We extract product listings, pricing signals, fabric details, size availability, and category hierarchies from Biba. 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 biba.in. All fields typed and schema-versioned.
"sku": "BIBA_SKU_84920", "title": "Indigo Cotton Straight Kurta", "brand": "Biba", "category": "Clothing", "sub_category": "Kurtas", "colour": "Indigo", "product_type": "Straight Fit"
| # | sku | title | brand | category | sub_category | colour |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Offers objects from biba.in. All fields typed and schema-versioned.
"sku": "BIBA_SKU_84920", "mrp": 2999.0, "selling_price": 1499.0, "discount_pct": 50, "is_on_sale": true, "currency": "INR", "tax_included": true
| # | sku | mrp | selling_price | discount_pct | is_on_sale | sale_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fabric & Details objects from biba.in. All fields typed and schema-versioned.
"sku": "BIBA_SKU_84920", "top_fabric": "100% Cotton", "lining_material": "Cotton", "pattern": "Floral Print", "wash_care": "Machine Wash Cold", "neck_type": "Round Neck", "sleeve_length": "Three Quarter"
| # | sku | top_fabric | bottom_fabric | dupatta_fabric | lining_material | weave_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Size & Inventory objects from biba.in. All fields typed and schema-versioned.
"sku": "BIBA_SKU_84920", "size_label": "M", "in_stock": true, "stock_quantity": 42, "chest_inches": 38.0, "waist_inches": 30.0, "length_inches": 44.0
| # | sku | size_label | in_stock | stock_quantity | chest_inches | waist_inches |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Category & Hierarchy objects from biba.in. All fields typed and schema-versioned.
"sku": "BIBA_SKU_84920", "parent_category": "Women", "collection_name": "Summer Symphony", "occasion": "Casual Wear", "fit": "Straight", "season": "Summer", "new_arrival": false
| # | sku | breadcrumb | parent_category | collection_name | occasion | fit |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Biba scraper handles the complexities of fashion retail platforms: dynamic size grids, promotional overlays, infinite scroll categories, and nested variant mapping.
Title, description, colour, fit, and every metadata field Biba surfaces, extracted at the SKU level with parent-child variant mapping.
Capture MRP, selling price, discount percentages, and active sale events, timestamped per crawl.
Extract available sizes, stock status, and exact measurements from Biba size charts for every garment.
Isolate fabric composition for tops, bottoms, and dupattas, along with wash care instructions and weave types.
Extract all product image URLs, bypassing lazy-loading mechanisms to ensure complete visual datasets.
Map the exact breadcrumb trails, collections, and occasion tags to maintain accurate taxonomy.
Monitor flash sales, end-of-season discounts, and promotional banners across the entire site.
Run continuous pipelines at daily or real-time cadences with change-detection diffing to monitor stock drops.
Extract physical store locations, operating hours, and contact details from the Biba store directory.
Brief in. Clean data out.
Provide category URLs, specific collections, or full-site requirements. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for biba.in.
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.
Fashion e-commerce sites use dynamic loading and complex variant structures. Here is how we maintain data integrity.
Biba loads size availability and stock status dynamically via client-side scripts. We use Playwright to execute these scripts and capture the exact stock state for every size variant.
Product images are often deferred until user scroll. Our crawlers simulate human scrolling behaviour to trigger lazy-loading and capture all high-resolution image URLs.
Site-wide sales often obscure base pricing with temporary overlays. We parse the underlying JSON state to extract both the original MRP and the current promotional price accurately.
Category pages use infinite scrolling rather than traditional pagination. We intercept the backend API calls to ensure no products are missed during category sweeps.
Apparel SKUs often share a parent identifier but differ by colour and size. Our schema normalises these relationships, providing a flat, queryable table of all possible combinations.
Retail brands track Biba pricing, discount depth, and sale events to optimise their own promotional calendars.
Merchandisers analyse category depth, colour prevalence, and fabric choices to inform seasonal buying decisions.
Fashion analysts track new arrivals and stock depletion rates to identify emerging ethnic wear trends.
Pricing teams correlate discount percentages with stock movement to build predictive markdown models.
Machine learning teams use high-resolution garment images and metadata to train computer vision models for fashion.
Analysts track SKU counts across categories to estimate Biba market penetration in specific apparel segments.
"Biba represents a massive dataset of Indian ethnic wear trends, pricing structures, and fabric preferences. Extracting this requires a pipeline built for complex apparel taxonomy."
Fashion retail moves fast. Scraping biba.in requires handling dynamic size grids, promotional overlays, and nested category structures. DataFlirt manages the proxy rotation, JavaScript execution, and schema parsing so your analytics team receives clean, normalised apparel data ready for immediate querying.
Everything supported by our biba.in 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, cookie sessions, and infinite scroll interactions.
We maintain pools of residential ISP proxies across Indian regions. Rotation happens per-request to prevent rate limiting and IP blocks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State is stored in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About biba.in scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from biba.in is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and category data. We do not extract personal user data or circumvent authentication walls.
We execute the client-side JavaScript that populates the size grids, capturing the exact in-stock status and available quantities for every size variant.
Yes. Our schema maps parent products to all available child variants, ensuring every size and colour combination is captured as a distinct, queryable record.
A full catalogue sweep typically completes within 4 to 6 hours, depending on concurrency limits set to respect target server load.
Yes. We bypass lazy-loading mechanisms to extract the source URLs for all high-resolution product images, including alternate angles and detail shots.
Our pipelines parse the underlying JSON state to extract both the base MRP and the active promotional price, ensuring accurate discount calculations even during site-wide sales events.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous price monitoring across all categories, we scope, build, and operate the pipeline. Tell us what you need.
