We extract product listings, pricing signals, sizing availability, and brand collections from Liberty Shoes. 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 libertyshoes.com. All fields typed and schema-versioned.
"sku": "F10-M-8921", "title": "Force 10 Sports Running Shoes", "brand": "Force 10", "price": 1499.0, "list_price": 1999.0, "discount_pct": 25, "material": "Mesh", "category": "Men > Sports"
| # | sku | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Stock objects from libertyshoes.com. All fields typed and schema-versioned.
"sku": "F10-M-8921", "price": 1499.0, "list_price": 1999.0, "discount_pct": 25, "in_stock": true, "sale_badge": "Sale", "currency": "INR", "scraped_at": "2026-05-12T09:14:00Z"
| # | sku | price | list_price | discount_pct | in_stock | stock_status_by_size |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Collections & Brands objects from libertyshoes.com. All fields typed and schema-versioned.
"sku": "SEN-W-441", "brand_name": "Senorita", "collection_name": "Festive Wear", "gender": "Women", "season": "Autumn/Winter", "launch_year": 2025, "target_demographic": "Adult"
| # | sku | brand_name | collection_name | gender | season | launch_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Locator objects from libertyshoes.com. All fields typed and schema-versioned.
"store_id": "LS-DEL-042", "store_name": "Liberty Exclusive Showroom", "city": "New Delhi", "state": "Delhi", "pincode": "110001", "latitude": 28.6324, "longitude": 77.2188, "store_type": "Company Owned"
| # | store_id | store_name | address | city | state | pincode |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search & Categorisation objects from libertyshoes.com. All fields typed and schema-versioned.
"keyword": "school shoes", "position": 3, "sku": "AHA-K-102", "title": "AHA Black School Shoes", "price": 799.0, "category_path": "Kids > School Shoes", "sort_order": "Relevance", "scraped_at": "2026-05-12T09:15:22Z"
| # | keyword | position | sku | title | price | category_path |
|---|---|---|---|---|---|---|
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Our pipeline handles every layer of the libertyshoes.com platform: storefront listings, dynamic pricing, sub-brand categorisation, and sizing matrices.
SKU, title, description, material specs, and care instructions scraped across all footwear categories.
Track availability across UK/IND sizing standards per colour variant for every shoe.
Isolate data for Force 10, Senorita, AHA, Coolers, and Healers collections automatically.
Capture MRP, discounted price, and promotional badges timestamped per crawl.
Extract primary and gallery image URLs at maximum resolution for visual analysis.
Scrape physical store locations, coordinates, and contact details from the retail footprint map.
Monitor out-of-stock flags at the SKU and size level to track inventory depletion.
Reconstruct the exact breadcrumb path from Men/Women to specific footwear types.
Only receive records where price or stock has changed since the last run to minimise compute costs.
Brief in. Clean data out.
Provide target categories, sub-brands, or full catalogue requirements. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for libertyshoes.com.
Schema validation, null-rate checks, and variant mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Footwear extraction requires precise variant mapping and stock tracking. Here is how we ensure data accuracy.
Footwear data is inherently multi-dimensional. We map every colour and size combination back to the parent SKU, ensuring accurate stock and price tracking per variant.
Stock availability by size often requires JavaScript execution to trigger API calls. We use Playwright to hydrate the DOM and capture true availability.
For large catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing downstream processing load.
Retail sites change their DOM structure frequently. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and coverage drops before you notice.
Footwear retailers monitor Liberty's pricing and discount strategies to adjust their own promotional calendars.
Analyse sub-brand distribution and category depth across the catalogue to identify market trends.
Track out-of-stock rates to infer demand for specific sizes and styles in the Indian market.
Map physical store locations for geospatial market research and expansion planning.
Extract material, sole type, and closure specifications for trend analysis and product development.
Ensure third-party sellers adhere to Liberty's minimum advertised pricing guidelines.
"Liberty Shoes maintains a massive catalogue of regional footwear preferences and pricing strategies, providing critical signals for the Indian retail market."
Extracting footwear data requires precise handling of multi-dimensional variants. Colour, size, and stock availability change constantly. DataFlirt manages the proxy rotation, JavaScript rendering, and schema normalisation so your data engineering team can focus on downstream analytics instead of pipeline maintenance.
Everything supported by our libertyshoes.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 and interaction flows for sizing grids.
We maintain pools of residential ISP proxies across IN regions. Rotation happens per-request to ensure high success rates.
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 libertyshoes.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public, non-authenticated product and pricing data. We do not extract personal data or circumvent authentication walls.
We map every colour and size combination back to the parent SKU, creating a matrix of availability and pricing for each specific variant.
Yes. We can configure the pipeline to target specific sub-brands like Force 10, Senorita, or AHA, or extract the entire catalogue.
Pipelines can be configured to run at hourly or daily cadences depending on your requirement for stock accuracy.
Yes. We extract geospatial data, operating hours, and contact details for all physical Liberty Shoes locations listed on the site.
Our packages start at a defined category list with weekly delivery. For larger requirements, 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.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed, we scope, build, and operate the pipeline. Tell us what you need.
