9 out of 10 small and medium enterprises now outsource large-scale data parsing services

The best way for businesses to increase their revenue is to introduce new iterations of their products or services. At the same time, it is essential that the masses or user base become aware of them—this is where marketing and advertising come in handy.

However, both product development or improvement and the process of delivering it to the masses today depend on one thing—data. Most of this data is collected through parsing services. This data is used for:

Enhancing or improving a product or service

Whether you sell a physical product or offer a service, you must continually improve it over time. This may include fixing previous shortcomings, implementing changes recommended by users, or adding new features. For example, most car manufacturers release new versions of their bestsellers every year.

You can also develop complementary products or tools that pair well with existing offerings. This is often done by companies based on customer requirements and purchasing patterns observed among clients. For instance, a shoe company might start selling socks, or a healthcare company might begin offering annual medical checkup packages.

Both of the above business decisions require time and money. That is why preliminary data research is vital.

Improving product reach

You may have a great product or a truly useful service, but if the target audience doesn't know about it, your revenue won't grow. Without data, even a ton of marketing spend may yield no benefit.

Data helps you identify the right audience—target age group, gender, region, occupation, and more. Using data in your marketing and advertising campaigns will lead to higher conversion rates at lower costs!

Challenges of large-scale data parsing

Large-scale parsing of data comes with many obstacles. You will encounter them if you try to build DIY solutions using free libraries in languages like Python or free UI-based tools. Although there are dozens of problems when implementing real-time parsing of massive amounts of data, the most common ones are the following:

Data collection speed can be a limiting factor.

Many small and medium enterprises need data from numerous sources that also require frequent updates. In this case, time can be critical—whether for scraping competitor prices or retrieving content from breaking news pages. To speed up the process, you may need to:

• Configure cloud infrastructure in the most efficient way possible.
• Write multithreaded code that can scale and collect data from multiple pages as needed.

If you are scraping data from dozens of websites and thousands or millions of web pages, you may find that data collection slows down or your cloud costs skyrocket due to inefficient resource usage.

Setting up cloud infrastructure correctly and efficiently will consume a significant portion of your parsing efforts.

Large-scale parsing cannot be done on a laptop; you will need to use virtual machines on cloud platforms such as Yandex Cloud, Selectel, or AWS. Setting them up can be easy if you follow some tutorials. The complexity lies in:

• Maintaining cloud infrastructure and keeping cloud costs under control.
• Updating/changing infrastructure strategy as your parsing needs grow.
• Adding new cloud infrastructure, such as data pipelines, for tasks like data cleaning, storage, processing, and more as your business scales.

You must consider the legal implications of scraping.

Before scraping a website, you need to:

• Check the robots.txt file.
• Ensure compliance with data protection and security laws of the country where the website is hosted, the country from which the website's data originates, and the country where you intend to use the data commercially.

With increasing data privacy regulations and laws, complying with point b can be very challenging when dealing with data from multiple sources.

When building DIY solutions, it may be impossible to comply 100% with all laws. While small-scale scraping for research purposes may cause no harm, large-scale web scraping without adhering to data laws can lead to serious trouble. In the past, companies have faced lawsuits for millions of dollars for not following proper data collection, usage, or storage laws.

Websites have many tricks up their sleeves to block crawlers.

They monitor traffic, and if you don't use proxy rotation, you can easily be blocked by websites. Another threat from websites is frequent UI changes, which can render your existing code useless.

This will require re-examining the HTML page format and rewriting code to extract all data points. Similarly, adding new websites can also be a daunting task, even if you are collecting the same data points. The difficulty will depend on how complex the site is and whether it uses the latest technologies. This unknown factor will always remain when adding new websites to scraping solutions.

Advantages of using a DaaS provider

We have only covered free tools and solutions and the problems they can create when used for large-scale website scraping. Paid tools and solutions can solve many or most of these problems, but not all.

The reason is simple—no tool fits all use cases. This is where scraping service providers come in. We also offer additional features and customizations that make web scraping a breeze.

One of the main aspects of website scraping is cost.

We offer a fully managed cloud service with minimal latency, robust SLAs, and on-demand support.

This ensures you don't have to worry about the efforts of scraping data from websites and can start integrating the collected data into your workflow (we offer several cloud-based integration options).

If something goes wrong—for example, a site changes its UI or scraping stops for a particular website—our tracking and monitoring tools immediately get to work to identify the specific issue, which is then handled by our internal teams. SLAs and on-demand support also provide extra peace of mind for clients, as we understand how vital data can be for small and medium businesses.