We extract vehicle dimensions, engine specifications, performance metrics, and model histories from Autoevolution. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Vehicle Specs objects from autoevolution.com. All fields typed and schema-versioned.
"make": "Porsche", "model": "911 Carrera", "generation": "992", "body_style": "Coupe", "production_years": "2019-Present", "engine_type": "Twin-Turbo Flat-6", "displacement_cc": 2981, "horsepower_hp": 385
| # | make | model | generation | body_style | segment | production_years |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Dimensions objects from autoevolution.com. All fields typed and schema-versioned.
"length_mm": 4519, "width_mm": 1852, "height_mm": 1298, "wheelbase_mm": 2450, "curb_weight_kg": 1505, "cargo_volume_l": 132, "drag_coefficient_cd": 0.29
| # | length_mm | width_mm | height_mm | wheelbase_mm | track_front_mm | track_rear_mm |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Performance & Fuel objects from autoevolution.com. All fields typed and schema-versioned.
"top_speed_kmh": 293, "acceleration_0_100_s": 4.2, "fuel_combined_l100km": 9.4, "co2_emissions_gkm": 214, "emission_standard": "Euro 6d-TEMP", "fuel_capacity_l": 64
| # | top_speed_kmh | acceleration_0_100_s | fuel_city_l100km | fuel_highway_l100km | fuel_combined_l100km | co2_emissions_gkm |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Motorcycle Specs objects from autoevolution.com. All fields typed and schema-versioned.
"brand": "Ducati", "model": "Panigale V4", "category": "Superbike", "engine_cc": 1103, "cooling_system": "Liquid", "dry_weight_kg": 175, "fuel_capacity_l": 16
| # | brand | model | category | engine_cc | cooling_system | gearbox |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for News & Reviews objects from autoevolution.com. All fields typed and schema-versioned.
"article_id": "184920", "headline": "2025 BMW M5 Touring Spied Testing on the Nurburgring", "author": "Mircea Panait", "publish_date": "2024-05-12T08:30:00Z", "category": "Spyshots", "tags": "['BMW', 'M5', 'Touring', 'V8', 'PHEV']", "related_models": "['BMW M5']"
| # | article_id | headline | author | publish_date | category | tags |
|---|---|---|---|---|---|---|
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Our Autoevolution scraper extracts the complete automotive database: from granular engine metrics and aerodynamic coefficients to historical model generations and daily industry news.
Engine architecture, transmission details, performance metrics, and drivetrain configurations scraped for every model year.
Wheelbase, track width, cargo volume, ground clearance, and drag coefficients captured and normalised to standard metric units.
WLTP and NEDC ratings, CO2 output figures, and emission standards tracked across all internal combustion and hybrid variants.
Track automotive lineage from inception. We map parent-child relationships between vehicle generations and facelifts.
Complete two-wheeler specifications including engine displacement, braking systems, dry weights, and suspension setups.
Daily articles, spyshots, and industry updates scraped with full text, author metadata, and high-resolution image links.
Corporate history, brand acquisitions, and manufacturer milestones extracted from the dedicated brand pages.
Extract raw image URLs for exterior shots, interior cabins, and technical diagrams across all model galleries.
Run one-off bulk exports of the historical catalogue or configure continuous pipelines for daily news and new model releases.
Brief in. Clean data out.
Provide target manufacturers, specific model lines, or news categories. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and table parsing logic for autoevolution.com.
Schema validation, unit normalisation checks, and historical data gap detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting deep automotive databases requires parsing complex nested HTML tables, handling historical data gaps, and normalising units across decades of vehicle records.
Autoevolution uses deeply nested and sometimes inconsistent HTML tables for specifications. Our parsers map specific row headers to schema fields, handling merged cells and missing data points without breaking the pipeline.
Vehicle specs often mix metric and imperial units depending on the target market. We normalise all outputs to strict metric formats (mm, kg, km/h, kW) or provide dual-field outputs based on your warehouse requirements.
Data for a 1960s classic car looks very different from a 2024 EV. Our schemas accept sparse data gracefully, ensuring that missing fields like 'battery capacity' on a vintage V8 do not cause validation failures.
Scraping tens of thousands of spec sheets triggers firewall blocks. We distribute requests across European datacenter and residential proxy pools, respecting rate limits while maintaining high throughput.
We maintain a hash index of last-seen values per model. Subsequent runs only push diffs for corrected specs or new facelifts, reducing compute cost and downstream processing load.
Car comparison websites and classifieds populate their backend databases with accurate, historical specifications.
Risk modellers correlate engine displacement, power-to-weight ratios, and top speeds with actuarial risk profiles.
Engineering teams validate track widths, wheelbases, and ground clearances to design compatible aftermarket components.
Corporate fleet operators track official fuel economy figures and CO2 emissions to optimise procurement and taxation.
Machine learning teams use the vast corpus of automotive specs and news to train domain-specific LLMs.
Analysts track industry trends, model lifecycles, and the shift towards electrification across manufacturer portfolios.
"Autoevolution holds decades of precise automotive engineering data - but extracting it requires navigating millions of nested HTML tables and inconsistent spec formats."
Most teams underestimate the investment required: reliable Autoevolution scraping requires handling severe rate limits, normalising imperial and metric units, and parsing complex historical data structures. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our autoevolution.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 high-concurrency crawl orchestration, deduplication, and retry logic. Perfect for navigating deep structural links across millions of model pages.
We deploy custom Python pipelines to clean strings, normalise units, and handle missing table cells before the data ever reaches your warehouse.
Pipelines run on AWS ECS. Airflow handles scheduling and dependency management. Data is written directly to S3 or BigQuery using efficient columnar formats.
Data delivered to where your team already works — no new tooling required.
About autoevolution.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Autoevolution is generally permissible for factual data like vehicle dimensions and engine specifications. DataFlirt targets only public, non-authenticated technical specs and news articles. We do not circumvent authentication walls. Clients should consult legal counsel for specific commercial use cases.
Our schemas are designed to be flexible. If a 1970s vehicle lacks a CO2 emissions figure or digital infotainment specs, the pipeline outputs null for those specific fields rather than failing the entire record validation.
Yes. We run custom parsing middleware that detects the unit type and converts it to a standard metric format (e.g., converting horsepower to kW, or inches to millimetres), ensuring your database remains clean and queryable.
We extract the high-resolution source URLs for gallery images, exterior shots, and interior views. We deliver these URLs in the JSON payload. If you require binary image downloads, we can configure an S3 sync job.
Yes. The Autoevolution two-wheeler database is fully supported, including specific fields for dry weight, chain drives, and cooling systems.
Our smallest packages start at a defined manufacturer list (e.g., all German brands) with weekly delivery. For the entire historical catalogue, we price based on initial bulk volume and ongoing update frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full dump of historical car specs or a daily feed of automotive news - we scope, build, and operate the pipeline. Tell us what you need.
