We extract furniture listings, dimension specs, material details, pricing signals, and brand intelligence from Pepperfry. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Furniture Listings objects from pepperfry.com. All fields typed and schema-versioned.
"sku": "FNT12345", "name": "Mintwud Yoshi Engineered Wood Study Table", "brand": "Mintwud", "category": "Furniture", "sub_category": "Study Tables", "price": 4599.0, "mrp": 8999.0, "discount_pct": 48, "stock_status": "In Stock"
| # | sku | name | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Offers objects from pepperfry.com. All fields typed and schema-versioned.
"sku": "FNT12345", "price": 4599.0, "mrp": 8999.0, "discount_pct": 48, "coupon_code": "PEP15", "emi_options": true, "bank_offers": "10% Instant Discount on HDFC Cards", "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | price | mrp | discount_pct | coupon_code | emi_options |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Specs & Dimensions objects from pepperfry.com. All fields typed and schema-versioned.
"sku": "FNT12345", "height_inch": 29.5, "width_inch": 47.2, "depth_inch": 23.6, "weight_kg": 24.5, "primary_material": "Engineered Wood", "finish": "Walnut", "colour": "Brown", "warranty_months": 12
| # | sku | height_inch | width_inch | depth_inch | weight_kg | primary_material |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Delivery & Assembly objects from pepperfry.com. All fields typed and schema-versioned.
"sku": "FNT12345", "pincode": "560034", "delivery_days": 4, "shipping_cost": 0.0, "assembly_offered": true, "assembly_cost": 499.0, "return_window_days": 7, "cod_available": false
| # | sku | pincode | delivery_days | shipping_cost | assembly_offered | assembly_cost |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from pepperfry.com. All fields typed and schema-versioned.
"review_id": "REV987654", "sku": "FNT12345", "rating": 4.5, "reviewer_name": "Rahul S.", "review_date": "2026-04-18", "review_text": "Sturdy table, assembly was quick.", "verified_buyer": true, "helpful_votes": 12
| # | review_id | sku | rating | reviewer_name | review_date | review_text |
|---|---|---|---|---|---|---|
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Our Pepperfry scraper processes the entire home and furniture catalogue: deep material specs, dimension matrices, dynamic pricing, and pincode-specific delivery SLAs - bypassing bot protection.
Extract product names, descriptions, categories, and sub-categories across the entire Pepperfry taxonomy.
Capture exact height, width, depth, primary material, finish, and colour data normalised into structured fields.
Monitor MRP, selling price, discount percentages, and active coupon codes timestamped per crawl.
Inject specific pincodes to extract accurate delivery timelines, shipping costs, and COD availability per region.
Track private labels like Woodsworth and Mintwud alongside third-party merchants selling on the platform.
Paginate through customer reviews to capture text, star ratings, verified buyer badges, and helpful vote counts.
Determine if carpenter assembly is required, the associated service cost, and the exact warranty duration.
Monitor how products rank within specific category pages and search results over time.
Track inventory availability and detect stock-out events across the catalogue.
Extract high-resolution image URLs, lifestyle shots, and dimension diagram assets.
Brief in. Clean data out.
Provide category URLs, brand names, or specific SKU lists. We design the extraction schema together.
We configure Scrapy crawlers, Playwright for pincode injection, and proxy rotation for pepperfry.com.
Schema validation, null-rate checks, and dimension parsing accuracy checks before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting structured data from heavy e-commerce DOMs requires rendering and proxy management. Here is how our infrastructure maintains stability.
E-commerce sites deploy bot detection to prevent price scraping. Our crawlers use Indian residential ISP proxies with realistic browser fingerprints to ensure uninterrupted extraction.
Delivery times and shipping costs on Pepperfry require setting a session pincode. We use Playwright to inject pincodes and maintain cookie state across requests to accurately map regional SLAs.
Furniture specifications are often nested in complex HTML tables. We use strict XPath and CSS selector chains to reliably parse dimensions, materials, and warranty data into a flat schema.
For daily price monitoring, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing storage bloat and downstream processing load.
Every run emits structured logs. We alert on null-rate spikes in critical fields like price or dimensions and repair selectors before you notice missing data.
Furniture retailers monitor Pepperfry pricing, discount cycles, and coupon codes to optimise their own pricing strategies.
Category managers track catalogue depth across sub-categories to identify whitespace and new product opportunities.
Brands analyse the performance and pricing of Pepperfry house brands like Woodsworth and Mintwud.
Logistics teams extract pincode-level delivery SLAs to benchmark their own fulfillment speeds against industry leaders.
Analysts track the prevalence of specific materials, finishes, and colours to forecast upcoming interior design trends.
Manufacturers audit third-party sellers on the marketplace for MAP violations and inaccurate product representations.
"Pepperfry holds the definitive dataset for Indian furniture retail - extracting its dimension matrices and pricing signals requires dedicated pipeline architecture."
Most engineering teams underestimate the cost of maintaining e-commerce scrapers. Reliable Pepperfry extraction requires residential proxies, full JavaScript rendering for pincode delivery checks, and daily selector maintenance. DataFlirt absorbs this operational overhead so your team can focus entirely on data modelling and analysis.
Everything supported by our pepperfry.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 retry logic. Playwright handles JavaScript rendering and pincode cookie sessions. Combined via scrapy-playwright middleware.
We maintain pools of Indian residential ISP proxies. Rotation happens per-request with sticky sessions where required for delivery SLA extraction.
Pipelines run on AWS Lambda and ECS. 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 pepperfry.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Pepperfry is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use Indian residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes do not break the pipeline.
Yes. We can inject a list of target pincodes into the session state to extract precise delivery estimates, shipping costs, and assembly availability for each region.
Yes. We extract the raw dimension strings and normalise them into structured fields for height, width, and depth in inches or millimetres, making the data immediately queryable.
Pipelines can be configured for daily or hourly refreshes depending on your requirements. For large catalogues, daily refreshes complete within a 4-8 hour window.
Our smallest packages start at a defined SKU list or specific category extraction with weekly delivery. For full catalogue tracking, 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 so you can validate schema fit and field completeness.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price-monitoring across the entire furniture assortment - we scope, build, and operate the pipeline. Tell us what you need.
