We extract product catalogues, regional pricing, size-level stock availability, and trend collections from Bershka. 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 Metadata objects from bershka.com. All fields typed and schema-versioned.
"product_id": "04561332800", "reference_number": "4561/332/800", "name": "Faux leather oversized biker jacket", "category": "Jackets", "subcategory": "Biker", "colour_name": "Black", "colour_code": "800", "gender": "Women"
| # | product_id | reference_number | name | category | subcategory | colour_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sizing & Inventory objects from bershka.com. All fields typed and schema-versioned.
"product_id": "04561332800", "size_name": "M", "size_id": "103", "in_stock": true, "low_stock_warning": true, "backorder_eligible": false, "stock_timestamp": "2026-05-12T09:14:00Z", "region": "ES"
| # | product_id | size_name | size_id | in_stock | low_stock_warning | backorder_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promotions objects from bershka.com. All fields typed and schema-versioned.
"product_id": "04561332800", "current_price": 35.99, "original_price": 45.99, "discount_pct": 21, "currency": "EUR", "promo_name": "Mid Season Sale", "region": "ES", "price_timestamp": "2026-05-12T09:14:00Z"
| # | product_id | current_price | original_price | discount_pct | currency | promo_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Materials & Care objects from bershka.com. All fields typed and schema-versioned.
"product_id": "04561332800", "outer_shell_composition": "100% polyurethane", "lining_composition": "100% polyester", "care_wash": "Machine wash max. 30ºC short spin", "care_iron": "Do not iron", "care_bleach": "Do not use bleach", "sustainability_label": "Join Life", "recycled_content_pct": 25
| # | product_id | outer_shell_composition | lining_composition | care_wash | care_iron | care_dryclean |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Media & Assets objects from bershka.com. All fields typed and schema-versioned.
"product_id": "04561332800", "main_image_url": "https://static.bershka.net/4/photos2/2026/I/0/1/p/4561/332/800/4561332800_1_1_3.jpg", "gallery_urls": "['https://static.bershka.net/4/photos2/2026/I/0/1/p/4561/332/800/4561332800_2_1_3.jpg', 'https://static.bershka.net/4/photos2/2026/I/0/1/p/4561/332/800/4561332800_2_2_3.jpg']", "model_height_cm": 175, "model_wearing_size": "S", "asset_timestamp": "2026-05-12T09:14:00Z"
| # | product_id | main_image_url | gallery_urls | model_height_cm | model_wearing_size | video_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Bershka scraper navigates Inditex's complex frontend architecture, capturing exact size-level inventory, regional pricing variants, and high-resolution media assets without triggering edge protection.
Capture SKUs, reference numbers, descriptions, and category hierarchies across Men, Women, and BSK collections.
Monitor size-level availability, low stock warnings, and out-of-stock states to accurately gauge product velocity.
Extract geo-localised pricing, original prices, and promotional discounts across European, Asian, and American storefronts.
Parse main images, gallery arrays, and video URLs directly from Bershka's content delivery network.
Extract exact material composition percentages, care instructions, and sustainability labels for ESG compliance.
Map 'Complete the look' associations and related product recommendations to understand merchandising strategies.
Monitor seasonal sales, mid-season markdowns, and specific discount percentages applied at the SKU level.
Traverse specific regional category trees to identify assortment differences between markets.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide target categories, regional markets, or specific reference numbers. We design the extraction schema together.
We configure Playwright crawlers, GraphQL interception, proxy rotation, and session management for bershka.com.
Schema validation, null-rate checks, price-outlier detection, and size-matrix verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Inditex web properties utilise aggressive bot protection and complex single-page architectures. Here is how we maintain data flow.
Bershka employs edge-layer protection that flags data centre IPs and headless browsers. We route requests through residential ISP proxies with realistic browser fingerprints, matching the expected regional origin of the request.
Rather than scraping purely visual DOM elements which change frequently, we intercept the structured GraphQL responses powering Bershka's React frontend. This yields cleaner, faster, and more reliable data extraction.
Stock and pricing vary drastically between Spain, the UK, and Mexico. Our session management isolates regional cookies and headers, ensuring the data reflects the exact market you intend to analyse.
Fast fashion inventory moves quickly. We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift in the GraphQL API, and coverage drops. SLA uptime is contractual.
Fashion retailers track Bershka's pricing strategies, discount depths, and promotional calendars to optimise their own markdowns.
Merchandising teams analyse category breadth, colour prevalence, and material usage to identify emerging fast fashion trends.
Analysts monitor size-level stock depletion rates to estimate sales velocity and identify best-performing SKUs.
Machine learning teams use Bershka's high-resolution product imagery and metadata to train computer vision models for apparel recognition.
Researchers aggregate fabric composition and 'Join Life' sustainability labels to audit environmental claims across the catalogue.
Marketplace sellers compare regional pricing matrices to identify arbitrage opportunities across different European and Asian markets.
"Fast fashion moves on hourly cycles. Extracting Bershka's catalogue requires tracking size-level stock volatility and regional pricing across 40+ markets simultaneously."
Scraping modern Inditex web properties involves navigating heavy single-page application architectures, complex GraphQL endpoints, and aggressive edge-layer bot protection. DataFlirt engineers abstract this complexity, delivering normalised stock and pricing feeds directly to your analytical warehouse.
Everything supported by our bershka.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.
Playwright handles the initial page load and cookie hydration, while custom middleware intercepts the subsequent GraphQL network requests containing the raw structured catalogue data.
We maintain pools of residential ISP proxies across European, Asian, and American regions. Rotation happens per-request to ensure the pricing and stock reflect the exact local market.
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 bershka.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Bershka is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and inventory data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review Inditex's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. By intercepting GraphQL endpoints, we minimise unnecessary DOM rendering while securing the underlying structured data.
We support all regional storefronts available on bershka.com, including ES, UK, US, MX, FR, DE, and IT. Market-specific data is captured by routing requests through corresponding local proxies and injecting the correct regional headers.
Real-time streaming pipelines can achieve sub-60-minute latency for stock availability signals on a defined SKU set. Full catalogue refreshes at daily cadence complete within a 4-8 hour window depending on the target region.
Yes. Our pipeline extracts the availability status for every individual size variant (e.g., XS, S, M, L, XL) associated with a product reference, including low stock warning indicators.
Our smallest packages start at a defined category list or SKU set with weekly delivery. For full multi-region catalogue extraction, we price based on volume, region count, and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 500 SKUs from a specific region as part of the pre-engagement scoping process, allowing you to 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 catalogue dump or a continuous inventory-monitoring feed across multiple markets, we scope, build, and operate the pipeline. Tell us what you need.
