We extract used car listings, price drops, 200-point inspection reports, and Spinny Max inventory. 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 Car Listings objects from spinny.com. All fields typed and schema-versioned.
"car_id": "SP129485", "make": "Hyundai", "model": "Creta", "variant": "1.6 SX Plus Auto", "year": 2018, "mileage_km": 42500, "price": 1045000, "spinny_assured": true
| # | car_id | make | model | variant | year | mileage_km |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inspection Reports objects from spinny.com. All fields typed and schema-versioned.
"car_id": "SP129485", "exterior_score": 8.5, "interior_score": 9.0, "engine_score": 9.5, "imperfections_list": "['Minor scratch on rear bumper', 'Small dent on left door']", "tyre_tread_depth": "60%", "report_pdf_url": "https://spinny.com/reports/SP129485.pdf"
| # | car_id | inspection_date | inspector_name | exterior_score | interior_score | engine_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Finance objects from spinny.com. All fields typed and schema-versioned.
"car_id": "SP129485", "current_price": 1045000, "original_price": 1060000, "price_drop_amount": 15000, "booking_amount": 10000, "emi_start": 21500, "buyback_guarantee_price": 780000
| # | car_id | current_price | original_price | price_drop_amount | booking_amount | emi_start |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Media & 360 View objects from spinny.com. All fields typed and schema-versioned.
"car_id": "SP129485", "primary_colour": "Polar White", "thumbnail_url": "https://spinny.com/img/SP129485_thumb.jpg", "gallery_images": "['url1.jpg', 'url2.jpg', 'url3.jpg']", "exterior_360_url": "https://spinny.com/360/ext/SP129485/", "imperfection_images": "['scratch1.jpg']"
| # | car_id | thumbnail_url | exterior_360_url | interior_360_url | gallery_images | imperfection_images |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Hubs & Locations objects from spinny.com. All fields typed and schema-versioned.
"hub_id": "HUB_BLR_04", "hub_name": "Spinny Park Yelahanka", "city": "Bengaluru", "latitude": 13.1005, "longitude": 77.5963, "total_inventory_count": 412
| # | hub_id | hub_name | city | state | address | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Spinny scraper captures high-velocity automotive inventory. We extract structured specifications, exact pricing, financing terms, and inspection details across standard and Spinny Max catalogues.
Make, model, variant, manufacturing year, RTO code, ownership history, and odometer readings scraped for every active listing.
Monitor initial listing prices, subsequent price drops, booking amounts, and guaranteed buyback values over time.
Extract granular inspection scores across exterior, interior, engine, and underbody, including listed imperfections and replaced parts.
Capture URLs for interactive 360-degree exterior and interior views, high-resolution gallery images, and specific imperfection photos.
Map inventory to specific physical Spinny Hubs, extracting exact locations, coordinates, and local inventory volumes.
Isolate luxury inventory from the Spinny Max catalogue, capturing premium features and specialized inspection criteria.
Extract starting EMI figures, required downpayments, and loan tenures displayed on individual vehicle listings.
Track inventory velocity by detecting when vehicles are marked as booked or sold, calculating days on market.
Run pipelines at hourly cadences to capture fresh inventory additions and immediate price adjustments across all cities.
Brief in. Clean data out.
Select target cities, price brackets, or specific makes/models. We map the required Spinny data fields.
We configure Scrapy and Playwright to handle Next.js state extraction and location-based inventory loading.
Schema validation, null-rate checks, and price outlier detection before full production launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on your cadence.
Modern single-page applications require specialized extraction techniques. Here is how we build resilient pipelines for Spinny.
Spinny relies on React and Next.js. Instead of scraping the rendered DOM, our pipelines intercept and parse the underlying JSON state objects directly from the HTML source. This guarantees perfect structured data without relying on brittle CSS selectors.
Spinny segments inventory strictly by city. Our crawlers inject specific geographical coordinates and local storage tokens to simulate browsing from Delhi, Mumbai, Bengaluru, and other hubs, ensuring complete national coverage.
Used cars sell quickly. We maintain a hash index of all active vehicle IDs. Hourly delta runs identify newly added cars, price modifications, and vehicles removed from the platform, calculating precise days-on-market metrics.
Spinny interactive 360-degree views are compiled from hundreds of sequential images. We extract the base manifest URLs and compile them into structured arrays, allowing your teams to rebuild or analyze the visual data.
To prevent rate limiting during high-frequency API polling, we route all traffic through ISP-grade residential proxies physically located in India. This mimics genuine local mobile application traffic.
Used car dealerships and aggregators monitor Spinny pricing models to adjust their own procurement bids and retail prices.
Auto finance and insurance firms ingest historical price depreciation data to refine their loan-to-value and underwriting algorithms.
Analysts track days-on-market metrics across different makes and variants to identify high-demand vehicle categories.
B2B automotive platforms track newly listed Spinny inventory to understand regional supply constraints and sourcing opportunities.
Retailers analyse Spinny Hub locations and localized inventory volumes to identify underserved micromarkets for new physical lots.
Data science teams use structured inspection reports and imperfection logs to train automated vehicle valuation models.
"Spinny represents the most structured used car catalogue in India, but tracking inventory velocity requires a dedicated pipeline."
Most teams underestimate the infrastructure required to track high-velocity used car inventory. Reliable Spinny extraction requires Next.js state parsing, 360-degree image asset extraction, and continuous polling for sold vehicles. DataFlirt handles this complexity so your engineers focus on pricing models.
Everything supported by our spinny.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.
We bypass brittle DOM parsing by intercepting the underlying Next.js API calls and JSON state objects, ensuring perfect schema alignment.
Used cars are high-velocity assets. We utilize Redis-backed deduplication to run hourly delta checks, pushing only new or modified listings.
Our Playwright clusters inject specific local storage parameters and coordinates to perfectly simulate browsing from any Indian city.
Data delivered to where your team already works — no new tooling required.
About spinny.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available vehicle listings and pricing data is generally permissible under Indian law. DataFlirt extracts only unauthenticated, public inventory data. We do not bypass OTP walls or access private user profiles. Clients must ensure their specific use cases comply with applicable regulations.
Used cars sell fast. We recommend and support hourly delta runs to capture new listings, sold vehicles, and price drops. Full catalogue refreshes are typically executed daily.
Yes. Our pipelines navigate both standard Spinny Assured inventory and the premium Spinny Max catalogue, retaining the specific metadata flags for each.
Yes. We extract the structured categorical scores (exterior, interior, engine) as well as the specific text lists detailing imperfections and replaced parts.
We maintain a stateful index of all active car IDs. When an ID disappears from the active search results or returns a sold status, we flag the record with a termination timestamp in your delivery feed.
We begin tracking price drops from the moment your pipeline is commissioned. We capture both the original listing price and the current price, logging every modification.
By default, we extract and deliver the high-resolution image URLs and 360-degree manifest links. If you require raw image binaries downloaded to your S3 bucket, we can configure a secondary media pipeline.
Yes. We provide a sample extraction of up to 500 vehicles from a specific city to validate schema alignment before you commit to a production pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily dump of Delhi inventory or real-time price drop alerts across India, we build and operate the pipeline. Tell us your requirements.
