We extract hyper-local inventory, grocery catalogues, restaurant menus, and dynamic delivery fees from Dunzo. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Store Metadata objects from dunzo.com. All fields typed and schema-versioned.
"store_id": "dz_89124", "name": "Dunzo Daily - Indiranagar", "type": "dark_store", "lat": 12.9784, "lng": 77.6408, "rating": 4.6, "delivery_time_mins": 19, "is_open": true
| # | store_id | name | type | lat | lng | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Grocery Inventory objects from dunzo.com. All fields typed and schema-versioned.
"sku_id": "sku_99214", "store_id": "dz_89124", "product_name": "Nandini Toned Fresh Milk", "brand": "Nandini", "price": 24.0, "mrp": 24.0, "in_stock": true, "unit_size": "500 ml"
| # | sku_id | store_id | product_name | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Restaurant Menus objects from dunzo.com. All fields typed and schema-versioned.
"item_id": "item_4412", "restaurant_id": "res_881", "item_name": "Chicken Biryani", "price": 320.0, "is_veg": false, "category": "Main Course", "available": true, "preparation_time": 25
| # | item_id | restaurant_id | item_name | description | price | is_veg |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dynamic Pricing objects from dunzo.com. All fields typed and schema-versioned.
"store_id": "dz_89124", "base_delivery_fee": 25.0, "surge_fee": 15.0, "packing_charge": 5.0, "tax_amount": 12.5, "total_time_mins": 22, "timestamp": "2026-05-12T09:14:00Z"
| # | store_id | geo_location | base_delivery_fee | surge_fee | packing_charge | tax_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Promotions objects from dunzo.com. All fields typed and schema-versioned.
"promo_id": "promo_112", "code": "DUNZO50", "min_order_value": 199.0, "max_discount": 50.0, "discount_pct": 20, "valid_until": "2026-12-31T23:59:59Z"
| # | promo_id | store_id | code | description | min_order_value | max_discount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Dunzo scraper handles the hyper-local complexity of quick commerce: dark store mapping, dynamic delivery fees, and rapid inventory shifts, using precise geo-coordinate injection and mobile API reverse engineering.
Inject exact lat/long coordinates to map hyper-local inventory availability across specific pin codes and neighbourhoods.
Capture base delivery fees, weather surge pricing, and small cart penalties at a minute-level cadence.
Track SKU-level stock depth and out-of-stock flags across Dunzo Daily fulfillment centres in real time.
Extract full category trees, item descriptions, dietary flags, and customisation options for food delivery partners.
Monitor exact selling price versus printed MRP to calculate platform-level discount depth across categories.
Scrape banner ads, store-level coupons, and user-agnostic promo codes with their specific redemption logic.
Bypass the web frontend to query Dunzo mobile API endpoints directly using spoofed device fingerprints and valid tokens.
Run concurrent pipelines across Bengaluru, Mumbai, Delhi NCR, Chennai, Pune, and Hyderabad simultaneously.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection.
Brief in. Clean data out.
Provide target cities, lat/long coordinates, or specific merchant IDs. We design the extraction schema together.
We configure mobile API interceptors, geo-spatial proxy routing, and token management for Dunzo endpoints.
Schema validation, null-rate checks, location accuracy verification, and sample datasets before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Quick commerce data is highly volatile and restricted by location. Here is how we bypass the limitations of standard web scrapers.
Dunzo requires precise geo-coordinates to serve a catalogue. We inject structured geo-payloads into the API headers to simulate users at specific addresses, ensuring accurate hyper-local inventory retrieval without default-location bias.
The Dunzo web interface offers limited data. We target their private mobile API endpoints directly, maintaining valid authentication tokens, HTTP/2 multiplexing, and device signatures to prevent rate limiting and blockades.
Quick commerce stock changes by the minute. We use high-frequency polling with change-detection diffing to capture out-of-stock events and price shifts without burning compute or bloating your warehouse with duplicate records.
Delivery fees fluctuate based on rider availability, time of day, and weather. We track these variables continuously across multiple geo-nodes to build accurate historical fee density maps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, API schema drift, and geo-coverage drops before they hit your warehouse. Uptime is guaranteed.
FMCG brands and rival quick-commerce apps monitor Dunzo Daily pricing, discounts, and delivery fees to adjust their own strategies.
Brands track their SKU presence, shelf-share, and out-of-stock rates across local dark stores to optimise supply chain distribution.
Analysts model hyper-local logistics costs by tracking surge pricing patterns against weather conditions and time of day.
Cloud kitchens monitor competitor menus, pricing strategies, and promotional offers within their specific delivery radius.
Retailers map dark store density, delivery times, and coverage zones to identify underserved neighbourhoods for new physical locations.
Hedge funds track SKU velocity, dark store expansion, and promotional burn rates as proxy metrics for platform growth and unit economics.
"Quick commerce is entirely hyper-local. A catalogue in Indiranagar is completely different from one in Koramangala. You cannot scrape it without precise geo-spatial orchestration."
Extracting data from Dunzo requires more than a standard crawler. It demands mobile API reverse engineering, precise lat/long coordinate injection, and high-frequency polling to catch inventory changes in a 19-minute delivery window. DataFlirt handles the device spoofing and token rotation so your team receives structured tables, not rate limits.
Everything supported by our dunzo.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Intercepting and replicating mobile API requests with valid headers, cryptographic signatures, and device fingerprints to bypass web-only restrictions.
Mapping residential IPs to the exact city of the target coordinates to prevent geo-mismatch blocks and ensure accurate local pricing.
Airflow scheduling AWS Lambda bursts for high-frequency inventory polling across thousands of pin codes simultaneously.
Data delivered to where your team already works — no new tooling required.
About dunzo.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Dunzo is generally permissible. DataFlirt targets only public, non-authenticated inventory, pricing, and restaurant data. We do not extract personal user data or circumvent authentication walls for private order histories.
We inject precise latitude and longitude coordinates into the API request headers. This allows us to simulate users standing at specific addresses, ensuring the catalogue returned is exactly what a local customer would see.
Yes. We map and monitor specific dark store fulfillment centres, tracking SKU-level pricing, discounts, and real-time out-of-stock flags across their entire FMCG and grocery catalogue.
For dynamic fields like delivery fees and surge pricing, we configure high-frequency polling pipelines that can refresh data at sub-5-minute intervals across targeted geographic nodes.
We target the mobile app API endpoints. The web interface often lacks full catalogue depth and real-time operational flags. We reverse engineer the app traffic to build a more stable and comprehensive pipeline.
Our smallest packages start at a defined list of geo-coordinates or store IDs with weekly delivery. For continuous high-frequency polling across multiple cities, we price based on request volume and delivery cadence.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off dark store catalogue dump or a continuous surge-pricing feed across multiple cities - we scope, build, and operate the pipeline. Tell us what you need.
