We extract salon listings, service menus, pricing signals, staff profiles, and customer reviews from Booksy. 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 Salon Profiles objects from booksy.com. All fields typed and schema-versioned.
"salon_id": "bksy_49281", "name": "Fade & Blade Barbershop", "category": "Barbershop", "city": "London", "rating": 4.9, "review_count": 1432, "latitude": 51.5074, "longitude": -0.1278
| # | salon_id | name | category | address_line | city | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Services & Pricing objects from booksy.com. All fields typed and schema-versioned.
"salon_id": "bksy_49281", "service_id": "srv_88392", "service_name": "Skin Fade & Beard Trim", "price_amount": 45.0, "price_currency": "GBP", "duration_minutes": 60, "is_popular": true, "scraped_at": "2026-05-12T10:14:00Z"
| # | salon_id | service_id | category_name | service_name | description | price_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Staff Members objects from booksy.com. All fields typed and schema-versioned.
"salon_id": "bksy_49281", "staff_id": "stf_1029", "first_name": "Marcus", "job_title": "Senior Barber", "rating": 5.0, "review_count": 842, "is_bookable": true, "scraped_at": "2026-05-12T10:14:05Z"
| # | salon_id | staff_id | first_name | last_name | job_title | avatar_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from booksy.com. All fields typed and schema-versioned.
"review_id": "rev_994821", "salon_id": "bksy_49281", "star_rating": 5, "author_name": "James T.", "service_received": "Skin Fade & Beard Trim", "review_text": "Best fade in the city. Marcus never misses.", "review_date": "2026-05-10", "scraped_at": "2026-05-12T10:14:10Z"
| # | review_id | salon_id | staff_id | author_name | star_rating | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from booksy.com. All fields typed and schema-versioned.
"keyword": "barber", "location": "Soho, London", "position": 1, "salon_id": "bksy_49281", "promoted_badge": false, "distance_miles": 0.4, "rating": 4.9, "scraped_at": "2026-05-12T10:15:22Z"
| # | keyword | location | position | salon_id | name | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Booksy scraper maps the local beauty economy: business metadata, exact service pricing, staff profiles, and customer reviews. We handle geo-location spoofing, GraphQL interception, and pagination logic.
Capture business name, precise coordinates, address, operating hours, social links, and portfolio image URLs across all categories.
Extract full service hierarchies, exact pricing, currency, duration, and popularity badges for every treatment offered.
Map individual staff members, their job titles, personal ratings, review counts, and specific services they provide.
Paginate through customer reviews to extract star ratings, text, date, service received, and owner responses.
Simulate searches from specific GPS coordinates to track ranking positions, distances, and promoted badges for local SEO analysis.
Extract data from Booksy US, UK, PL, ES, and other localized domains using region-specific residential proxies.
Run continuous pipelines that detect price changes, new staff additions, or altered operating hours without re-processing static data.
Bypass fragile HTML parsing by directly intercepting Booksy internal GraphQL API responses for structured, reliable JSON data.
Handle rate limits and IP blocks using residential proxy rotation, realistic TLS fingerprints, and automated CAPTCHA solving.
Brief in. Clean data out.
Provide target cities, coordinates, business categories, or specific Booksy URLs. We design the extraction schema.
We configure Python crawlers, GraphQL interception, residential proxy pools, and rate-limit handling.
Schema validation, null-rate checks, and geo-coordinate verification before full production launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on an agreed schedule.
Booksy relies on dynamic single-page applications and strict rate limiting. Here is how we maintain stable data pipelines.
Booksy renders data via a complex React frontend powered by internal GraphQL APIs. Instead of parsing DOM elements, our Playwright scripts intercept the network layer, capturing the raw JSON payloads directly from the API for perfect schema stability.
Search results on Booksy are strictly bound to user location. We configure our headless browsers with exact GPS coordinates and match them with localized residential proxies to extract accurate, location-dependent ranking data.
Booksy aggressively throttles IPs that paginate through hundreds of reviews or search pages quickly. We distribute requests across thousands of residential IPs, randomising delays to mimic human browsing behaviour and avoid HTTP 429 errors.
Salons name their services inconsistently. We extract the raw names but also capture Booksy internal category IDs, allowing you to normalise pricing data across thousands of businesses for accurate market analysis.
Frontend APIs change without warning. Our Prometheus and Grafana stack monitors null rates and field availability in real time. If Booksy updates their GraphQL schema, our engineers are alerted instantly to patch the pipeline.
Software companies selling to salons and barbershops use Booksy data to build highly targeted outreach lists with verified operating details.
Franchises and independent salons monitor local competitor pricing for standard services to optimise their own service menus.
Retailers and service brands map salon density, review sentiment, and average pricing by postcode to identify underserved neighbourhoods.
Market researchers track the emergence of new service types and changing consumer preferences through review text analysis.
Local business directories enrich their own databases with verified coordinates, operating hours, and portfolio images.
Machine learning teams use structured service descriptions, pricing, and review pairs to train vertical-specific language models.
"Booksy holds the definitive dataset for the local beauty and wellness economy, but extracting structured pricing and service menus requires navigating complex GraphQL endpoints and aggressive rate limits."
Most teams underestimate the infrastructure required to extract local business data at scale. Reliable Booksy scraping demands geo-proxies, GraphQL interception, and daily schema maintenance. DataFlirt absorbs that operational burden so your engineers can focus on product development.
Everything supported by our booksy.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.
We bypass complex DOM rendering by using Playwright to intercept and decode internal GraphQL API responses, ensuring structured and resilient data extraction.
Pools of residential ISP proxies mapped to specific cities and postcodes ensure that localized search results reflect exact ground truth.
Pipelines run on AWS Lambda and Kubernetes. Airflow handles scheduling and dependency management, with all state stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About booksy.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Booksy is generally permissible under applicable law. DataFlirt targets only public, non-authenticated business profiles, service menus, and reviews. We do not extract personal user data or circumvent authentication walls.
We utilise residential ISP proxies, distribute requests across high-concurrency pools, and randomise request timing. By intercepting GraphQL APIs rather than loading full browser assets for every page, we minimise bandwidth and avoid triggering automated blocks.
Yes. We configure our crawlers with exact latitude and longitude coordinates, combined with location-matched proxies, to simulate searches from any specific neighbourhood or city worldwide.
Depending on your pipeline configuration, we can refresh target salon data daily, weekly, or monthly. Change-detection pipelines will highlight price adjustments immediately upon completion of a run.
Our smallest packages start at a defined list of locations or business categories (typically 5,000-20,000 profiles) with monthly delivery. For continuous national-scale extraction, we price based on compute volume and frequency.
We extract the direct URLs to all portfolio images, avatar photos, and salon gallery pictures. We deliver the URLs in the dataset for you to download, or we can configure a pipeline to download and store the images in your S3 bucket.
Yes. We provide a sample run of up to 200 salon profiles in your target city as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of London barbershops or a continuous price-monitoring feed across multiple countries, we scope, build, and operate the pipeline. Tell us what you need.
