What is Oddle Eats Data Scraper, and how does it work?
An Oddle Eats scraper is a specialized tool that automates the process of collecting restaurant and menu details from the Oddle Eats platform. Businesses use our Oddle Eats data scraping service to gather structured datasets containing menu items, prices, ratings, and operating hours. The scraper works by connecting to the website, extracting HTML elements, and transforming them into clean, usable formats like CSV or JSON. With customizable filters, you can target specific restaurant categories or cuisines. Whether you need to scrape Oddle Eats restaurant data for competitive analysis or menu tracking, our service ensures high-speed, accurate, and real-time extraction—saving you hours of manual effort while providing valuable insights for smarter decision-making.
Why extract data from Oddle Eats?
Using an Oddle Eats menu scraper allows restaurants, delivery services, and analysts to gain a competitive edge. By leveraging an Oddle Eats restaurant scraper, businesses can monitor competitor pricing, track menu changes, and analyze dish popularity trends. Access to updated Oddle Eats data enables better demand forecasting, targeted promotions, and improved customer satisfaction. Restaurants can also discover gaps in competitor offerings to introduce new, high-demand dishes. Market researchers can use extracted data for regional food trend analysis, while delivery platforms can optimize menus for higher conversions. In short, scraping Oddle Eats provides actionable intelligence that fuels pricing strategy, product development, and operational efficiency in the fast-changing online food delivery industry.
Is it legal to extract Oddle Eats data?
When done responsibly, using an Oddle Eats scraper Singapore can be compliant with data usage policies. The key is to follow ethical scraping practices and respect platform terms. Our Oddle Eats API integration approach ensures that the process is smooth, secure, and targeted toward public information only. We focus on extracting open, non-sensitive data for competitive intelligence, menu tracking, and restaurant research. Businesses should avoid scraping private or copyrighted content without permission. By combining legal compliance with high-quality data, you can still extract real-time Oddle Eats data effectively while minimizing risks—ensuring that your market insights are both reliable and within ethical boundaries.
How can I extract data from Oddle Eats?
To scrape Oddle Eats restaurant data, you can use either automated scraping tools or API-based solutions. Our Oddle Eats data extraction service supports real-time monitoring of menus, prices, ratings, and promotions. We offer custom filters for location, cuisine type, and restaurant category, ensuring you get only the most relevant data. The process involves setting up your extraction parameters, running the scraper, and exporting data into formats like CSV, JSON, or Excel. For large-scale needs, API-based integration ensures continuous data flow directly into your analytics dashboard. This method is ideal for businesses seeking updated and structured datasets without manual effort—helping you make informed, data-driven decisions.
Do You Want More Oddle Eats Scraping Alternatives?
If your needs go beyond the Oddle Food Delivery Dataset, our platform also supports a wide range of sources via Food Data Scraping API. We can help you extract menus, pricing, and reviews from multiple food delivery and restaurant aggregator platforms worldwide. This multi-source approach allows you to compare Oddle Eats data with competitors, analyze cross-platform pricing, and discover emerging culinary trends. By integrating various datasets, businesses can build a more holistic view of the market, identify opportunities, and optimize strategies for maximum profitability. Whether it’s Oddle Eats or other delivery platforms, we ensure fast, reliable, and scalable data extraction for any project size.
Input options
Our Oddle Eats scraper offers multiple input options to ensure precise data targeting. You can specify restaurant URLs, city names, cuisine types, or categories to customize your extraction process. With our Oddle Eats data scraping service, you can also upload bulk restaurant lists for automated processing, saving time on manual setup. For advanced users, filters like price range, rating, and delivery time can refine the dataset further. Whether you need to scrape Oddle Eats restaurant data for a single outlet or hundreds at once, our system supports flexible configurations. Combined with our Oddle Eats menu scraper, these input options ensure you capture only the most relevant and actionable data for your market research or business strategy.
Sample Result of Oddle Eats Data Scraper
import requests
import json
import pandas as pd
from bs4 import BeautifulSoup
BASE_URL = "https://eats.oddle.me"
OUTPUT_FILE = "oddle_restaurants.csv"
def extract_restaurant_data(restaurant):
return {
"Name": restaurant.get("name"),
"Brand": restaurant.get("brand"),
"Outlet": restaurant.get("outlet_name"),
"Address": restaurant.get("address", {}).get("street"),
"City": restaurant.get("address", {}).get("city"),
"PostalCode": restaurant.get("address", {}).get("postal_code"),
"Country": restaurant.get("address", {}).get("country"),
"Rating": restaurant.get("rating"),
"ReviewCount": restaurant.get("review_count"),
"Cuisines": ", ".join(restaurant.get("cuisines", [])),
"MinOrder": restaurant.get("min_order"),
"DeliveryFee": restaurant.get("fees", {}).get("delivery_fee"),
"IsOpen": restaurant.get("is_open_now"),
"LeadTime": restaurant.get("lead_time_min"),
"MenuURL": restaurant.get("source_page")
}
if __name__ == "__main__":
data = {
"scrape_metadata": {
"platform": "Oddle Eats",
"country": "SG",
"currency": "SGD",
"query": {
"search_term": "ramen",
"delivery_postal_code": "048616",
"date": "2025-08-14",
"time": "19:00"
},
"executed_at": "2025-08-14T13:15:27Z",
"source_url": "https://eats.oddle.me",
"pagination": { "page": 1, "per_page": 20, "total_results": 2 }
},
"records": [
{
"restaurant_id": "oddle_628371",
"name": "Tonkotsu House",
"brand": "Tonkotsu House",
"outlet_name": "Raffles Place",
"slug": "tonkotsu-house-raffles-place",
"cuisines": ["Japanese", "Ramen"],
"rating": 4.6,
"review_count": 862,
"is_open_now": True,
"lead_time_min": 35,
"min_order": 20.0,
"fees": { "delivery_fee": 3.9, "platform_fee": 0.5, "service_fee": 0.0 },
"fulfillment": ["delivery", "pickup"],
"delivery_zones": [
{ "postal_prefix": "01-08", "min_order": 25.0, "fee": 4.5 }
],
"address": {
"street": "16 Collyer Quay, #01-05",
"postal_code": "049318",
"city": "Singapore",
"country": "SG"
},
"coordinates": { "lat": 1.28384, "lng": 103.8521 },
"opening_hours": {
"mon_fri": "11:00-21:30",
"sat": "11:30-21:00",
"sun": "11:30-20:30"
},
"badges": ["Popular", "Halal-friendly"],
"promotions": [
{
"type": "percentage",
"title": "10% OFF self-pickup",
"code": "PICK10",
"valid_until": "2025-09-30"
}
],
"menu": [
{
"category_id": "cat_ramen",
"category_name": "Ramen",
"items": [
{
"item_id": "itm_1001",
"name": "Tonkotsu Classic",
"description": "12hr pork bone broth, chashu, ajitama, menma, negi.",
"image_url": "https://cdn.example.com/i/tonkotsu-classic.jpg",
"price": 14.9,
"discount_price": 13.4,
"in_stock": True,
"spicy_level": 0,
"allergens": ["Egg", "Gluten"],
"nutrition": { "kcal": 620, "protein_g": 32, "carb_g": 58, "fat_g": 28 },
"options": [
{
"group_name": "Noodle Texture",
"required": True,
"max_select": 1,
"choices": [
{ "name": "Soft", "price_delta": 0.0 },
{ "name": "Normal", "price_delta": 0.0 },
{ "name": "Hard", "price_delta": 0.0 }
]
},
{
"group_name": "Add-ons",
"required": False,
"max_select": 3,
"choices": [
{ "name": "Extra Chashu", "price_delta": 3.0 },
{ "name": "Extra Egg", "price_delta": 1.8 },
{ "name": "Kikurage", "price_delta": 1.2 }
]
}
],
"popularity_rank": 1,
"last_seen_price": { "value": 14.9, "seen_at": "2025-07-30T09:12:00Z" }
},
{
"item_id": "itm_1002",
"name": "Spicy Miso Ramen",
"description": "Miso broth, minced pork, chili oil, corn, scallion.",
"image_url": "https://cdn.example.com/i/spicy-miso.jpg",
"price": 15.9,
"in_stock": True,
"spicy_level": 3,
"allergens": ["Soy", "Gluten", "Sesame"],
"options": [],
"popularity_rank": 3
}
]
},
{
"category_id": "cat_sides",
"category_name": "Sides",
"items": [
{
"item_id": "itm_2001",
"name": "Gyoza (6pc)",
"description": "Pan-fried pork dumplings with house sauce.",
"price": 6.9,
"in_stock": False,
"allergens": ["Gluten", "Soy"]
}
]
}
],
"updated_at": "2025-08-14T12:58:10Z",
"source_page": "https://eats.oddle.me/tonkotsu-house-raffles-place"
},
{
"restaurant_id": "oddle_774205",
"name": "Green Bowl",
"brand": "Green Bowl",
"outlet_name": "Tanjong Pagar",
"slug": "green-bowl-tanjong-pagar",
"cuisines": ["Healthy", "Salads"],
"rating": 4.3,
"review_count": 241,
"is_open_now": False,
"lead_time_min": 25,
"min_order": 18.0,
"fees": { "delivery_fee": 2.9, "platform_fee": 0.5, "service_fee": 0.0 },
"fulfillment": ["pickup"],
"address": {
"street": "5 Wallich St, #B2-12",
"postal_code": "078883",
"city": "Singapore",
"country": "SG"
},
"coordinates": { "lat": 1.2765, "lng": 103.8457 },
"opening_hours": { "mon_sat": "10:00-20:00", "sun": "Closed" },
"badges": ["Eco-packaging"],
"promotions": [],
"menu": [
{
"category_id": "cat_bowls",
"category_name": "Signature Bowls",
"items": [
{
"item_id": "itm_3001",
"name": "Avocado Crunch",
"description": "Mixed greens, avocado, quinoa, cherry tomato, almonds, lemon vinaigrette.",
"price": 12.5,
"in_stock": True,
"allergens": ["Tree Nuts"],
"nutrition": { "kcal": 410, "protein_g": 12, "carb_g": 38, "fat_g": 22 }
},
{
"item_id": "itm_3002",
"name": "Protein Power",
"description": "Brown rice, grilled chicken, edamame, egg, tahini sauce.",
"price": 13.8,
"in_stock": True,
"allergens": ["Egg", "Sesame"]
}
]
}
],
"updated_at": "2025-08-13T16:41:02Z",
"source_page": "https://eats.oddle.me/green-bowl-tanjong-pagar"
}
]
}
print(f"Processing {len(data['records'])} restaurants...")
results = []
for idx, restaurant in enumerate(data['records'], 1):
print(f"[{idx}/{len(data['records'])}] Extracting data from: {restaurant['name']}")
restaurant_data = extract_restaurant_data(restaurant)
if restaurant_data:
results.append(restaurant_data)
if results:
df = pd.DataFrame(results)
df.to_csv(OUTPUT_FILE, index=False)
print(f"Data saved to {OUTPUT_FILE}")
else:
print("No data extracted.")
Integrations with Oddle Eats Data Scraper
Oddle Eats scraper can be seamlessly integrated with various platforms and workflows to enhance data-driven decision-making. By connecting the Oddle Eats data scraping service with CRM systems, POS software, or analytics dashboards, businesses can automate menu updates, pricing changes, and inventory tracking. E-commerce platforms can integrate this data for real-time product listings, while marketing tools can leverage restaurant ratings, promotions, and reviews for targeted campaigns. Additionally, food delivery aggregators can merge scraped data with competitor analysis for strategic insights. With APIs and webhooks, Oddle Eats data can flow directly into business intelligence tools, enabling faster reporting and operational efficiency. These integrations streamline restaurant operations, improve customer experiences, and ensure accurate, up-to-date menu and pricing information.
Executing Oddle Eats Data Scraping Actor with Real Data API
Running the Oddle Eats data scraping service with a Real Data API ensures businesses can retrieve structured, accurate, and live restaurant data instantly. The scraping actor connects to Oddle Eats, fetches menus, prices, promotions, delivery zones, and customer reviews, then returns the information in JSON format. Using a Real Data API integration, developers can automate scheduled scrapes, filter results by cuisine, location, or ratings, and push the data to internal systems. This execution method supports pagination, concurrency, and rate-limit handling, ensuring efficient large-scale extraction. The Real Data API also supports custom query parameters, enabling highly targeted data pulls. With secure API keys and HTTPS protocols, businesses can reliably integrate Oddle Eats data into their applications, dashboards, and analytics pipelines.
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