scraped from the web. It is better to go for a DaaS solution as compared to using paid tools or building your in-house web scraping team. Cost-effective, hassle-free and you get to focus on your core business areas.
Our team at PromptCloud believes that using data to make data-backed decisions is very important today. Hence, we make sure that the transition that companies need to make to integrate data pipelines is much simpler. We take the requirements from you and provide the data in an easy to consume format. This way, there is minimum disruption for the businesses that are moving to data-backed solutions.
We provide different options for businesses that need to plug in scraped data into their system in a specific format. Along with multiple data storage solutions. DaaS solutions like ours not only make your web scraping costs lower but also remove the maintenance. Such as the hosting, and infrastructure costs from the picture entirely. The biggest benefit is that we take care of data quality and cleanliness. For whichever website that you need to scrape data from.
If you liked the content above, we are sure our article on Common Mistakes of In-house Web Scraping System would be of interest to you.
