When starting a new web scraping project, many developers jump straight into coding without first understanding how the website works. An experienced developer usually does the opposite. Before they begin writing a single line of code, they study the target website, understand how its data is delivered, identify possible restrictions, and decide whether scraping is even the right approach. This initial investigation can save hours of rewriting code later.
To get started..
Here is how I would approach a website I have never scraped before
1. Start by Understanding the Website
First, browse the website like a normal user.
Look at the pages that contain the data you need. Check whether the information appears directly on the page, whether you need to click buttons to reveal it, and whether the content changes when you interact with the page.
At this point, the goal is simple: understand the website's structure from a user's perspective.
For example, if I need product information, I would identify:
- Product name
- Price
- Availability
- Product URL
- Reviews
- Pagination or infinite scrolling
I would also determine whether the same data appears consistently across different pages. This gives me a rough map of what I need to extract before thinking about implementation.
2. Inspect the HTML with Chrome DevTools
Next, I open Chrome DevTools and inspect the page's HTML.
I want to understand how the information is represented in the Document Object Model (DOM).
For example, a product name might appear inside an element such as:
<h2 class="product-title">Example Product</h2>
The important question is not simply, "Where is the data?"
It is:
Can I identify a reliable selector that will continue working when the website changes slightly?
An experienced scraper avoids depending unnecessarily on fragile selectors, automatically generated class names, or specific positions in the DOM.
This is where tools such as CSS selectors and XPath become useful.
3. Check the Network Tab
This is one of the most important parts of the investigation.
I open the Network tab in DevTools and reload the page. Then I interact with the website and watch the requests being made by the browser. Learn more
In particular, I look for Fetch/XHR requests.
Why?
Because the HTML page may not contain the actual data I need.
A website might load an empty page first and then request product information from a backend endpoint. The browser receives the response and uses JavaScript to display it.
If that response already contains structured JSON data, requesting the underlying endpoint may be much simpler than scraping the rendered page. Inspect Network Activity with Chrome DevTools
For example:
Browser -> Website -> API endpoint -> JSON response -> Page
Instead of building:
Scraper -> Browser -> Render JavaScript -> Find elements -> Extract data
I may be able to build:
Scraper -> API endpoint -> JSON -> Extract data
That can make the system faster, simpler, and easier to maintain.
However, I would first confirm that the endpoint is legitimately accessible for the intended use and complies with the site's terms and applicable rules.
4. Determine Whether JavaScript Rendering Is Required
Not every website can be scraped effectively with a simple HTTP request.
Some websites return most of their content directly in the HTML. Others rely heavily on JavaScript.
I test this early.
One useful check is to request the page without executing JavaScript and compare the response with what appears in the browser.
If the data exists in the initial HTML, tools such as requests and an HTML parser may be enough.
If the page depends heavily on JavaScript, browser automation tools such as Playwright or Selenium may be necessary.
The goal is not to use the most powerful tool available.
It's to use the simplest tool that reliably solves the problem.
5. Look for APIs Before Building a Browser Scraper
If the website communicates with an API, I investigate how that API works.
I look at:
- Request URLs
- HTTP methods
- Query parameters
- Request headers
- Response formats
- Pagination
- Authentication requirements
- Error responses
This helps determine whether an API-based approach is possible.
However, discovering an endpoint does not automatically mean it should be used. I would check the site's documentation, terms, access requirements, and intended usage before building around it.
The engineering principle is simple:
Prefer a stable and permitted data interface over scraping rendered HTML when one is available.
6. Check robots.txt and site policies
Before collecting data at scale, I check the website's robots.txt file.
It can provide useful information about which crawlers are allowed or disallowed from accessing particular paths.
However, robots.txt is not a complete legal or technical permission system. It should be treated as one signal among several.
I will also look for the site's terms of service, API documentation, data use policies, and any published crawling guidelines.
This is part of responsible scraping, not an optional step.
7. Understand Rate Limits and Server Behavior
Finally, I’ll think about how frequently the scraper should make requests.
A scraper that sends hundreds or thousands of requests as quickly as possible can create unnecessary load and may trigger defensive systems.
I will also look for documented API limits first. If limits aren’t documented, I start conservatively and monitor the server responses.
Important signals include:
- HTTP 429 responses
- HTTP 403 responses
- Connection failures
- Increasing response times
- Temporary blocks
- CAPTCHA challenges
A production scraper should also implement sensible delays, retries with backoff, timeouts, logging, and error handling. Learn more
The Real Work Happens Before the Scraper
A reliable scraping project is rarely about writing a clever extraction script.
The difficult part is understanding the system you are interacting with.
Before writing code, I want to answer five questions:
- Where is the data?
- How is the data delivered?
- Does the page require JavaScript?
- What restrictions or policies apply?
- How can I collect the data without creating an unreliable or unnecessarily aggressive system?
Once those questions have clear answers, the implementation becomes much easier. That is the difference between simply writing a scraper and engineering a scraping system.
Final Thoughts
The best scraper isn't necessarily the one with the most sophisticated code. It's the one built on a clear understanding of the website, how its data works, its limitations, and what you want to achieve.
P.S. I help developer tools and SaaS companies reduce onboarding friction by creating clear API documentation, tutorials, and technical content. You can reach out to me via LinkedIn, also if you're interested in learning digital skills check out Wismartech
Top comments (0)