We extract cosmetics listings, shade variations, fragrance profiles, ingredient lists, and pricing from faces.com. Delivered as clean JSON, CSV, or Parquet to your warehouse.
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 faces.com. All fields typed and schema-versioned.
"product_id": "FCS-89210", "title": "Double Wear Stay-in-Place Makeup", "brand": "Estée Lauder", "category": "Makeup", "sub_category": "Foundation", "price": 245.0, "currency": "AED", "product_url": "https://www.faces.com/ae-en/estee-lauder-double-wear"
| # | product_id | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Shades & Variations objects from faces.com. All fields typed and schema-versioned.
"parent_id": "FCS-89210", "variant_id": "VAR-1029", "shade_name": "2W1 Dawn", "hex_code": "#D4A373", "colour_family": "Warm", "price": 245.0, "in_stock": true, "stock_level": "High"
| # | parent_id | variant_id | shade_name | hex_code | colour_family | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fragrance Profiles objects from faces.com. All fields typed and schema-versioned.
"product_id": "FCS-44120", "brand": "Dior", "fragrance_family": "Floral", "top_notes": "['Bergamot', 'Mandarin']", "heart_notes": "['Grasse Rose', 'Jasmine']", "base_notes": "['White Musk', 'Patchouli']", "concentration": "Eau de Parfum", "volume_ml": 100
| # | product_id | title | brand | fragrance_family | top_notes | heart_notes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promos objects from faces.com. All fields typed and schema-versioned.
"product_id": "FCS-89210", "base_price": 245.0, "discount_price": 196.0, "discount_pct": 20, "currency": "AED", "promo_badge": "Beauty Week Special", "gift_with_purchase": false, "stock_status": "In Stock"
| # | product_id | base_price | discount_price | discount_pct | currency | promo_badge |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from faces.com. All fields typed and schema-versioned.
"review_id": "REV-99182", "product_id": "FCS-89210", "rating": 5, "review_text": "Provides excellent coverage without feeling heavy.", "skin_type": "Combination", "age_range": "25-34", "helpful_votes": 14, "date_posted": "2026-03-14"
| # | review_id | product_id | author_name | rating | review_text | skin_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our faces.com scraper navigates complex product variations, shade matrices, and dynamic promotional pricing — handling JavaScript rendering and regional blocks automatically.
Extract titles, descriptions, how-to-use instructions, and high-resolution image URLs across all beauty categories.
Map parent products to dozens of shade variants, capturing hex codes, colour families, and variant-specific pricing.
Structure olfactory profiles into top, heart, and base notes, alongside concentration types and volume metrics.
Extract and normalise raw ingredient strings into parseable arrays for formulation analysis and compliance checks.
Capture base prices, discount percentages, promotional badges, and gift-with-purchase indicators.
Track inventory status at the variant level to monitor out-of-stock rates and restock cadences.
Collect user feedback, star ratings, and reviewer metadata such as skin type and age range.
Maintain the exact taxonomy used by faces.com to categorise brands, lines, and sub-categories.
Extract localised catalogues and pricing across UAE, KSA, and other supported Middle Eastern regions.
Run continuous pipelines that output only changed records, reducing downstream processing costs.
Brief in. Clean data out.
Provide target brands, categories, or specific product URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for faces.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Beauty sites rely heavily on visual matrices and dynamic inventory. Here is how we maintain data integrity.
A single foundation can have 50+ shades, each with unique stock statuses and hex codes. Our pipeline maps these multi-dimensional variants back to the parent product, ensuring no orphaned SKUs.
Faces.com frequently updates pricing during beauty weeks and holiday events. We capture base price, active discount, and promo badges, timestamping every observation.
Pricing and availability differ vastly between the UAE, KSA, and Kuwait storefronts. We route requests through region-specific residential proxies to capture accurate local data.
Product reviews, swatch images, and dynamic stock indicators are loaded client-side. We use Playwright to execute JavaScript and wait for network idle states before parsing the DOM.
We hash product records per run. If a price or stock level changes, we emit the diff. If nothing changes, we skip it, keeping your data warehouse clean and compute costs low.
Beauty retailers and distributors monitor competitor pricing, discount depth, and promotional calendars.
Merchandising teams analyse brand coverage, category depth, and shade availability to optimise their own catalogues.
Analysts track new product launches and review velocity to identify emerging skincare and fragrance trends.
Formulators and compliance teams mine ingredient lists to track the adoption of active compounds or restricted substances.
Cosmetics brands audit retail partners to ensure adherence to Minimum Advertised Price policies.
Consultancies aggregate review sentiment and pricing tiers to map the competitive landscape of the Middle Eastern beauty market.
"Faces.com holds a critical dataset for Middle Eastern and global beauty trends, but mapping its shade variations and fragrance notes requires dedicated infrastructure."
Extracting cosmetics data means dealing with multi-dimensional product variants, nested ingredient lists, and flash sales. DataFlirt manages the residential proxies, JavaScript execution, and schema normalisation so your data science team receives clean, queryable tables on schedule.
Everything supported by our faces.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 interaction flows.
We maintain pools of residential ISP proxies across target 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 faces.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We map every shade variant to its parent product ID, capturing the specific hex code, colour family, price, and stock status for each individual shade.
We route our crawlers through geo-specific residential proxies (e.g., UAE, KSA). This ensures we capture the correct local currency, pricing, and catalogue availability for your target region.
We extract the raw ingredient text block provided by the brand on the product page. Depending on your requirements, we can apply post-processing to split this text into a structured array of individual ingredients.
We can configure pipelines to run daily, weekly, or at custom intervals. For critical SKUs, we can implement high-frequency checks to monitor flash sales or rapid stock depletion.
Yes. When reviewers provide metadata such as skin type, skin tone, or age range, we extract these fields alongside the star rating and review text.
Absolutely. We provide a sample extraction of up to 500 products so you can validate the schema, variant mapping, and data quality before committing to a production pipeline.
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 80,000 SKUs — we scope, build, and operate the pipeline.
