We extract membership tiers, meeting room specifications, event calendars, and location metadata from Betahaus. 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 Locations & Amenities objects from betahaus.com. All fields typed and schema-versioned.
"city": "Berlin", "neighbourhood": "Kreuzberg", "address": "Rudi-Dutschke-Strasse 23", "capacity_pax": 500, "opening_hours": "09:00-18:00", "amenities": "['WiFi', 'Coffee', 'Printing']"
| # | location_id | city | neighbourhood | address | capacity_pax | opening_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Membership Plans objects from betahaus.com. All fields typed and schema-versioned.
"plan_name": "Pro Membership", "price_monthly": 150.0, "currency": "EUR", "access_hours": "24/7", "meeting_room_credits": 4, "mail_handling": true
| # | plan_id | location | plan_name | price_monthly | currency | access_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Meeting Rooms objects from betahaus.com. All fields typed and schema-versioned.
"room_name": "Arena", "capacity_pax": 50, "price_per_hour": 75.0, "currency": "EUR", "natural_light": true, "equipment": "['Projector', 'Whiteboard', 'Video Conferencing']"
| # | room_id | location | room_name | capacity_pax | price_per_hour | price_per_day |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Events & Workshops objects from betahaus.com. All fields typed and schema-versioned.
"title": "Founders Breakfast", "date": "2026-03-15", "start_time": "09:30", "location": "Betahaus Barcelona", "format": "In-person", "price": 0.0
| # | event_id | title | date | start_time | end_time | location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Private Offices objects from betahaus.com. All fields typed and schema-versioned.
"desk_count": 8, "availability_status": "Available", "price_monthly": 2400.0, "currency": "EUR", "floor_level": 3, "included_services": "['Cleaning', 'High-speed Internet', '24/7 Access']"
| # | office_id | location | desk_count | availability_status | price_monthly | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our pipeline captures every layer of the Betahaus platform: from granular membership pricing and meeting room inventories to dynamic event calendars across all European locations.
Extract comprehensive location details including addresses, opening hours, capacity metrics, and map coordinates for every Betahaus space.
Track pricing for flex desks, fixed desks, day passes, and corporate plans across different cities and membership tiers.
Catalogue meeting room names, passenger capacities, hourly rates, daily rates, and included A/V equipment.
Extract schedules for workshops, networking events, and pitch nights, including dates, times, speakers, and registration links.
Monitor private office listings, desk counts, floor plans, and monthly rates where publicly accessible.
Map available amenities per location, capturing data on coffee bars, printing stations, phone booths, and 24/7 access flags.
Scrape data uniformly across all Betahaus locations including Berlin, Barcelona, Hamburg, and Sofia.
Extract raw pricing data and standardise currency codes to ensure clean comparative analysis across European markets.
Configure daily or weekly syncs to capture new event additions, pricing updates, and changes in room availability.
Extract lists of partner benefits, local business discounts, and software perks available to Betahaus members.
Brief in. Clean data out.
Provide target locations, membership types, or event pages. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, manage proxy rotation, and handle any rate limits on betahaus.com.
Schema validation, null-rate checks, and sample data reviews before full pipeline launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on an agreed cadence.
Extracting coworking data requires navigating dynamic calendars, varied CMS layouts, and rate limits. We manage the infrastructure so you receive clean data.
Frequent requests to meeting room and event endpoints can trigger rate limits. We use residential EU proxies and controlled concurrency to mimic normal browsing behaviour.
Event calendars and booking widgets often rely on client-side rendering. We run full Playwright browser sessions to hydrate these components before extraction.
Marketing websites frequently update their layouts. Our selector strategy uses multiple fallback chains to ensure data extraction continues smoothly despite DOM changes.
We maintain a hash index of previously scraped records. Subsequent runs only push diffs, reducing downstream processing load and storage costs.
Every run emits structured logs. We monitor for null-rate spikes or coverage drops, ensuring any site changes are addressed immediately.
Coworking operators monitor Betahaus membership tiers and meeting room rates to optimise their own pricing strategies.
Commercial real estate analysts track desk capacities and location expansion to gauge flexible workspace demand.
Startup ecosystem platforms aggregate Betahaus workshops and pitch nights into centralised community calendars.
HR teams evaluate flex desk availability and pricing across European cities for distributed workforce planning.
B2B vendors identify upcoming events and workshops to target relevant startup founders and attendees.
Workspace designers analyse amenity offerings across locations to establish baseline standards for modern offices.
"Betahaus represents a prime node in the European startup ecosystem. Extracting its pricing and event data provides direct visibility into regional workspace demand."
Manual collection of coworking rates and event schedules fails when scaling across multiple cities. DataFlirt automates the extraction of Betahaus membership tiers, room availability, and community calendars using headless browsers and residential proxies. We deliver clean, structured data directly to your warehouse, allowing your analysts to focus on pricing strategy and market research rather than pipeline maintenance.
Everything supported by our betahaus.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 and deduplication. Playwright manages JavaScript rendering for dynamic event calendars and pricing widgets.
We route requests through European residential ISP proxies, rotating IPs to prevent rate limiting while accessing location data.
Pipelines execute on AWS Lambda and ECS. Airflow manages scheduling and dependencies, with all state stored securely in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About betahaus.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information, such as location details, public event calendars, and advertised pricing, is generally permissible. We do not extract personal data from authenticated member portals.
We extract data from all publicly listed locations on betahaus.com, including Berlin, Barcelona, Hamburg, and Sofia.
We extract meeting room specifications, capacities, and listed pricing. Live booking availability is typically gated behind member authentication and is not supported.
Pipelines can be configured to run daily or weekly, ensuring your database accurately reflects newly added workshops and networking events.
Yes, we capture private office desk counts, floor levels, and monthly rates wherever this information is publicly listed on the site.
Pricing data is extracted as raw numerical values alongside the explicit currency code (e.g., EUR), allowing you to normalise the data in your warehouse.
We support JSON, CSV, and Parquet, delivered directly to AWS S3, Google BigQuery, Snowflake, or via Webhook.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of European coworking rates or a continuous feed of startup events, we build and operate the pipeline. Tell us your requirements.
