We extract footwear listings, pricing signals, size availability matrices, and physical store inventory from khadims.com. 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 khadims.com. All fields typed and schema-versioned.
"sku": "KHD-M-54321", "title": "Khadim Men Brown Formal Slip-On Shoes", "brand": "Khadim", "category": "Men", "price": 1499.0, "mrp": 2199.0, "discount_pct": 31, "colour": "Brown", "sizes_available": "['6', '7', '8', '9', '10']"
| # | sku | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Inventory objects from khadims.com. All fields typed and schema-versioned.
"sku": "KHD-M-54321", "price": 1499.0, "mrp": 2199.0, "discount_pct": 31, "in_stock": true, "available_sizes": "['6', '7', '8', '9', '10']", "out_of_stock_sizes": "['11', '12']", "promotion_badge": "End of Season Sale", "price_timestamp": "2023-10-27T08:15:00Z"
| # | sku | price | mrp | discount_pct | discount_abs | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Product Specifications objects from khadims.com. All fields typed and schema-versioned.
"sku": "KHD-M-54321", "outer_material": "Synthetic Leather", "inner_material": "Textile", "sole_material": "PU", "closure": "Slip-on", "shoe_type": "Formal", "heel_height": "Low", "occasion": "Office/Formal", "care_instructions": "Wipe with a clean, dry cloth"
| # | sku | outer_material | inner_material | sole_material | closure | shoe_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Store Locations objects from khadims.com. All fields typed and schema-versioned.
"store_id": "STR-KA-042", "store_name": "Khadim's - Indiranagar", "city": "Bengaluru", "state": "Karnataka", "pincode": "560038", "phone": "+91-80-12345678", "latitude": 12.9783, "longitude": 77.6408, "operating_hours": "10:30 AM - 09:30 PM"
| # | store_id | store_name | address | city | state | pincode |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Categories objects from khadims.com. All fields typed and schema-versioned.
"category_id": "CAT-MEN-FORMAL", "name": "Men's Formal Shoes", "parent_category": "Men", "url": "https://www.khadims.com/category/men-formal", "product_count": 342, "meta_title": "Buy Men's Formal Shoes Online | Khadims", "scraped_at": "2023-10-27T08:15:33Z"
| # | category_id | name | parent_category | url | product_count | banner_image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Khadims scraper targets the specific complexities of footwear retail: size availability matrices, colour variant grouping, specification tables, and physical store locators.
Capture every SKU across Men, Women, Kids, and Accessories categories, including title, description, and high-resolution image URLs.
Extract size availability per SKU in real time. Differentiate between in-stock, low-stock, and out-of-stock sizes for accurate inventory tracking.
Monitor Selling Price against MRP. Capture discount percentages, promotional badges, and flash sale pricing.
Map parent-child relationships for shoes available in multiple colours, ensuring accurate cross-referencing of styles.
Parse unstructured specification tables into clean JSON fields: sole material, upper material, closure type, and occasion.
Extract details of all physical retail outlets nationwide, including address, coordinates, phone numbers, and operating hours.
Configure pipelines to run daily, outputting only the SKUs where price or size availability has changed since the last run.
Collect primary and secondary product image URLs, normalising them for ingestion into your own PIM or analytics dashboard.
Traverse the entire site navigation tree to maintain accurate taxonomy data, from root categories down to specific sub-segments.
Brief in. Clean data out.
Specify categories, specific SKUs, or the entire catalogue. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, handle pagination, and manage proxy rotation for khadims.com.
Schema validation, null-rate checks, and size-matrix testing before full pipeline deployment.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Footwear retail sites present unique scraping challenges, particularly around dynamic inventory and variant mapping. Here is how we ensure data accuracy.
Size availability and price updates on khadims.com are often hydrated via JavaScript after page load. We use Playwright to execute JS and interact with size selectors, ensuring we capture the true stock state rather than stale HTML.
A single shoe style may exist in multiple colours, sometimes as separate URLs and sometimes as dynamic toggles. Our extraction logic maps these relationships accurately, grouping variants under a single parent identifier.
To extract the entire catalogue without triggering IP blocks or getting cached responses, we route requests through Indian residential proxies, maintaining low concurrency and human-like request patterns.
Product specifications (material, closure, care instructions) often vary in format across different categories. Our pipeline normalises these tables into consistent, strongly-typed JSON fields.
Instead of scraping complex mapping interfaces, we intercept the underlying API calls powering the Khadims store locator to extract precise geographic coordinates and store metadata directly.
Footwear brands track Khadims' MRP and discount strategies across categories to optimise their own promotional calendars.
Retail buyers analyse category depth, colour availability, and material trends to inform seasonal purchasing decisions.
Analysts monitor size-level stockouts to identify supply chain constraints and popular size distributions in the Indian market.
Real estate and expansion teams extract store locator data to map geographic presence and identify underserved retail catchments.
Aggregators compare Khadims' direct-to-consumer pricing against their listings on third-party platforms like Myntra and Amazon.
Product teams track the distribution of materials (PU, leather, synthetic) and styles across the catalogue to identify market shifts.
"Footwear retail moves on size availability and discount cycles. You cannot optimise pricing without knowing precisely which SKUs are out of stock on your competitor's site."
Scraping footwear catalogues requires handling complex size-colour matrices and dynamic inventory states. DataFlirt manages the JavaScript execution and proxy rotation required to track thousands of SKUs across khadims.com, delivering clean, normalised datasets so your analytics team can focus on strategy rather than pipeline maintenance.
Everything supported by our khadims.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 and colour selectors.
We maintain pools of Indian residential ISP proxies to avoid geographic blocking and rate limits during full catalogue extracts.
Pipelines run on AWS Lambda and ECS. Airflow handles daily scheduling and diff computation. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About khadims.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline iterates through the available size options on the product page, recording exactly which sizes are in stock and which are sold out at the time of the scrape.
For the entire catalogue, we typically recommend daily runs. For specific high-priority categories or SKUs, we can configure hourly pipelines to track fast-moving inventory and flash sales.
Yes. We extract the complete list of Khadims retail outlets via their store locator, including addresses, PIN codes, operating hours, and latitude/longitude coordinates.
We map parent-child relationships. If a shoe has three colour variants, we extract each as a distinct record but link them with a shared parent ID to maintain catalogue integrity.
Scraping publicly available product, pricing, and store data is generally permissible. DataFlirt extracts only public, non-authenticated information. We do not access user accounts, cart data, or loyalty programs.
We begin accumulating historical data from the day your pipeline is commissioned. Every run is timestamped, allowing you to build a time-series database of price changes over time.
We deliver in JSON, CSV, XLS, and Parquet. We can push directly to AWS S3, Snowflake, BigQuery, or trigger Webhooks for real-time integration.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off store location export or continuous price and size monitoring across the entire catalogue — we scope, build, and operate the pipeline. Tell us what you need.
