We extract new vehicle variants, city-level on-road pricing, used vehicle listings, expert reviews, and dealership networks from Zigwheels. 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 New Vehicles objects from zigwheels.com. All fields typed and schema-versioned.
"make": "Tata", "model": "Nexon", "variant_name": "Creative Plus S", "ex_showroom_price": 1179900, "on_road_price": 1354200, "city": "Bengaluru", "engine_cc": 1199, "fuel_type": "Petrol", "transmission": "Manual", "mileage_arai": 17.44
| # | make | model | variant_name | ex_showroom_price | on_road_price | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Used Vehicles objects from zigwheels.com. All fields typed and schema-versioned.
"listing_id": "ZW-U-849210", "make": "Hyundai", "model": "Creta", "registration_year": 2021, "kms_driven": 34500, "owner_number": 1, "location": "Mumbai", "asking_price": 1250000, "fuel_type": "Petrol", "transmission": "Automatic"
| # | listing_id | make | model | registration_year | kms_driven | owner_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Dealerships objects from zigwheels.com. All fields typed and schema-versioned.
"dealer_id": "DLR-9921", "dealer_name": "Advaith Hyundai", "authorised_brands": "['Hyundai']", "city": "Bengaluru", "pincode": "560001", "rating": 4.2, "reviews_count": 128, "contact_numbers": "['+91-9876543210']"
| # | dealer_id | dealer_name | authorised_brands | address | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Specifications objects from zigwheels.com. All fields typed and schema-versioned.
"variant_id": "VAR-44912", "engine_type": "1.5L Turbo GDi", "max_power_bhp": 157.81, "max_torque_nm": 253, "ground_clearance_mm": 190, "seating_capacity": 5, "airbags_count": 6, "safety_rating_gncap": 5, "sunroof_type": "Panoramic"
| # | variant_id | engine_type | max_power_bhp | max_torque_nm | cylinders | ground_clearance_mm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews objects from zigwheels.com. All fields typed and schema-versioned.
"review_id": "REV-88392", "vehicle_id": "MOD-112", "author_name": "Rahul S.", "rating_overall": 4.5, "rating_performance": 5.0, "review_title": "Excellent highway cruiser", "date_posted": "2023-11-14", "verified_owner": true, "helpful_votes": 24
| # | review_id | vehicle_id | author_name | rating_overall | rating_looks | rating_performance |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Zigwheels segments pricing by city and relies heavily on dynamic loading for variant specifications. Our managed pipeline handles location mocking, JavaScript execution, and pagination automatically.
Extract every trim level, transmission option, and engine configuration mapped to its parent vehicle model.
Capture accurate ex-showroom prices, RTO taxes, insurance costs, and handling charges across 500+ Indian cities.
Parse nested specification tables covering engine metrics, dimensions, safety features, and interior equipment.
Track secondary market listings including depreciation curves, odometer readings, and dealer asking prices.
Extract showroom addresses, contact details, and authorised service centre locations by brand and region.
Aggregate verified owner feedback, capturing granular ratings for performance, comfort, and maintenance.
Map available paint schemes, dual-tone options, and interior upholstery choices per variant.
Capture battery capacity, claimed range, charging times, and motor specifications for electric vehicles.
Monitor manufacturer price hikes and dealer discount updates with daily diffs pushed to your warehouse.
Brief in. Clean data out.
Provide target brands, vehicle segments, or specific cities for on-road pricing. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and location-header spoofing for zigwheels.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Automotive portals use complex DOM structures and location-gated pricing. Here is how we maintain data integrity.
Zigwheels displays different on-road prices, RTO taxes, and offers based on the user's location. We inject specific city cookies and headers into our Playwright sessions to extract precise local pricing across hundreds of pincodes.
Used car listings and detailed specification comparisons rely heavily on client-side rendering. We use Playwright to execute JavaScript, trigger infinite scroll events, and expand collapsed feature tables to ensure complete data capture.
Automotive taxonomy is complex, with varying trim levels and optional packs. Our extraction logic normalises these hierarchies, mapping child variants to parent models reliably even when Zigwheels alters their page layouts.
To prevent IP bans during high-volume extractions of used car inventory, we route requests through Indian residential proxy pools, maintaining realistic request rates and rotating fingerprints per session.
Vehicle specifications rarely change, but used car inventory and on-road prices fluctuate daily. We maintain a hash index of previously scraped data, emitting only modified records to reduce your storage and processing costs.
OEMs monitor on-road pricing, dealer discounts, and variant positioning against rival models across key metropolitan markets.
Fintech and auto-tech companies ingest secondary market listings to train depreciation algorithms and dynamic pricing engines.
Insurers extract exact variant specifications, safety features, and ex-showroom prices to underwrite policies accurately.
Market researchers map authorized dealer density and service centre availability to identify coverage gaps.
Analysts track new variant launches, feature adoption trends (e.g., ADAS, sunroofs), and user sentiment via reviews.
Consultancies monitor the expanding electric vehicle catalogue, comparing range claims and battery specifications against ICE equivalents.
"Zigwheels holds the most granular variant-level automotive data in India, but mapping on-road prices across 500 cities requires serious infrastructure."
Extracting automotive data accurately means navigating location-gated pricing, complex variant hierarchies, and JavaScript-heavy specification tables. DataFlirt manages the proxies, the DOM parsing, and the city-level cookie spoofing so your team receives clean, normalised vehicle records ready for analysis.
Everything supported by our zigwheels.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.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About zigwheels.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We can configure the pipeline to iterate through a predefined list of Indian cities and pincodes, injecting the necessary location cookies to extract accurate ex-showroom, RTO, and insurance costs per variant.
Automotive taxonomy is notoriously nested. Our schema normalises the data, ensuring every specific trim level is accurately linked to its parent model, fuel type, and transmission configuration.
Yes. We can configure daily or sub-daily pipelines to monitor the used car section, capturing new listings, price drops, and sold vehicles to maintain an accurate view of the secondary market.
We extract publicly listed dealership names, addresses, authorised brands, and contact numbers across all major cities, formatting them into a clean location dataset.
Zigwheels specifications are often hidden behind interactive UI elements. We use Playwright to render the page fully and execute the necessary JavaScript to expose all technical data before extraction.
Absolutely. We provide a sample run covering a specific manufacturer or a subset of used car listings during the scoping phase, allowing you to validate schema fit and data completeness before engagement.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of used car listings or a complete extraction of new vehicle specifications across 50 cities — we scope, build, and operate the pipeline. Tell us what you need.
