Our Web Scraping Pipeline: From Collection to Clean Database

Introduction

Data has to be one of the most important aspects and probably game changers for a business. good data means (mostly) good and justifiable decisions, which makes a business progress and grow based on real insight, instead of just assuming or hoping for their decisions to work.

And what's the most painful thing about data? collecting and analyzing it the right way. The internet has to be the biggest mine of data out there, since we have access of zettabytes upon zettabytes of data, and the numbers keep increasing everyday.

Now, of course you don't need this data, especially when most of it is brain rot memes or AI generated videos of Will Smith eating spaghetti, when you're trying to build a restaurant business. So how do we find the data we need? How do we handle extracting, cleaning, storing, and analyzing huge amounts of data?

In the blog post, i will share with you the process we adopt at Mijane Technologies to extract data for clients and turning it into valuable insight that gives you the bigger picture of where you are, and to make better data-driven decisions.

Web scraping and Mijane Technologies

One of our main services at Mijane Technologies is Web Scraping. we've worked on this field for a little over 2 years now, and clients are satisfied with our work! We've managed to deliver various scraping solutions for platforms such as Instagram, Tiktok, Target, Walmart, Bonfire.. and the list goes on.

Why do our clients need web scraping? The use cases vary. Some need competitor pricing data to stay competitive in their market. Others want to build lead lists by scraping business directories or public social media profiles. We've helped e-commerce clients track product availability and pricing across multiple platforms, and we've built databases for market research by collecting publicly available business information.

The common thread is always the same: the data exists publicly on the internet, but there's no easy way to export it or access it through an API. You either scrape it or spend weeks doing it manually. For a client who needs thousands of data points, scraping isn't just convenient, it's the only realistic option.

What makes our approach different is that we don't just deliver raw scraped data and disappear. We build complete pipelines that include data validation, enrichment, and proper database storage. Our clients get clean, structured data they can actually use, not spreadsheets full of broken links and duplicate entries.

Our Scraping Strategies

The biggest challenge in web scraping isn't writing the code to extract data. It's doing it without getting blocked. Websites don't like bots hitting their servers repeatedly, so they have rate limits, CAPTCHAs, and IP blocks to stop automated scraping.

Our core stack is built around Puppeteer, a headless browser automation library that lets us interact with websites exactly like a real user would. We pair it with specialized plugins that help us bypass common anti-bot measures. For proxy rotation, we use a variety of third party services (e.g scrape.do), which handles rotating residential IPs automatically so our requests appear to come from different locations and don't trigger rate limits.

The key to not getting blocked? Slow down and act human. We implement random delays between requests, randomize mouse movements and scroll patterns, and vary our browsing behavior. It's counterintuitive when you want to scrape thousands of records quickly, but patience prevents your IP from getting blacklisted halfway through.

Once we have the raw data, we run it through an enrichment pipeline. For lead generation projects, we use Tomba to find missing email addresses and Reoon to verify them. On one recent project, we started with scraped business data and ended up with 696 verified, deliverable email addresses ready for outreach. Without validation, half of those would have bounced.

How do we manage large amounts of data?

Scraping is just the first step. The data you pull from websites is messy: missing fields, inconsistent formats, duplicates, and typos everywhere. You can't just dump it directly into a production database and call it done.

We use Supabase and Postgres to handle storage and organization. Our approach is simple: keep raw data separate from clean data. When we scrape, everything goes into a raw staging table first, exactly as we found it. Then we run cleaning scripts that deduplicate records, normalize formats, fill in missing fields with enrichment data, and validate everything before it moves to production tables.

This two-table system means we never lose the original data if something goes wrong during cleaning. It also makes it easy to re-run processing logic when we find edge cases or bugs in our cleaning scripts.

For large datasets, we batch the processing instead of trying to clean everything at once. It's slower, but it prevents memory issues and makes debugging much easier when something breaks.

Wrap up

Web scraping isn't just about extracting data from websites. It's about building a reliable pipeline that can collect, validate, and structure information so it's actually usable for making business decisions. The scraping itself is maybe 30% of the work. The other 70% is handling the mess that comes with real-world data.

If you need help collecting data for market research, lead generation, or competitive analysis, Mijane Technologies can build a custom scraping solution for your business. Reach out at info@mijane.tech and let's talk about what data you need.