Many businesses today rely on data-based decisions, and web scraping is the main method to gather large amounts of information from different sources.
However, websites are becoming a more challenging target every year. They frequently update structure and layout, include dynamic elements, and apply advanced anti-bot measures.
These roadblocks and the need to optimize business operational costs cultivate the transition from in-house web scraping to cloud-based services.
In-House Web Scraping: Is it Still Worth it?
In-house web scraping, otherwise known as local scraping, is the process of developing and maintaining self-built web scraping tools within an organization or individually.
Local web scraping begins with building custom scripts. Such tools are written in programming languages like Python, Ruby, or JavaScript to navigate websites, parse HTML, and extract data. It also includes setting up the necessary infrastructure for hosting the scraper (often Amazon AWS) and storing the output.
Setting up an in-house infrastructure is costly at the beginning. Businesses need to invest in a developer, to be exact, the developer’s time spent building a scraper and expertise. For example, a freelance developer can cost from $30 to $150 per hour. A simple script may take several hours to build, but this fails to account for maintenance, scaling, and infrastructure costs, including proxies.
In-house infrastructure can be more cost-effective than using third-party services in the long-run. However, it requires a level of scale and commitment not every company can afford.
Challenges of In-House Web Scraping
Let’s have a look at some of the particular challenges businesses encounter while running their scraping operation in-house. These roadblocks are associated with the changing nature of websites and the need to navigate through complex structures.
Dynamic content. Many modern websites load content through JavaScript. Traditional web scraping tools like Requests and BeautifulSoup can only extract static HTML content. As a result, developers are increasingly forced to rely on browser-based scraping, which is an order of magnitude more complex and resource-intensive.
Anti-bot systems. Websites often apply various anti-scraping measures to prevent automated data collection. For example, Google uses reCAPTCHA and Kohl’s, an American e-commerce store – Akamai services. Overcoming these and other systems requires knowledge and experience, which go far beyond simple techniques like changing the user-agent.
Structural changes. Websites have different structures and layouts. This requires building a separate parser for each website. Even worse, if a website makes any change to the structure, it may cause the scraper to break. So, there’s a need to constantly maintain the self-built tool to adapt parsing logic and error handling.
Proxy servers. Proxies and web scraping go hand in hand. To overcome IP bans and blacklisting, you need to choose the appropriate type of proxy server and then maintain a pool of IP addresses to avoid detection. There’s also a need to monitor proxy usage and implement rotation. Balancing the cost and performance adds another layer of complexity.
What Is Cloud-Based Web Scraping
One could say that much of web scraping is already cloud-based, as engineers prefer to host their code on geographically-relevant remote servers. However, at this point most tasks are still carried out manually, simply not on premises.
To save engineering effort and operational costs, businesses are increasingly choosing to offload parts of their operation to data infrastructure providers like Bright Data. The first target is of course proxy servers, as quality IPs like residential proxies are uneconomical to source in-house. But lately, there’s been an increasing demand (and supply) to outsource website unblocking, infrastructure scaling, or even the full data collection cycle to specialists.
Cloud-based web scrapers come in various shapes and sizes. In the case of Bright Data, there are three types of services to choose from:
- Web scraping APIs and proxy APIs. When you send a request to the API, it opens the webpage and retrieves the information. The user only needs to write a portion of the code while the tool performs most of the work, including handling anti-bot measures. The major difference between the two tools is their integration method – proxy APIs replace proxy servers with very few adjustments.
- Scraping browser. The tool gives access to cloud web browsers. Similar to web unblockers, it fetches data while handling anti-bot mechanisms. Such scraping browsers are controlled using the CDP protocol with libraries like Playwright and Puppeteer, which provides more control over the browser.
- Cloud-based platforms. Cloud-based scraping platforms have the most features. Such tools provide a user-friendly interface where you can write and execute scripts, manage the data extraction workflow, and store the scraped data on the cloud. With cloud-based platforms like Web Scraper IDE, users can perform end-to-end web scraping tasks without managing infrastructure or setting up complex systems locally.
Why Choose Cloud-Based Tools?
Here are the main reasons to choose a cloud-based tool:
- Easily scale up or down. Most providers offer different packages ranging from small plans catered to individual users to enterprises that need to scrape large amounts of data.
- There is no need to run a headless browser on your own. With local web scraping tools, you need to run a headless browser on your own. Cloud-based services handle this remotely for you.
- Bypass anti-bot systems. Cloud-based web scraping services come with built-in proxy management. They also apply techniques like IP and user-agent rotation or request throttling to mimic human behavior and avoid detection.
- No maintenance. Cloud-based services offload the burden of maintaining and managing infrastructure. The service providers handle server maintenance, software updates, and other technical aspects that allow you to focus on scraping tasks.
- One point contact. When you subscribe to a service, you can access and manage the scraper via the dashboard. This simplifies the scraping workflow by allowing you to work in a single environment. In most cases, such services are large enough to cover the needs of individual users and enterprises.
However, cloud-based services aren’t without flaws. Users have less control over the resources, as they’re limited to the specific features and functionalities provided by the service.
Another thing to consider is that even though cloud services have flexible prices, the cost can soar once your data needs increase. For example, JavaScript rendering is a very common price modifier because a full browser is more taxing than an HTTP library.
The Bottom Line
While in-house infrastructure offers absolute control and customization, it comes with challenges such as scraping dynamic content, dealing with IP blocks and resource management.
Cloud-based web scraping services, on the other hand, can easily navigate through modern websites by solving most of the obstacles for the user. As a result, businesses can focus more on extracting data rather than grappling with technical complexities.
