We extract workspace listings, hot desk pricing, amenity lists, location coordinates, and user reviews from Coworker. 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 Listings objects from coworker.com. All fields typed and schema-versioned.
"workspace_id": "CW-84921", "name": "WeWork Galaxy", "city": "Bengaluru", "country": "India", "latitude": 12.9738, "longitude": 77.6119, "capacity": 1200, "host_name": "WeWork India"
| # | workspace_id | name | url | country | city | address |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Desks objects from coworker.com. All fields typed and schema-versioned.
"workspace_id": "CW-84921", "hot_desk_daily": 800.0, "hot_desk_monthly": 12000.0, "dedicated_desk_monthly": 18000.0, "private_office_monthly": 45000.0, "currency": "INR", "day_pass_available": true, "price_timestamp": "2026-05-12T09:14:00Z"
| # | workspace_id | hot_desk_daily | hot_desk_monthly | dedicated_desk_monthly | private_office_monthly | meeting_room_hourly |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Amenities & Features objects from coworker.com. All fields typed and schema-versioned.
"workspace_id": "CW-84921", "has_wifi": true, "internet_speed_mbps": 150, "has_coffee": true, "access_24_7": true, "pet_friendly": false, "has_parking": true, "meeting_rooms_count": 8
| # | workspace_id | has_wifi | internet_speed_mbps | has_coffee | has_parking | access_24_7 |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from coworker.com. All fields typed and schema-versioned.
"review_id": "REV-99214", "workspace_id": "CW-84921", "rating_overall": 4.8, "rating_wifi": 5.0, "rating_community": 4.5, "review_text": "Excellent internet and great community events. Parking is limited.", "review_date": "2026-03-14", "verified_member": true
| # | review_id | workspace_id | reviewer_name | rating_overall | rating_wifi | rating_community |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from coworker.com. All fields typed and schema-versioned.
"location": "Bengaluru", "position": 3, "workspace_id": "CW-84921", "name": "WeWork Galaxy", "rating": 4.8, "review_count": 342, "starting_price": 800.0, "is_featured": true, "scraped_at": "2026-05-12T09:15:22Z"
| # | keyword | location | position | workspace_id | name | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Coworker scraper handles every layer of the platform: global map searches, dynamic desk pricing, amenity lists, and the review corpus with JavaScript rendering, session management, and anti-bot circumvention built in.
Name, address, geo-coordinates, description, operating hours, capacity, and every metadata field Coworker surfaces scraped at the listing level.
Extract daily, monthly, and hourly rates for hot desks, dedicated desks, private offices, and meeting rooms across all supported currencies.
Map raw text into structured booleans for wifi, coffee, 24/7 access, pet policies, parking, and accessibility features.
Full review text, overall scores, sub-ratings for wifi and community, verified member flags, and timestamps paginated across all review pages.
Execute bounding box queries and location-based searches to capture all inventory in a specific city, neighbourhood, or postal code.
Extract operator names, total locations managed, and response time metrics to identify major regional players.
Parse opening times, weekend access rules, and staff availability hours into structured time formats.
Extract high-resolution gallery links, floor plans, and primary listing photos for internal dashboard enrichment.
Run one-off bulk exports or configure continuous pipelines at weekly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide target cities, countries, operator names, or specific listing URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and pagination logic for coworker.com.
Schema validation, null-rate checks, price-outlier detection, and coordinate mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Coworker relies heavily on map-based interfaces and dynamic content loading. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Scraping global directories triggers rate limits and IP bans. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management trained on real user behaviour patterns.
Coworker search results are often tied to map bounding boxes via hidden API calls. We intercept these XHR requests directly or use full Playwright sessions to manipulate map bounds, ensuring no listings are missed in dense urban areas.
Listing layouts vary depending on the operator and workspace type. Our selector strategy uses multiple fallback chains per field, so a missing price table or rearranged amenity list does not break your data pipeline overnight.
For large global tracking, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost, storage bloat, and downstream processing load. You get a clean changelog rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops, and respond before you notice. SLA uptime is contractual, not aspirational.
Coworking operators monitor hot desk, private office, and meeting room rates across specific neighbourhoods to optimise their own pricing.
Commercial real estate firms track coworking density, amenity trends, and yield across cities to identify underserved markets.
Workspace aggregators enrich their internal databases with global inventory, standardising amenities and geo-coordinates.
B2B service providers identify independent workspace operators to sell software, furniture, or facility management services.
Enterprise HR teams build internal booking tools and stipends based on actual workspace availability and costs in distributed team locations.
PE firms evaluate operator footprint expansion and review sentiment over time to assess market position before acquisition.
"Coworker holds the most comprehensive index of flexible workspaces globally, but extracting structured pricing across 170 countries requires specialised infrastructure."
Most teams underestimate the investment required: reliable Coworker scraping requires residential proxies, map-based pagination handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our coworker.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 map interactions. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies globally. 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 coworker.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Coworker is generally permissible under applicable law. DataFlirt targets only public, non-authenticated workspace, pricing, and review data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review Coworker's ToS and consult legal counsel for specific use cases.
We programmatically generate bounding box coordinates to cover target cities or regions, intercepting the underlying API calls or driving headless browsers to paginate through all available map pins without missing dense clusters.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record per workspace for hot desk, dedicated desk, and private office rates from the date your pipeline starts.
Targeted updates for specific cities or operators can run at hourly cadences. Full global catalogue refreshes typically complete within a 24-hour window depending on the total volume requested.
Yes. We extract the full list of amenities and normalise them into structured boolean fields. We also capture overall review scores alongside specific sub-ratings for wifi, community, and facilities.
Our smallest packages start at a defined city or operator list with weekly delivery. For full global extraction or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 500 workspaces or a specific major city as part of the pre-engagement scoping process, so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off global directory export or continuous pricing feeds across key markets, we scope, build, and operate the pipeline. Tell us what you need.
