We extract product catalogues, size grids, pricing signals, and fabric data from Lindex. 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 Information objects from lindex.com. All fields typed and schema-versioned.
"product_id": "8371940", "article_number": "8371940-0909", "title": "Ribbed long sleeve top", "department": "Womenswear", "category": "Tops", "sub_category": "Long sleeve tops", "fit_type": "Slim fit", "scraped_at": "2026-05-12T09:14:00Z"
| # | product_id | article_number | title | brand | department | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Stock objects from lindex.com. All fields typed and schema-versioned.
"product_id": "8371940", "price": 19.99, "original_price": 24.99, "currency": "EUR", "discount_pct": 20, "on_sale": true, "in_stock": true, "available_sizes": "['XS', 'S', 'M', 'L']", "out_of_stock_sizes": "['XL', 'XXL']"
| # | product_id | price | original_price | currency | discount_pct | on_sale |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants & Colours objects from lindex.com. All fields typed and schema-versioned.
"product_id": "8371940", "colour_name": "Dusty Pink", "colour_code": "0909", "is_primary_variant": true, "primary_image_url": "https://lindex.com/media/images/8371940_front.jpg", "gallery_image_urls": "['https://lindex.com/media/images/8371940_back.jpg']", "variant_url": "https://lindex.com/eu/p/8371940-dusty-pink"
| # | product_id | parent_article_number | colour_name | colour_code | variant_url | primary_image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Materials & Care objects from lindex.com. All fields typed and schema-versioned.
"product_id": "8371940", "material_composition": "95% Cotton, 5% Elastane", "sustainability_label": "Better Cotton Initiative", "organic_cotton_pct": 95, "wash_temperature": "40 degrees machine wash", "ironing_instructions": "Iron medium heat", "tumble_dry_safe": false
| # | product_id | material_composition | lining_composition | sustainability_label | organic_cotton_pct | recycled_material_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from lindex.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "product_id": "8371940", "star_rating": 4, "review_title": "Great everyday basic", "fit_feedback": "True to size", "quality_rating": 5, "review_date": "2026-04-18"
| # | review_id | product_id | reviewer_nickname | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our pipeline extracts every data point from Lindex: complex size grids, regional pricing variations, high-resolution imagery, and sustainability metadata.
Extract womenswear, kidswear, lingerie, and cosmetics categories with complete hierarchy mapping.
Track stock availability for every specific size variant, capturing low stock warnings and out-of-stock statuses.
Capture localized pricing across EU, UK, and Nordic storefronts in native currencies.
Extract material composition, organic cotton percentages, and specific sustainability certifications.
Group parent products with all available colour variants and their respective SKUs.
Extract high-resolution URLs for product flats, model shots, and fabric detail images.
Monitor active sales, percentage discounts, and multi-buy promotional offers.
Parse wash temperatures, ironing rules, and material handling guidelines for every garment.
Receive only updated records for price changes and stock movements to optimise warehouse storage.
Brief in. Clean data out.
Provide target categories, regions, or specific product lines. We design the extraction schema together.
We configure Scrapy crawlers, handle regional routing, and manage Lindex's dynamic content loading.
Schema validation, null-rate checks, and size-grid integrity testing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting accurate fashion data requires handling dynamic size grids, geo-blocking, and localized catalogues. Here is how we build resilient pipelines.
Lindex loads size availability and stock levels dynamically via frontend APIs. We intercept these network requests or parse the hydrated JSON state to guarantee accurate size-level inventory data.
Lindex enforces strict geo-redirects based on IP. We utilise targeted residential proxies in specific European countries to scrape accurate local pricing and localized inventory without being redirected.
Fashion catalogues often separate colour variants into unique URLs. Our pipeline maps these individual article numbers back to a unified parent product record for clean database insertion.
We construct the highest resolution image URLs from Lindex's CDN parameters, ensuring your visual search models or competitor analysis tools receive the best possible image quality.
Fashion stock moves quickly. We hash the size and price arrays per SKU. Subsequent runs only emit records when a size drops out of stock or a price changes, reducing your processing compute.
Fashion retailers track Lindex pricing, discount depth, and promotional calendars to adjust their own markdown strategies.
Merchandising teams analyse category breadth, colour distribution, and new product launch velocity to benchmark their own collections.
Analysts track which styles, colours, and fits sell out fastest at the size level to predict upcoming seasonal trends.
ESG analysts track the percentage of organic cotton and recycled materials in Lindex collections over time.
Machine learning teams ingest high-resolution garment images and clean category metadata to train visual search and auto-tagging models.
Supply chain analysts monitor out-of-stock rates across specific categories to gauge supply chain health and consumer demand.
"Lindex holds critical signals for European fashion trends, pricing strategies, and sustainable material adoption but requires a dedicated pipeline to query."
Most teams underestimate the investment required for fashion scraping. Extracting reliable size-level stock data, regional pricing, and high-resolution image assets requires residential proxies, full JavaScript rendering, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on analysis.
Everything supported by our lindex.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 for dynamic size grids and localized content.
We route requests through region-specific residential IPs to bypass Lindex geo-redirects and capture accurate local pricing.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About lindex.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We use localized residential proxies to access specific regional versions of Lindex (e.g., Sweden, UK, EU) to extract accurate local pricing, currency, and regional stock availability.
Our pipeline captures stock status at the size level. We map which specific sizes are available, low in stock, or completely sold out for every colour variant.
Yes. We bypass the compressed thumbnail URLs and construct the parameters required to extract the highest resolution images directly from the Lindex CDN.
Yes. We run daily or weekly pipelines and use hash-based diffing to identify when an item goes on sale, when a discount percentage changes, or when the original price is modified.
Yes. We parse the raw text descriptions into structured fields, allowing you to query specific percentages of organic cotton, recycled polyester, or elastane across the catalogue.
Yes. We provide a sample run of up to 500 products as part of the pre-engagement scoping process so you can 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 all categories, we scope, build, and operate the pipeline. Tell us what you need.
