We extract product listings, sizing availability, MRP pricing, discounts, and store locator data from Relaxo Footwear. 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 relaxofootwear.com. All fields typed and schema-versioned.
"sku": "SM-648", "title": "Sparx Men's Running Shoes", "brand": "Sparx", "category": "Men > Sports Shoes", "mrp": 1299.0, "selling_price": 1099.0, "discount_percentage": 15, "currency": "INR", "average_rating": 4.2
| # | sku | title | brand | category | sub_category | mrp |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Variants objects from relaxofootwear.com. All fields typed and schema-versioned.
"parent_sku": "SM-648", "variant_sku": "SM-648-BLU-8", "colour": "Navy Blue", "size_uk": "8", "mrp": 1299.0, "selling_price": 1099.0, "stock_status": "In Stock", "pincode_checked": "110001"
| # | parent_sku | variant_sku | colour | size_uk | mrp | selling_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Locator objects from relaxofootwear.com. All fields typed and schema-versioned.
"store_id": "RLX-DEL-042", "store_name": "Relaxo Exclusive Outlet - Connaught Place", "city": "New Delhi", "state": "Delhi", "pincode": "110001", "latitude": 28.6315, "longitude": 77.2167, "store_type": "Exclusive"
| # | store_id | store_name | address_line_1 | address_line_2 | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Navigation objects from relaxofootwear.com. All fields typed and schema-versioned.
"category_id": "cat_mens_sports", "category_name": "Sports Shoes", "parent_category": "Men", "breadcrumb": "Home > Men > Sports Shoes", "url": "https://www.relaxofootwear.com/men-sports-shoes", "product_count": 412, "is_active": true
| # | category_id | category_name | parent_category | breadcrumb | url | product_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Specifications objects from relaxofootwear.com. All fields typed and schema-versioned.
"sku": "SM-648", "upper_material": "Mesh", "sole_material": "EVA", "closure_type": "Lace-Up", "toe_shape": "Round", "warranty_days": 30, "country_of_origin": "India", "net_quantity": "1 Pair"
| # | sku | upper_material | sole_material | closure_type | toe_shape | fit_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our scraper handles the entire Relaxo Footwear domain: Sparx, Flite, Bahamas, and School Shoes. We extract nested variants, dynamic pricing, and geographical store data with robust error handling.
Extract every shoe across all sub-brands (Sparx, Flite, Bahamas, Boston) including descriptions, images, and category paths.
Map parent products to child variants by size and colour, capturing specific availability states for each combination.
Track MRP versus current selling price, calculating absolute and percentage discounts per variant.
Scrape retail store addresses, contact numbers, and GPS coordinates from the Relaxo store locator directory.
Simulate pincode entry to extract region-specific delivery estimates and stock availability.
Extract structured technical details including sole material, upper material, closure type, and warranty terms.
Map exact breadcrumb trails and category navigation trees to understand site architecture and assortment.
Maintain hash indexes of product records to deliver clean diffs, isolating new launches and price drops.
Configure continuous pipelines at daily or weekly cadences to sync Relaxo catalogue changes to your warehouse.
Brief in. Clean data out.
Provide categories, brands (e.g., Sparx), or specific store regions. We design the extraction schema together.
We configure Scrapy crawlers, session management, and pincode simulation logic for relaxofootwear.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.
eCommerce sites rely on dynamic rendering for variants and stock. Here is how we ensure complete data capture.
Footwear variants often load asynchronously. We use Playwright to execute JavaScript and trigger size/colour selection events, capturing the exact stock status and price for every combination.
Delivery estimates and inventory can vary by region. Our crawlers maintain session cookies and inject specific Indian pincodes to normalise data extraction across geographical zones.
Category pages may use infinite scroll or dynamic API endpoints. We intercept background XHR requests to extract structured product JSON directly, bypassing fragile DOM parsing where possible.
Store locator pages embed coordinates in map initialization scripts. We parse these inline scripts to extract exact latitude and longitude values for retail mapping.
We alert on sudden drops in total SKU count or abnormal discount percentages, ensuring site layout changes do not silently corrupt your downstream analytics.
Rival footwear brands track Sparx and Flite pricing strategies, discount depths, and promotional periods to adjust their own retail pricing.
Retail analysts map category depth, size availability curves, and colour variations to understand Relaxo's inventory strategy.
Real estate and retail expansion teams use scraped store locator data to plot Relaxo's physical footprint and identify catchment areas.
Distributors and channel partners track online MSRP compliance and official stock availability across specific pincodes.
Supply chain models ingest out-of-stock signals across specific size/colour variants to estimate consumer demand patterns.
Analysts track new product launches, material trends, and category expansion within the Indian mass-market footwear segment.
"Relaxo's multi-brand catalogue represents a massive segment of Indian footwear retail — but tracking stock depth across Sparx, Flite, and Bahamas requires dedicated pipeline infrastructure."
Most teams underestimate the investment required: reliable retail scraping requires residential proxies, dynamic variant rendering, regional pincode session handling, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our relaxofootwear.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. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across IN/US/UK/DE regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 relaxofootwear.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from retail websites is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and store locator data. We do not extract personal user data or circumvent authentication walls.
Yes. We map every size and colour combination back to the parent SKU, extracting the specific price and stock status for each individual variant.
We configure our crawlers to inject specific Indian pincodes during the session, allowing us to capture region-specific stock availability and delivery estimates.
Yes. We extract the complete list of physical retail locations, including addresses, contact numbers, and latitude/longitude coordinates.
Pipelines can be scheduled at daily, weekly, or custom intervals. We use change-detection logic to push only the records that have been updated since the previous run.
Absolutely. We provide a sample run of up to 500 SKUs or specific category pages 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 a continuous price-monitoring feed across all variants — we scope, build, and operate the pipeline. Tell us what you need.
