We extract heavy machinery listings, dealer inventories, pricing signals, and technical specifications from Mascus. 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 Machine Listings objects from mascus.com. All fields typed and schema-versioned.
"listing_id": "M8392104A", "title": "Caterpillar 320 EL", "brand": "Caterpillar", "model": "320 EL", "year_of_manufacture": 2018, "operating_hours": 4520, "price": 85000.0, "currency": "EUR"
| # | listing_id | title | brand | model | year_of_manufacture | operating_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Technical Specs objects from mascus.com. All fields typed and schema-versioned.
"listing_id": "M8392104A", "engine_power": "122 kW", "gross_weight": "22500 kg", "emission_level": "Stage IV", "condition_grade": "4/5", "ce_marked": true, "full_service_history": true
| # | listing_id | engine_power | gross_weight | emission_level | track_width | transport_dimensions |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Dealer Data objects from mascus.com. All fields typed and schema-versioned.
"dealer_id": "D49281", "dealer_name": "Nordic Heavy Machinery Oy", "phone_number": "+358 40 123 4567", "inventory_count": 142, "address": "Teollisuustie 15, Vantaa", "website": "www.nordicheavy.fi", "member_since": "2014"
| # | dealer_id | dealer_name | contact_name | phone_number | website | |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Market objects from mascus.com. All fields typed and schema-versioned.
"listing_id": "M8392104A", "current_price": 85000.0, "price_excluding_tax": 68548.0, "vat_percentage": 24.0, "days_on_market": 14, "currency": "EUR", "leasing_available": true
| # | listing_id | current_price | original_price | price_excluding_tax | vat_percentage | days_on_market |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Search Results objects from mascus.com. All fields typed and schema-versioned.
"keyword": "excavator 20t", "total_results": 1842, "position": 3, "listing_id": "M8392104A", "title": "Caterpillar 320 EL", "price": 85000.0, "promoted_listing": false
| # | keyword | category | total_results | position | listing_id | title |
|---|---|---|---|---|---|---|
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Our Mascus scraper handles every layer of the platform: equipment specifications, dealer inventories, cross-border pricing, and operating hour metrics with JavaScript rendering and anti-bot circumvention built in.
Make, model, year, operating hours, gross weight, and every technical specification field Mascus surfaces for heavy machinery.
Extract dealer names, addresses, inventory counts, and contact details across all global Mascus directories.
Capture local prices, VAT percentages, and tax-excluded figures across different European and global domains.
Parse unstructured description text and structured spec tables for engine power, emission levels, and CE markings.
Monitor machine usage metrics correlated with age to build accurate depreciation models for residual value analysis.
Maintain structural integrity of the Mascus taxonomy, from construction and agriculture to material handling.
Scrape local domains like mascus.de, mascus.co.uk, and mascus.fi while normalising output fields into English.
Distinguish between organic inventory and paid placements to understand dealer marketing behaviour.
Run continuous pipelines at daily cadences with change-detection diffing to track newly added or sold inventory.
Brief in. Clean data out.
Provide categories, brands, geographic regions, or dealer IDs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and JavaScript rendering for mascus.com.
Schema validation, null-rate checks, price-outlier detection, and sample records before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Mascus employs rate limiting and bot detection to protect dealer data. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Mascus monitors request velocity and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to avoid IP bans.
Dealer contact details are often gated behind JavaScript interactions. We run full Playwright browser sessions to trigger these reveals and extract the underlying data.
Mascus operates dozens of local domains with different languages and currency formats. Our pipeline normalises these variations into a single, unified schema for your warehouse.
Our selector strategy uses multiple fallback chains per field, ensuring that minor layout changes on specific machinery categories do not break your data pipeline.
We maintain a hash index of last-seen values for inventory listings. Subsequent runs only push diffs, reducing downstream processing load and storage requirements.
Financial institutions and leasing companies use historical pricing and operating hours to build depreciation models.
Heavy machinery dealers monitor competitor inventory levels, pricing strategies, and days-on-market metrics.
Construction firms and agricultural enterprises track specific machine models across Europe to find the best acquisition targets.
Analysts correlate machine age, condition grades, and operating hours with asking prices to determine residual asset values.
Manufacturers track the secondary market volume of their brands versus competitors to gauge long-term asset lifecycle.
Parts suppliers analyse regional concentrations of specific machine models to optimise their spare parts distribution networks.
"Mascus holds the definitive dataset for global heavy equipment pricing, but extracting normalised specifications across fifty local domains requires serious infrastructure."
Most teams underestimate the investment required: reliable Mascus extraction requires residential proxies, full JavaScript rendering for contact details, domain-specific normalisation, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our mascus.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 across global regions. 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 mascus.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Mascus is generally permissible under applicable law. DataFlirt targets only public, non-authenticated inventory and dealer data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review platform terms and consult legal counsel for specific use cases.
Dealer contact numbers are often obscured and require user interaction to reveal. We use Playwright to simulate these clicks and extract the dynamically loaded contact information at scale.
We support the global mascus.com domain as well as local variants including mascus.de, mascus.co.uk, mascus.fi, and mascus.fr. Our extraction schema normalises data across these domains into a unified format.
Full category refreshes at daily cadence complete within a 6 to 12 hour window depending on catalogue size. Streaming pipelines for specific dealer targets can achieve sub-60-minute latency.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per listing to track price adjustments and calculate accurate days-on-market metrics.
Our smallest packages start at a defined category or dealer list with weekly delivery. For full-site extractions or custom schema requirements, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off machinery catalogue dump or a continuous price-monitoring feed across global dealers, we scope, build, and operate the pipeline. Tell us what you need.
