Not only do businesses aim to leverage public data by collecting it and performing its analysis, but they also want to do so in the most cost-effective way. Easier said than done, right?
In this article, we’ll discuss the key factors influencing data acquisition costs, the advantages and disadvantages of in-house scraping solutions, and some ways to reduce data acquisition expenses.
When it comes to data acquisition costs, there are a couple of factors that affect them. Let’s take a closer look at each of them.
Some targets tend to implement bot-detection mechanisms to prevent their content from being scraped. The targeted sources' precautions will define the technologies needed to access and retrieve the public data.
Server restrictions mainly take the form of a header check, CAPTCHA, and IP bans.
HTTP headers are one of the first things websites look at while trying to determine whether a real user or a scraper is accessing their website. The main purpose of HTTP headers is to facilitate the further transmission of the request details between the client (internet browser) and server (website).
There are various HTTP headers, and they contain information about the client and the server involved in the request. For example, the language preference (HTTP header Accept-Language), recommendations regarding what compression algorithm should be used to handle the request (HTTP header Accept-Encoding), browser and operating system (HTTP header User-Agent), etc.
Even though a single header may not be very unique as plenty of people use the same browser and operating system version, the combination of all headers and their values are likely to be unique for a particular browser running on a specific machine. This combination of HTTP headers and cookies is called the client’s fingerprint.
If a website finds the header set suspicious or lacking information, it may display an HTML document that contains false data or ban the requestor completely.
That’s why it’s crucial to optimize the header and cookie fingerprint details transmitted in a request. This way, the chances of getting blocked while scraping will significantly decrease.
CAPTCHA is yet another validation method used by websites that want to avoid being abused by malicious bots. At the same time, CAPTCHA is a serious challenge for the benevolent scraping bots that intend to gather public data for research or business purposes. CAPTCHA may be one of the responses that the targeted servers will throw at you if you fail the header check.
CAPTCHAs come in various forms, but these days they mostly rely on image recognition. It complicates things for scrapers as they are less sophisticated than humans in visual information processing.
Another common type of CAPTCHA is reCAPTCHA containing a single checkbox you need to click on to prove you’re not a bot. Seemingly simple action turns out to be quite tricky, as it’s not the checkmark that the test is looking at but the path that leads to it, including the mouse movements.
Lastly, the most recent type of reCAPTCHA doesn’t require any interaction. Instead, the test will look at a user’s history of interacting with web pages and overall behavior. Based on these indicators, in most cases, the system will be able to differentiate between a human and a bot.
The best thing you can do is avoid triggering CAPTCHA altogether by sending the correct header information, randomizing the user agent, and setting up intervals between the requests.
An IP block is the most radical measure web servers can take to ban suspicious agents from crawling through their content. If you fail to pass the CAPTCHA test, the odds are that you’ll receive an IP block shortly after it.
It’s noteworthy that putting some additional effort into avoiding an IP block in the first place is a better tactic than dealing with the consequences once it’s already happened. Thus, you need two things to prevent your IP from being banned: an extensive proxy pool and a legit fingerprint. Both are pretty demanding regarding resources and maintenance, thus affecting the overall cost of public data gathering.
Server restrictions you may face.
It follows from the previous part that you must develop technologies perfectly tailored to your targets to succeed in web scraping and avoid unnecessary hassle.
If you’re considering building an in-house scraper, you should think about the entire infrastructure and dedicate resources to maintaining relevant hardware and software. The system may include the following elements:
Proxies are irreplaceable helpers during every web scraping session. Depending on the complexity of the target, you may need Datacenter or Residential Proxies to help you access and fetch the required content. A well-developed proxy infrastructure comes from ethical sources, includes multitudes of unique IP addresses, offers country and city-level targeting, proxy rotation, unlimited concurrent sessions, and other features.
Simply put, APIs are the middlemen between different software components that enable two-way communication between them. APIs are a crucial part of the digital ecosystem as they help developers save time and resources.
APIs are being actively implemented in various IT fields, and web scraping is no exception. Scraper APIs are tools created for data scraping operations at a large scale.
When deciding whether to invest in developing an in-house scraper or outsource it to another company, you should first consider what requirements your web scraper must comply with. These requirements will be defined by the scope of your data needs, the frequency and speed with which you expect your data to be retrieved, and the complexity of your targets.
One of the advantages of an in-house scraper is that it is highly customizable. In other words, you can tailor it to the unique demands of your project. You’re in total control of the scraper, and it’s only up to you to decide how you want to build and maintain it.
If web scraping and data gathering are at your business's core and you have sufficient experience and dedicated resources to invest in an in-house scraper, then it might be the best pick for you.
Although in-house web data scraping may satisfy data acquisition needs pretty well in some cases and can also be affordable in terms of finances at the start, it has its drawbacks.
As your data collecting needs may grow over time, you will need to procure a scraper infrastructure that can be easily scaled up. It will require enormous resource commitment. So if data extraction and web scraping are not the focus area of your business, it will be more reasonable to outsource scraping solutions.
Data collection costs are defined by the requirements you set for your project and the technologies used to handle your targets. Overall, you should collect all the information about your data sources. Note that some data sources might ask you to pay a monthly fee to give you access to their up-to-date APIs.
So, before you start your project, you should figure out whether you’ll be able to access the data on their servers freely or you’ll have to draft up a data agreement with them. Once you know that, you can start estimating your data project cost. A preliminary formula will look like this:
Number of data sources * Average monthly data access costs
However, it’s important to note that this will only estimate costs related to your data sources. You should also add costs associated with the nature of your workflow. For example, suppose you decide to collect public data with in-house solutions. In that case, the final cost must be calculated based on the costs of proxy infrastructure, APIs involved in the process, computing resources, and others. In some cases, it might be cheaper to use a Scraper API.
If you decide to stick to the latter after weighing the cons and pros of in-house scrapers and outsourced ones, you should consider using Scraper APIs for data harvesting.
Here are the main features of Scraper APIs that make them almost undefeatable when it comes to public web data extraction:
Resistant to the bot detection responses, including CAPTCHAs, IP blocks, etc.
Integrated proxy infrastructure and customizable results delivery to the cloud storage of your choice.
An embedded auto-retry functionality will make sure that you get the results delivered successfully.
Structured and ready-to-use data in JSON will make data management and analysis easier while reducing data cleaning and normalization costs.
Full of useful features aimed at seamless data extraction, Scraper APIs are perfect helpers in large-scale data gathering from the most challenging targets.
As you can see, the factors influencing the data collection costs are also the main technological challenges that scrapers face. To make the scraping process cost-effective, you must successfully utilize tools capable of handling your targets and all possible anti-scraping measures. Such public data-gathering solutions as Scraper APIs can be of great help here.
How much does web scraping cost?
There’s no universal answer to this question since web scraping costs depend on many factors. Some of them are whether you perform it in-house or outsource it, the scale of your project, the complexity of the targets, and others.
Why is collecting data expensive?
In most cases, data collection becomes expensive because of technology maintenance as well as data treatment costs. This is especially true for in-house data collection. Indeed, you must have engineers, IT, and DevOps specialists who will build and maintain hardware and software for your data collection processes.
In addition, for the data to be worth the investment, you need to do data cleaning. As a result, it’s essential to build a robust data management system that will allow you to access the most valuable information and pass it on to decision makers. Alternatively, you can use a service that performs data cleaning for you, such as a Scraper API mentioned earlier.
What are the main sources of data?
More often than not, any type of data on a website can be scraped. As a result, a data source can be text, links, image URLs, inner and outer HTML codes, videos, social media posts, and similar.
Which data is more expensive to collect?
The most expensive data to collect is the one that requires more sophisticated tools. For example, in some cases, you'll need to use a particular type of proxy to overcome common scraping challenges such as CAPTCHAs, geo-restrictions, or IP blocks. Then, you might need to use an IPv4 proxy, a residential proxy, or other. Often, these can be more expensive.
Another example is if you want to scrape data that frequently changes, such as the stock market or travel fare data. You'll need a real-time data scraper for that. While it's more expensive, it will perform better than its slower counterparts.
About the author
Maryia Stsiopkina is a Content Manager at Oxylabs. As her passion for writing was developing, she was writing either creepy detective stories or fairy tales at different points in time. Eventually, she found herself in the tech wonderland with numerous hidden corners to explore. At leisure, she does birdwatching with binoculars (some people mistake it for stalking), makes flower jewelry, and eats pickles.
All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.
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