We extract workspace listings, hourly pricing, meeting room availability, amenity lists, and location metadata from Deskpass. 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 Workspace Profiles objects from deskpass.com. All fields typed and schema-versioned.
"workspace_id": "dp_84921", "name": "Industrious Fulton Market", "city": "Chicago", "state": "IL", "zip_code": "60607", "latitude": 41.8865, "longitude": -87.6521, "capacity": 150
| # | workspace_id | name | address | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Passes objects from deskpass.com. All fields typed and schema-versioned.
"workspace_id": "dp_84921", "pass_type": "day_pass", "price": 35.0, "currency": "USD", "credits_required": 1, "minimum_booking_hours": 8, "pricing_tier": "premium"
| # | workspace_id | pass_type | price | currency | credits_required | minimum_booking_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Amenities objects from deskpass.com. All fields typed and schema-versioned.
"workspace_id": "dp_84921", "wifi_speed_mbps": 500, "coffee_tea": true, "printing_services": true, "phone_booths": true, "pet_friendly": false, "kitchen_access": true
| # | workspace_id | wifi_speed_mbps | coffee_tea | printing_services | phone_booths | pet_friendly |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Operating Hours objects from deskpass.com. All fields typed and schema-versioned.
"workspace_id": "dp_84921", "day_of_week": "Monday", "open_time": "08:00", "close_time": "18:00", "staffed_hours": "09:00-17:00", "weekend_access": false
| # | workspace_id | day_of_week | open_time | close_time | staffed_hours | weekend_access |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Meeting Rooms objects from deskpass.com. All fields typed and schema-versioned.
"room_id": "mr_9932", "workspace_id": "dp_84921", "room_name": "Lakeview Boardroom", "capacity": 12, "hourly_rate": 75.0, "screen_sharing": true, "whiteboard": true
| # | room_id | workspace_id | room_name | capacity | hourly_rate | screen_sharing |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Deskpass scraper navigates map boundaries, dynamically loads venue details, and extracts pricing and availability data across thousands of locations globally.
Extract business names, addresses, geographic coordinates, and descriptions for every coworking space listed on the platform.
Capture daily desk rates and hourly meeting room costs in both local currency and Deskpass credit values.
Parse unstructured amenity lists into boolean fields for phone booths, pet policies, printing access, and kitchen facilities.
Extract open times, close times, staffed hours, and weekend access policies per location.
Catalogue individual meeting rooms within a venue, including capacity limits and specific AV equipment availability.
Iterate through geographic bounding boxes to discover unlisted or newly added spaces across target cities.
Extract parking availability, costs, and nearby public transit instructions provided by the venue operator.
Capture high-resolution image URLs for venue galleries, desk setups, and meeting room interiors.
Run daily or weekly pipelines to track new venue additions, pricing adjustments, and removed locations.
Brief in. Clean data out.
Provide target cities, zip codes, or specific venue IDs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, coordinate map traversal, and handle pagination for deskpass.com.
Schema validation, null-rate checks, location coordinate accuracy, and amenity mapping before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Deskpass relies on dynamic map loading and heavy client-side rendering. Here is how we extract complete datasets without missing hidden venues.
Deskpass limits the number of venues returned in a single map view. We use a grid-search algorithm to programmatically pan and zoom across target cities, ensuring 100% venue discovery without hitting pagination limits.
Venue details and real-time credit pricing load asynchronously via JavaScript. We execute full Playwright browser sessions to hydrate the DOM before extracting pricing tiers and meeting room availability.
Different venue operators format their amenities and rules differently. We apply post-processing scripts to normalise text into structured boolean fields, ensuring clean data for your downstream analytics.
Aggressive map querying triggers API rate limits. We distribute requests across residential proxy pools and enforce strict concurrency limits to maintain pipeline stability and avoid IP bans.
We maintain a hash index of known venues. Subsequent runs output diffs, highlighting newly added spaces, removed venues, or pricing changes, reducing processing load on your data warehouse.
Real estate firms track coworking supply density, pricing trends, and amenity standards across different urban markets.
Coworking operators monitor local pricing, meeting room rates, and operating hours to position their own inventory competitively.
Travel managers integrate venue locations and pricing into internal tools to budget for remote team offsites.
City planners and researchers correlate flexible workspace locations with transit hubs and parking infrastructure.
Workspace aggregators ingest venue metadata, photos, and amenities to enrich their own marketplace listings.
Investors use location density and pricing data to identify underserved neighbourhoods for new coworking space investments.
"Deskpass maps the flexible work economy, but querying location availability and pricing across thousands of spaces requires dedicated infrastructure."
Most teams underestimate the investment required. Reliable Deskpass scraping requires map viewport manipulation, full JavaScript rendering for dynamic pricing, and normalisation of operator-entered text. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our deskpass.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 manages crawl orchestration and deduplication. Playwright handles map rendering, lazy loading, and asynchronous pricing calls.
We maintain pools of residential ISP proxies. Rotation prevents API rate limits during intensive map grid searches.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About deskpass.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available venue information is generally permissible. DataFlirt targets only public, non-authenticated location, pricing, and amenity data. We do not extract private member data or bypass authentication walls.
We use a grid-search algorithm to programmatically divide target cities into bounding boxes. Our crawlers query the backend APIs for each coordinate grid, ensuring all venues are captured regardless of map zoom level.
Yes. Every pipeline run produces timestamped snapshots. We can maintain a time-series table per venue to track fluctuations in credit requirements or fiat pricing.
Yes. We navigate to individual venue pages to extract the complete inventory of meeting rooms, including capacity, hourly rates, and specific AV equipment.
Pipelines can be configured to run daily or weekly. A full scrape of major US and European cities typically completes within 4 to 6 hours.
Our minimum engagement starts with a defined list of target cities. We price based on geographic coverage and delivery frequency. Contact us for a specific quote.
Yes. We provide a sample run of up to 50 venues in a specific city to validate schema fit and data accuracy before you commit.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of global venues or continuous tracking of coworking rates, we build and operate the pipeline. Tell us your target cities.
