What Is Web Scraping?

Web scraping is a method of automatically collecting publicly available information from websites. Instead of manually copying and pasting content, a scraper visits a page, identifies specific pieces of data (like prices, titles, or contact info), and stores it in a file or database.

It’s a common practice used by businesses, researchers, developers, and marketers. For example, an eCommerce company might scrape competitor prices to adjust their own, or a real estate agency might collect property listings from several sources to display them in one place.

Web scraping saves time, improves data accuracy, and helps users make smarter decisions based on real-time insights.

What Is Golang?

Golang (or simply “Go”) is a programming language created by Google that’s known for being fast, reliable, and easy to learn. It’s used by major tech companies for building web servers, APIs, and cloud-based tools—but it’s also gaining popularity in the world of web scraping.

Why? Go was designed to handle tasks that require speed and multitasking. This makes it a great option for scraping large amounts of data from multiple websites at the same time. Unlike heavier languages, Go is lightweight and doesn’t need a lot of system resources to get the job done.

In short, Go is perfect for users who want a scraping tool that’s fast, efficient, and scalable—whether you’re running one scraper or a hundred.

Why Choose Golang for Web Scraping?

When it comes to web scraping, not all programming languages perform the same. Some are easier to start with, while others are better for speed and handling lots of data. Golang checks several key boxes that make it stand out for scraping tasks:

• It’s fast. Go is compiled, which means it runs directly on your system without needing an interpreter. That results in quicker scraping and faster data processing.
• It handles multitasking well. Go was built with concurrency in mind. That means you can scrape multiple pages or websites at the same time—without slowing things down.
• It’s stable. Scraping can often break due to small changes on websites. Go helps build stable, reliable tools that won’t crash or lag under pressure.

In other words, if you’re dealing with large-scale scraping or want something more powerful than simple scripts, Go is worth considering.

Tools That Make Scraping in Go Easier

While Go is powerful on its own, you’ll want to pair it with the right tools to get the most out of it. Luckily, there are a few libraries that make the process smoother—even for those new to the language.

• Multilogin – Helps you avoid blocks by creating unique browser profiles. Works with Selenium for managing multiple scraping sessions and bypassing anti-bot systems.
• Colly – This is one of the most popular web scraping frameworks for Go. It simplifies tasks like visiting pages, following links, and collecting specific data. It’s lightweight, beginner-friendly, and handles the technical stuff in the background.\
• GoQuery – Inspired by jQuery, this tool lets you search and interact with HTML elements using familiar CSS selectors. It’s especially helpful if you’re scraping complex pages with a lot of structure.
• Selenium (for Go) – Best used when you’re dealing with websites that rely heavily on JavaScript. While not the fastest option, it allows full browser control—great for scraping dynamic content.

With the right combo of Go and these tools, scraping becomes faster, more flexible, and a lot less frustrating.

How Scraping With Go Works (In Simple Terms)

Go requests the webpage, reads the HTML (not the visuals), extracts the needed info using set rules, and saves it—usually to CSV, JSON, or a database. Thanks to multithreading, it handles hundreds of pages in seconds.

Planning Your Scraping Project

Before diving into any scraping task, it’s smart to plan a few key things—especially if you want smooth results.

• Pick the right website. Start with a site that’s public and doesn’t require a login. Static pages (ones that don’t load content dynamically) are easier to scrape.
• Know what you want to collect. Whether it’s product prices, article titles, or contact info, be specific about your goal.
• Check the page’s structure. You don’t need to be a coder to do this. Right-click on a web page and select “Inspect” to see what elements the data is inside (like headlines, images, or links).
  Avoid overloading a website. Scraping too fast or too often can trigger blocks. It’s good practice to go slowly and respect the website’s rules.

A bit of prep work goes a long way. The more you know about your target site, the easier it is to collect the right data without running into errors.

Avoiding Blocks and Bans

Most websites are okay with occasional scraping—but if you overdo it or look suspicious, they can block your scraper. That’s why it’s important to know how to stay under the radar.

Here’s how you can avoid getting banned:

• Use a real User-Agent. This is the little string that tells websites what browser you’re using. Go’s default one can look fake, so it’s smart to switch it to something more natural (like Chrome or Firefox).
• Don’t scrape too fast. Rapid-fire requests can make you look like a bot. Add small delays between page visits to mimic real browsing behavior.
• Rotate IP addresses with proxies. This spreads your requests across different IPs, which helps you avoid triggering rate limits or blocks. Residential or mobile proxies work best for this.
• Avoid scraping private or login-required pages. These are often protected by stronger anti-bot systems, so unless you’re experienced, stick to public content.

With Go and the right precautions, you can scrape efficiently without annoying the websites you visit.

Saving and Using the Data

Once your scraper collects the data, you’ll want to save it in a way that’s easy to understand and reuse later. Go makes this part just as smooth.

The two most common formats are:

• CSV files – These are like spreadsheets. Perfect for viewing in Excel or Google Sheets. Great for lists like prices, product names, and URLs.
• JSON files – These are better if your data is more complex or you plan to use it in other apps or systems. JSON keeps things structured and is widely used in web development.

Once you’ve saved the data, you can use it to:

• Monitor competitor pricing
• Track content changes over time
• Build databases or dashboards
• Feed it into your own website or app

In short, collecting the data is just the first step. How you use it is where the real value comes in.

Automating Your Scraping Tasks

Manually running a scraper every time you need fresh data can get tiring—especially if you want updates daily or even hourly. That’s where automation comes in.

With Go, it’s easy to set up your scraper to run on a schedule. You can:

• Schedule tasks every few minutes, hours, or days
• Automatically save the results in your chosen format
• Trigger updates to your database, dashboards, or tools

This is useful for things like:

• Price tracking — Automatically collect pricing data from competitors
• Content monitoring — Get notified when new blog posts or job listings are published
• Social media analysis — Collect engagement stats or new posts regularly

Common Challenges and How to Handle Them

Even with a great setup, web scraping isn’t always smooth sailing. Websites change, errors pop up, and sometimes your scraper just stops working.

Here are a few common challenges—and how to deal with them:

• Website layout changes – If the structure of a page changes (like a class name or tag), your scraper might miss the data. The fix? Regularly review your target sites and keep your selectors up to date.
• CAPTCHAs and anti-bot protection – Some sites try to detect and block automated tools. Using proxies, rotating user agents, and adding small delays can help bypass these defenses.
• Rate limits – Websites may block you if you send too many requests too quickly. Slow down your scraper and avoid scraping entire sites in one go.
• Data inconsistency – Not all pages follow the same structure. You’ll need to add checks so your scraper doesn’t break when something’s missing.

The key is to treat scraping as an ongoing process, not a one-time task. 

When to Consider a Scraping API or Ready Dataset

Sometimes building your own scraper isn’t the best use of time—especially if you need large volumes of data quickly, or you’re not comfortable handling scraping challenges yourself.

That’s where scraping APIs and ready-to-use datasets come in.

• Scraping APIs let you request the data you need without building or maintaining a scraper. You just send a query and get clean, structured results in return.
• Datasets are pre-collected collections of data that you can download instantly. They’re ideal if you’re looking for things like product listings, social media data, or public reviews.

These options are especially helpful if:

• You’re short on time or resources
• You want guaranteed data quality
• You’re dealing with websites that have strong anti-bot protection

Instead of reinventing the wheel, APIs and datasets offer a plug-and-play solution to get exactly what you need.

How Multilogin Helps With Scraping

Multilogin is an antidetect browser designed to run multiple browser profiles, each with a unique fingerprint, cookies, and IP. It’s a powerful tool for users who scrape data from websites that block bots.

Here’s why it works well for scraping:

• Each profile looks like a real user, helping you avoid bans and detection.
• Built-in residential proxies lets you assign different IPs to each session.
• Compatible with automation tools like Puppeteer and Selenium, so you can launch and control scraping tasks with ease.

If you’re collecting data from strict sites, Multilogin makes it easier to stay undetected and scrape at scale.

Final Thoughts

Web scraping has become an essential tool for businesses, analysts, and developers who rely on fresh, accurate data. Whether you’re collecting product prices, tracking content, or building your own dashboard, having the right tools and strategy makes all the difference.

While building your own scraper gives you full control, it’s not always necessary. Depending on your goals, using a prebuilt API or buying a dataset might be the better route.

The key is knowing your needs—and choosing the most efficient path to get the data that drives results.