Build or Buy? Keys to a Successful Web Scraping Project

Introduction

A frequently asked question I get from people starting a new web scraping project is: "Should I build this myself or buy it?" My answer always depends on the situation...

If you have a technical team, it's tempting to dive right into any technical problem and try to build a solution, but we must remember that nowadays there is a SaaS solution for almost everything, so you can always get to market faster without building. Keep reading to learn everything you need to know about building your own web scraping infrastructure and why you might choose a different path.

Web Scraping Basics

Web scraping is a method of extracting datasets and various forms of content from a website. Bots are used to extract data automatically, compile it, and make it usable. Web scraping differs from screen scraping, so it's important not to confuse the two. Web scrapers are often used for large-scale projects that process large amounts of data. But before starting a scraping project, there are several key points to consider:

• - What is the goal of your project?
• - What kind of data do you need?
• - Where will you get the data?
• - How will you store the data?
• - How will you clean and analyze the data?

Answering these questions before starting the project will help set realistic expectations and avoid potential pitfalls.

Did you know that web scraping is not just about collecting data? It's a way to automatically obtain unstructured data and structure it with a lower chance of error. Web scraping, or as many call it, data extraction, can be quite expensive. This is especially true if you don't know what you're doing and lack the necessary infrastructure knowledge.

If you have a project that requires collecting large amounts of data from a website, a cloud-based web scraper can be a useful solution. Many wonder if they can do it themselves, and while it's possible, it's often cheaper and more thorough to use a ready-made web scraper or consult with specialists.

Factors to Consider When Deciding to Build or Buy

Before making a decision, it's important to plan and examine the pros and cons of each approach. The points below can help with an initial assessment. Having answers to these questions will also help you have informed conversations with internal teams or external vendors during the evaluation process.

• Project scale; The scale and complexity of the project drive many decisions. For example, needing millions of records from several complex websites is different from a few hundred rows of data every two months.
• One-time vs. ongoing data: A one-time data collection project does not require ongoing investment and R&D, while a continuous data collection project requires constant investment in improving the solution and its maintenance.
• Do you have the right technical resources to build and maintain it; web scraping is a niche expertise not available to most developers. You need to assess whether your team has the necessary skills and budget.
• Opportunity cost: Undertaking any project in-house means making compromises—something else won't be done, since most companies don't have unlimited resources.

After you have answers to all the above factors, let's look at each option—build and buy—and examine scenarios where one is better than the other.

Building a Web Scraping Solution

First of all, you need a team of data engineers or the skills yourself to support such work. The learning curve for a beginner is so steep that it will be very expensive and slow down execution. What else should you consider before going this route? If you decide to build a scraper yourself, you may need software development help—we are ready to provide it.

Before building a web scraper, there are several key points that may not be immediately obvious:

• - Server costs; Web scrapers are resource-intensive, so you'll need additional budget for this. If you don't want to run scrapers locally on your laptop, you'll have to buy servers and possibly a database.
• - Proxy server costs; It's well known that webmasters become suspicious when the same IP address visits a site frequently. This is usually classified as malicious activity, so webmasters tend to block such IPs. A large pool of IP addresses for rotation is necessary for any successful web scraping project. This costs money, so you need to factor it in.
• - Maintenance costs: As your project scales, you'll need DevOps experience to manage servers and proxies. You'll also need developer time to fix scrapers when they break (yes, they break often).
• - Research and development: Web scraping is one of the most dynamic fields. Website technologies are constantly changing, and sites continuously introduce anti-bot protection. If your scraper works today, there's no guarantee it will work tomorrow. A culture of constantly learning the latest tools, techniques, and methods is an investment you must consider if you build in-house.

What Exactly Does Web Scraping Infrastructure Do and How Can It Help You?

Any infrastructure includes many components; let's look at the most important ones for web scraping. You need to decide where to host your scraper. This can be a dedicated server or a cloud server.

With cloud hosting, you may have to pay a monthly fee for bandwidth consumption. With a dedicated server, you have more control over data, but the bill will be higher.

Next, you need to decide which programming language to use to build your scraper. Common languages for building scrapers include Python, R, and PHP.

Python, however, is known as the preferred programming language for web data collection projects. You also need to decide how data will be structured. Generally, there are two ways to structure data.

You can use a relational database management system to store data in tables. This is often done if you are storing data for reports or visualization. This type is also more like the SQL query language.

With this language, you can also use small code snippets to complete a web scraping project. You can also store data in a non-relational database management system if you need to store large volumes of data or perform computations.

The Big Picture

The big picture is that there are many steps and several ways to do certain things. The whole process includes choosing a coding language, validating data, and then writing code to test. Then you need to implement the code to complete the rest of the project.

If you are not a software engineer, a data analyst with strong coding experience, or have some programming background, you may not know where to start. This is the main reason companies prefer to use ready-made web scrapers as an alternative.

They give the same result, if not better, since web scraping templates are developed by professionals. Plus, it can be significantly cheaper and take less time.

Key Scenarios Where You Should Build?

• You have an in-house data engineering team with experience building and maintaining web scraping projects
• You have sensitive security requirements that a vendor cannot meet
• You have non-standard requirements that require extensive R&D and cannot be handled by existing web scraping tools/services
• If web scraping is a key element of your business strategy and control over intellectual property must be kept in-house

Advantages of Building Your Own Web Scraping Solution

The two main reasons to build your own web scraping application are customization and control. Building your own application allows you to customize everything from the internal logic to sorting and presenting data.

• Customization: Since you build the application to your requirements, you have the ability to create it in a way that best fits your needs. You will know everything about the code and logic. You will know exactly what to fix if something breaks, and you can prioritize features to add as you see fit.
• Control: You have full control over system management and maintenance, as well as data collection and transformation. All servers are under your management, and you don't rely on third parties.

There Are Certain Challenges When Doing It Yourself

Developing a web scraper in-house comes with challenges. Constant issues related to managing proxies and bot access are difficult to overcome without relevant experience. Regular maintenance requires updates and constant monitoring.

This is one of the main problems with building infrastructure in-house. Additionally, there are potential issues with ongoing costs and ensuring the accuracy of the extracted data.

Disadvantages of Building Your Own Web Scraping Solution

The main disadvantages of building your own web scraping solutions are:

• It takes a lot of time to develop, build, and test all necessary features
• Costs are hard to predict and often require more time and money than expected, compared to vendor tools that have a fixed price
• You have to maintain and pay for a large infrastructure, often hiring DevOps specialists to manage servers and proxies
• It can divert resources from core activities and lead to opportunity costs
• You need to invest time and money in learning the latest technologies in the field

Pros and Cons of Building Your Own Web Scraping Infrastructure

Building your own web scraping infrastructure is a good way to get a custom solution. This is one of the main advantages of doing it yourself. You'll have the ability to tailor the design and functionality to your business needs.

However, the disadvantages can outweigh the benefits of doing it yourself. It can be quite time-consuming and costly. Also, if you lack the experience to see the project through from start to finish, you might miss some key points.

Buying a Web Scraping Solution

While there are many web scraping tools available for purchase, it's important to consider a few key points before buying. First, consider the size and scale of the project. If the project is small, a ready-made web scraper might be the best option. However, if the project is large or complex, you may need to build your own web scraper. Second, consider the team's skill level. If the team has no experience with web scrapers, you may need to purchase a more user-friendly web scraper. Finally, consider the budget. There are many web scraping tools available at different price points. It's important to consider the features and functionality of the web scraper in relation to the price.

What About Using Pre-Built Options?

Web scraping has a number of advantages, such as the ability to scale extracted data, automatic data delivery, and a flexible way to extract and organize data. Despite the many benefits of this method, assembling data arrays can become even easier and cheaper if you use a ready-made web scraper. With a pre-built option, all the work is already done.

You'll get additional benefits such as:

• Cost efficiency
• Lower operating costs
• Lower training costs

The main advantages are that it's a finished product. You also spend less time and don't need coding experience.

Pre-built options also provide support for various components throughout the scraping process. For example, a pre-built API reduces the chance of being blocked during scraping. It's also a great way to break down data more quickly.

Key Factors to Consider When Choosing a Web Scraping Tool

Without a lot of experience, web scraping might seem relatively inexpensive and simple. However, it can actually be quite painful if you choose the wrong tools.

Here are some factors to consider before making a choice:

• Setup time
• Do you need developer assistance?
• Price
• Scalability
• Anti-blocking protection
• Customer support
• Maintenance
• On-premises vs. cloud systems
• Features

Advantages of Buying

• Speed to market: Most scraping tools/services let you start getting data almost immediately because they have already built all the necessary infrastructure.
• No additional infrastructure to pay for or maintain. As with any cloud service, you are abstracted from managing servers and proxies.
• Customer support; web scraping is notoriously fragile; things often break. Most web scraping tool vendors provide support to address all the issues you would otherwise have to spend time on.
• Outsourced R&D: Most tools continuously improve their capabilities to ensure their services meet current technological challenges.
• Pay only for what you get; with in-house tools, if something goes wrong, you still have to pay your in-house team to fix it, regardless of how long it takes. With outsourcing, you pay only when the service is successfully used—for example, some tools charge per usage.