How to Efficiently Scrape Food Delivery Website Data

I’ll never forget the first time I tried to compare restaurant prices across three different food delivery apps for a side project. I had a spreadsheet, a cup of coffee, and a wildly optimistic sense of how long it would take. Fast-forward four hours, and I’d barely scratched the surface—copying and pasting menus, prices, and reviews by hand. My wrist was sore, my coffee was cold, and my “quick research” had turned into a marathon of tedium.

Sound familiar? You’re not alone. As the food delivery industry has exploded—reaching and projected to hit —the hunger for data has only grown. Restaurants, market analysts, and sales teams all want a bite of the action, but manually gathering this information is about as fun as peeling a hundred onions. That’s where website scraping tools come in, and why I’m excited to show you, step-by-step, how to scrape food delivery data (using Uber Eats as our example) with —the AI-powered web scraper my team and I built to make this process as painless as possible.

Let’s dig in—no cold coffee required.

What is Food Delivery Data and Why Scrape It?

When we talk about “food delivery data,” we’re referring to the rich, structured (and sometimes not-so-structured) information listed on platforms like Uber Eats, DoorDash, and Grubhub. This includes:

• Restaurant details: Name, address, phone number, cuisine type, ratings, review counts, price level, and hours of operation.
• Menu information: Dish names, descriptions, prices, photos, and sometimes nutritional info or tags (like “vegan” or “spicy”).
• Delivery logistics: Estimated delivery times, delivery fees, and distance.
• Promotions: Special deals, coupons, or discounts.
• Customer feedback: Ratings and written reviews for both restaurants and individual menu items.

Why bother scraping all this data? Because it’s a goldmine for anyone looking to make data-driven decisions in a fiercely competitive market. Scraping food delivery websites can unlock insights like:

• Which cuisines and dishes are trending in a given city
• How competitors are pricing their menus and structuring promotions
• What customers are saying in reviews (and what they’re complaining about)
• How delivery fees and times vary by area

Manually collecting this information is not just tedious—it’s nearly impossible to do at scale. Modern tools automate the process, turning unstructured web pages into structured datasets (think: a spreadsheet with every restaurant, menu item, and price in your city). That’s the kind of data that can drive real business results.

Scraped food delivery data is actionable data. It helps you move faster, make smarter decisions, and stay ahead of the competition.

Key Use Cases: How Scraping Food Delivery Data Drives Business Results

So, what do you actually do with all this data? Here’s a quick look at how different teams use scraped food delivery data to drive ROI:

The bottom line? Scraped food delivery data is actionable data. It helps you move faster, make smarter decisions, and stay ahead of the competition.

Comparing Website Scraping Tools for Food Delivery Data

Let’s be honest: not all are created equal—especially when it comes to scraping dynamic sites like Uber Eats. Here’s how Thunderbit stacks up against a couple of traditional options:

Traditional tools like Octoparse and ParseHub have their place, but they often require a lot of manual setup and ongoing maintenance. Thunderbit, on the other hand, was built to make scraping as easy as ordering takeout.

Why Thunderbit Stands Out for Food Delivery Website Scraping

I’m biased, but here’s why I think Thunderbit is the best for food delivery data:

• AI Suggest Fields: Thunderbit’s AI reads the page and suggests exactly which fields to extract—no more clicking every menu item by hand.
• Subpage Scraping: Need menu details or reviews from each restaurant’s page? Thunderbit can automatically visit and extract data from subpages with a single toggle.
• Instant Export: Export your data to Excel, Google Sheets, Airtable, or Notion for free—no hoops to jump through.
• Low Setup Cost: There’s a free tier, and the pay-as-you-go model means you only pay for what you scrape. No need to shell out for a pricey monthly subscription if you just need a quick dataset.
• Adaptability: Thunderbit’s AI adapts to site changes. If Uber Eats updates its layout, just click “AI Suggest Fields” again and you’re back in business.

For me, the biggest win is how little time it takes to go from “I need this data” to “I have this data.” That’s what Thunderbit is all about.

Step-by-Step Guide: Scrape Food Delivery Data from Uber Eats with Thunderbit

Let’s get practical. Here’s how I scrape Uber Eats data (restaurant listings, menus, prices, reviews, and more) using Thunderbit. You don’t need to know how to code, and you don’t need to spend hours wrestling with complicated settings.

Step 1: Set Up Thunderbit and Access Uber Eats

First, . It’s lightweight, free to start, and you’ll see a Thunderbit icon in your browser toolbar once it’s ready.

Next, navigate to in your browser. Enter your location (e.g., “Los Angeles, CA”) or log in if you have an account. Make sure you’re on the page that lists the restaurants you want to scrape. If Uber Eats uses infinite scroll, scroll down so all the restaurants you want are loaded.

Thunderbit works on the currently open page—what you see is what you’ll scrape.

Step 2: Use AI Suggest Fields to Identify Data Points

Click the Thunderbit icon to open the interface. Choose “Current Page” as your data source, then hit the AI Suggest Fields button.

Thunderbit’s AI will scan the Uber Eats page and automatically suggest a table of fields—think “Restaurant Name,” “Category,” “Rating,” “Number of Reviews,” “Delivery Time,” “Delivery Fee,” and more. You’ll see a preview of the data for a few restaurants so you can check that it’s capturing what you want.

If you want to tweak the fields (rename columns, delete ones you don’t need, or add custom ones), you can do that here. Thunderbit even lets you set the data type for each field—text, number, URL, etc.—which is handy for later analysis.

Step 3: Customize Fields and Set Up Subpage Scraping

Want more details, like menu items or reviews from each restaurant’s page? Enable Subpage Scraping in Thunderbit. The tool will automatically detect which field contains the restaurant links and ask you to confirm.

You can then tell Thunderbit which fields to extract from the subpages—like the first three menu items and their prices, or the restaurant’s exact address. Thunderbit’s AI can suggest these fields, or you can select them manually.

If you’re dealing with a long list of restaurants and Uber Eats uses infinite scroll, make sure all the restaurants you want are loaded before you start scraping. Thunderbit’s cloud mode can handle auto-scrolling and pagination for you.

Step 4: Start Scraping and Export Data

Now for the fun part: click Scrape. Thunderbit will extract the data from the page (and subpages, if enabled), populating a table in real time.

Once the scrape is done, review the data in Thunderbit to make sure everything looks good. Then, export it in your preferred format:

• Excel/CSV: Download a file for use in Excel or Google Sheets.
• Google Sheets: Send the data directly to a new Google Sheet (great for sharing or live analysis).
• Airtable/Notion: Export to your database of choice—Thunderbit even uploads images if you scraped menu photos.
• JSON/Clipboard: For developers or custom workflows.

Exporting is always free and unlimited with Thunderbit.

Tips for Efficient and Accurate Food Delivery Data Scraping

Scraping food delivery sites can be tricky, but a few best practices go a long way:

• Plan your scope: Decide what data you need and how much. Scraping every menu item from every restaurant in New York City? That’s a lot of data. Focus on what’s actionable.
• Leverage scheduling: Need regular updates? Use Thunderbit’s to automate weekly or daily runs.
• Use cloud scraping for scale: For large datasets, Thunderbit’s cloud mode is faster and won’t tie up your computer.
• Avoid duplicates: Use unique keys (like restaurant name + address) to de-duplicate entries, especially if scraping overlapping areas.
• Monitor for missing data: Spot-check your results for gaps or errors. If a field is missing, try re-running AI Suggest Fields.
• Respect rate limits: Don’t scrape too aggressively—Thunderbit mimics human browsing speed, but if you’re scraping thousands of restaurants, consider breaking it into batches.
• Utilize AI Prompts: Thunderbit lets you add AI prompts to transform or clean data as it’s scraped (e.g., extract just the number from “30–40 min”).
• Keep an eye on site changes: If Uber Eats updates its layout, just re-run AI Suggest Fields.
• Combine data sources: Scrape multiple platforms (Uber Eats, DoorDash, etc.) for richer analysis.

For more on best practices, check out .

Legal and Ethical Considerations When Scraping Food Delivery Websites

Before you start scraping, it’s important to stay on the right side of the law (and good internet citizenship):

• Check the Terms of Service: Uber Eats’ typically prohibits scraping without permission. For internal analysis, you’re usually fine, but don’t redistribute or resell the data.
• Respect robots.txt: This file tells bots what’s allowed. Thunderbit, as a browser extension, acts like a regular user, but it’s good to check.
• Don’t overload servers: Scrape at a reasonable pace and avoid hammering the site.
• Avoid private data: Only scrape information that’s publicly visible—never attempt to access user accounts or personal info.
• Use data responsibly: Internal analytics are fair game, but public-facing uses (like publishing a dataset) could draw attention.
• Stay within legal frameworks: In the U.S., scraping public data is generally legal, but don’t circumvent security or scrape personal data.
• Data privacy: Treat any data you collect with care, even if it’s mostly business info.

The golden rule: scrape responsibly and ethically. If you get blocked or see a CAPTCHA, that’s a sign to slow down or stop.

Troubleshooting: Common Issues in Food Delivery Data Scraping

Even with Thunderbit, you might hit a few snags. Here’s how I handle the most common issues:

• Site layout changes: If Uber Eats updates its design, just re-run AI Suggest Fields. Thunderbit’s AI adapts quickly.
• Login/location requirements: Use Browser Scraping mode, log in, and set your address manually before scraping.
• Pagination/infinite scroll: Make sure all restaurants are loaded before scraping, or use cloud mode for auto-scrolling.
• Anti-bot measures: If you hit a CAPTCHA, solve it manually. If you’re blocked, slow down the scrape or switch IPs.
• Partial scrapes/errors: Break large jobs into smaller chunks, and always check you’re on the latest version of Thunderbit.
• Data formatting issues: Use Thunderbit’s AI prompts to clean data as you scrape, or tidy up in Excel after export.
• Keeping data fresh: Use scheduling for regular updates, or re-run scrapes as needed.

If you ever get stuck, try the process manually in your browser to see what’s happening, and don’t hesitate to reach out to .

Conclusion & Key Takeaways: Unlocking the Power of Food Delivery Data

The food delivery industry is booming, and the competition is only getting hotter. Scraping food delivery data—menus, prices, reviews, and more—has become a must-have for anyone who wants to make smarter, faster business decisions.

Manual data collection is a slog. But with , you can turn a tedious chore into a quick, repeatable workflow. Thunderbit’s AI-driven approach means you don’t have to be a developer (or a glutton for punishment) to get structured, actionable data from Uber Eats and beyond.

If you haven’t tried Thunderbit yet, and give it a spin. There’s a free tier, so you can see for yourself how easy it is to go from “I need this data” to “I have this data.” Whether you’re a restaurant owner, analyst, or just a curious foodie, the insights you’ll uncover can give you a real edge.

So, here’s to data-driven decisions, fewer cold coffees, and more time spent actually enjoying your next meal. Happy scraping—and bon appétit for data.

FAQs

1. Why would someone want to scrape food delivery data?

Scraping food delivery data provides valuable insights into restaurant offerings, menu prices, customer reviews, delivery logistics, and promotions. It enables businesses to perform competitor analysis, optimize their own menus, generate leads, analyze local markets, and improve customer experiences—all based on real-time data.

2. What are the main use cases for food delivery data scraping?

Key use cases include competitor pricing analysis, identifying trending dishes, aggregating customer reviews for sentiment analysis, building B2B lead lists, analyzing market gaps for expansion, and feeding real-time data into pricing and demand forecasting models.

3. How does Thunderbit simplify the scraping process compared to other tools?

Thunderbit uses AI to detect data fields automatically, adapt to website layout changes, and scrape subpages like individual restaurant menus. It also offers easy export options to Excel, Google Sheets, Notion, and more—all with minimal setup and a low-cost pay-per-use model.

4. What should users consider when scraping ethically and legally?

Before scraping, users should review the website's terms of service, avoid scraping private or personal data, respect rate limits, and ensure they're using the data responsibly. Scraping publicly available information for internal analysis is typically acceptable, but redistribution or resale may violate policies.

5. What are best practices for successful food delivery data scraping?

Plan your data scope carefully, use subpage scraping for deeper insights, avoid duplicate entries, spot-check for missing data, respect site rate limits, and schedule regular updates. Leveraging Thunderbit’s AI prompts and cloud scraping mode also improves efficiency and accuracy.

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