We extract cosmetics listings, shade matrices, ingredient profiles, pricing signals, and customer reviews from Beauty Bay. 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 Product Listings objects from beautybay.com. All fields typed and schema-versioned.
"sku": "BYBA0012", "brand": "By BEAUTY BAY", "title": "Bright 42 Colour Palette", "price": 25.0, "currency": "GBP", "stock_status": "in_stock", "vegan_flag": true, "cruelty_free_flag": true
| # | sku | brand | title | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Shade & Colour Matrices objects from beautybay.com. All fields typed and schema-versioned.
"variant_sku": "FLOR0034-SH02", "parent_sku": "FLOR0034", "shade_name": "Warm Peach", "hex_code": "#F4C2A6", "price": 12.5, "stock_status": "out_of_stock", "colour_family": "Peach", "swatch_image_url": "https://beautybay.com/images/swatch/FLOR0034-SH02.jpg"
| # | variant_sku | parent_sku | shade_name | hex_code | colour_family | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promos objects from beautybay.com. All fields typed and schema-versioned.
"sku": "BYBA0012", "base_price": 25.0, "sale_price": 17.5, "discount_pct": 30, "tribe_price": 15.0, "currency": "GBP", "promo_tags": "['Black Friday', 'Haul']", "scrape_timestamp": "2026-11-24T10:05:00Z"
| # | sku | base_price | sale_price | discount_pct | tribe_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from beautybay.com. All fields typed and schema-versioned.
"review_id": "REV-992817", "sku": "BYBA0012", "rating": 5, "author": "Sarah J.", "review_date": "2026-10-12", "review_title": "Incredible pigment", "verified_buyer": true, "shade_purchased": "N/A"
| # | review_id | sku | rating | author | review_date | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Brand & Category Data objects from beautybay.com. All fields typed and schema-versioned.
"brand_name": "The Ordinary", "brand_url": "https://www.beautybay.com/l/theordinary/", "category_path": "Skincare > Serums", "product_count": 45, "is_trending": true, "active_promotions": "['3 for 2 on Skincare']", "brand_description": "Clinical formulations with integrity."
| # | brand_name | brand_url | category_path | product_count | banner_image_url | brand_description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Beauty Bay scraper handles the complexities of cosmetic retail data: dynamic shade selectors, messy ingredient lists, promotional pricing, and deeply paginated brand catalogues.
Title, brand, description, usage directions, and every metadata field Beauty Bay surfaces, scraped at the SKU level.
Handle complex UI matrices for foundations and concealers. We map parent SKUs to child variants, capturing hex codes, shade names, and specific swatch images.
Extract raw ingredient text for chemical analysis, formulation comparisons, and allergen detection.
Capture base price, sale price, discount percentages, and Tribe loyalty pricing flags, timestamped per crawl.
Full review text, star ratings, helpful vote counts, and verified buyer flags, paginated across all review pages.
Detect out-of-stock statuses at the individual shade and variant level to monitor demand and supply chain gaps.
Capture product tags including Vegan, Cruelty-Free, Trending, Exclusive, and New In.
Navigate the complete A-Z brand list to extract entire brand portfolios and monitor new product launches.
Run one-off bulk exports or configure continuous pipelines at daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide brand URLs, category paths, or specific SKUs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for beautybay.com.
Schema validation, null-rate checks, price-outlier detection, and swatch mapping verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Cosmetics retail sites rely on heavy JavaScript for variant selection and bot mitigation. Here is how we stay resilient.
Beauty Bay product pages use dynamic JavaScript for shade selection and price updates. We run full Playwright browser sessions to trigger these DOM changes and capture data that headless HTTP clients miss entirely.
We use UK and US residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass rate limits and bot challenges.
A single foundation can have 50+ shades. Our scrapers map these complex matrices, ensuring every child SKU is correctly associated with its parent product, price, and stock status.
For large brand catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing ingredient lists, and schema drift.
Retailers and brands monitor pricing, discount events, and clearance sales to optimise their own pricing strategies.
R&D teams extract ingredient lists to track formulation trends, identify common active ingredients, and monitor allergen compliance.
Analysts track new product launches, out-of-stock velocity, and review volume to identify trending indie beauty brands.
Merchandising teams analyse category depth and shade ranges to identify whitespace in their own product offerings.
Brands mine review text and star ratings to understand customer feedback on specific formulations, packaging, or shade matches.
Brands audit retail listings for Minimum Advertised Price violations and unauthorised discounting.
"Beauty Bay aggregates the most volatile indie beauty brands and fast-moving trends. Extracting this catalogue requires mapping thousands of complex shade matrices and ingredient lists."
Cosmetics scraping is notoriously difficult due to complex parent-child SKU relationships, dynamic shade selectors, and highly unstructured ingredient text. DataFlirt handles the JavaScript rendering and normalisation so your data science teams receive clean, structured tables ready for immediate analysis.
Everything supported by our beautybay.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 handles JavaScript rendering, shade selection, and interaction flows.
We maintain pools of residential ISP proxies across UK and US regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. 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 beautybay.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from retail sites is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time.
Yes. Our scrapers map the complex parent-child SKU relationships, iterating through every available shade to capture specific hex codes, swatch images, and stock statuses.
Yes. We extract the raw ingredient text block from the product details tab, providing it as a clean string for your formulation analysis.
Pipelines can be configured for daily or weekly refreshes depending on your requirements. Real-time streaming is available for specific monitored SKUs.
Our smallest packages start at a defined brand list or category subset with weekly delivery. We price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across thousands of SKUs, we scope, build, and operate the pipeline. Tell us what you need.
