We extract product specifications, material details, dimension matrices, pricing signals, and inventory status from Durian. 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 Specifications objects from durian.in. All fields typed and schema-versioned.
"sku": "DUR-SOF-00941", "title": "Winston 3 Seater Leather Sofa", "category": "Living Room", "material": "Genuine Leather", "finish": "Walnut", "dimensions": "2100mm x 900mm x 850mm", "warranty": "5 Years", "seating_capacity": 3
| # | sku | title | category | sub_category | material | finish |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from durian.in. All fields typed and schema-versioned.
"sku": "DUR-SOF-00941", "price": 84990.0, "list_price": 115000.0, "discount_pct": 26, "currency": "INR", "emi_options": "Starts at 4,001/month", "offer_tags": "['Festive Sale', 'Free Assembly']", "price_timestamp": "2023-11-04T10:15:00Z"
| # | sku | price | list_price | discount_pct | currency | emi_options |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Delivery objects from durian.in. All fields typed and schema-versioned.
"sku": "DUR-SOF-00941", "in_stock": true, "pincode_serviceable": true, "delivery_days": "7-10 Days", "assembly_required": "Carpenter Assembly", "return_policy": "7 Days Replacement", "dispatch_time": "48 Hours", "shipping_cost": 0.0
| # | sku | in_stock | stock_depth | delivery_days | assembly_required | pincode_serviceable |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Media & Assets objects from durian.in. All fields typed and schema-versioned.
"sku": "DUR-SOF-00941", "primary_image_url": "https://durian.in/media/catalog/winston-main.jpg", "gallery_urls": "['https://durian.in/media/catalog/winston-side.jpg', 'https://durian.in/media/catalog/winston-back.jpg']", "material_swatch_url": "https://durian.in/media/swatch/leather-brown.jpg", "manual_pdf": "None", "video_url": "https://youtube.com/watch?v=example"
| # | sku | primary_image_url | gallery_urls | video_url | ar_model_url | manual_pdf |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category Metadata objects from durian.in. All fields typed and schema-versioned.
"category_id": "CAT-LIV-01", "category_name": "Leather Sofas", "breadcrumb": "Home > Living Room > Sofas > Leather Sofas", "product_count": 142, "sorting_rank": 4, "seo_title": "Buy Premium Leather Sofas Online | Durian", "scraped_at": "2023-11-04T10:15:33Z"
| # | category_id | category_name | breadcrumb | product_count | filter_tags | sorting_rank |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Durian's catalogue relies heavily on structured specifications, high-resolution imagery, and dynamic availability checks. We handle the session state and DOM parsing required to extract clean product records.
Extract dimensions, weight, material composition, seating capacity, and care instructions into normalised, queryable fields.
Simulate user sessions across specific Indian pincodes to map delivery timelines, shipping costs, and regional inventory availability.
Capture base price, discounted price, EMI calculations, and promotional tags timestamped per crawl.
Scrape primary images, gallery assets, lifestyle shots, and material swatches at their highest available resolution.
Map parent-child relationships between product models and their respective colour, fabric, or wood finish variations.
Extract warranty terms, return policies, and assembly requirements (DIY vs Carpenter) for every SKU.
Maintain exact category hierarchies to distinguish between commercial office furniture and residential pieces.
Run daily diffs to identify new product launches, discontinued items, and price adjustments without processing the entire catalogue.
Process the entire 15,000+ SKU catalogue within minutes using distributed infrastructure and proxy rotation.
Brief in. Clean data out.
Provide target categories, required pincodes, and desired data fields. We design the extraction schema together.
We configure Scrapy crawlers, session management for location data, and structure parsers for durian.in.
Schema validation, null-rate checks, image URL verification, and dimension standardisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting structured data from modern eCommerce storefronts requires handling dynamic state and inconsistent DOM structures. Here is how we build resilient pipelines.
Durian alters delivery timelines and inventory status based on user location. We maintain strict cookie sessions and regional IP routing to simulate specific Indian pincodes, ensuring accurate regional data.
Furniture specifications often appear in unstructured HTML tables or bulleted lists. Our parsers use regex and text-pattern matching to extract dimensions (W x D x H) and materials into strict numeric and categorical fields.
Product pages load compressed images for performance. We intercept API responses and parse JSON state blocks to extract the original, high-resolution image URLs required for your downstream applications.
For large catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and storage bloat. You receive a clean changelog rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing dimensions, schema drift, and coverage drops, responding before you notice.
Furniture retailers monitor Durian's pricing, festive discounts, and EMI structures to adjust their own promotional strategies.
Marketplace category managers analyse Durian's catalogue depth across materials and finishes to identify gaps in their own inventory.
Proptech and interior design platforms ingest structured dimensions and high-res images to populate their 3D planning software.
Logistics teams track delivery timelines across different pincodes to benchmark regional fulfillment performance.
Analysts track new product launches and category expansion to understand trends in the premium Indian furniture market.
Computer vision teams use classified lifestyle images and product shots to train furniture recognition and style-matching models.
"Durian holds a highly structured catalogue of premium Indian furniture specifications, but extracting precise dimensions and material data requires a dedicated pipeline."
Most teams underestimate the investment required: reliable Durian scraping requires session handling for pincode availability, dynamic specification parsing, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our durian.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows for pincode checks.
We maintain pools of residential ISP proxies across Indian regions. Rotation happens per-request with sticky sessions where location consistency is required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About durian.in scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and specification data is generally permissible. DataFlirt targets only public, non-authenticated catalogue data. We do not extract personal user data or circumvent authentication walls.
Yes. We configure pipelines to maintain specific session states, allowing us to extract delivery timelines, shipping costs, and inventory availability for any given set of pincodes.
Our parsers use custom regex and text-pattern matching to normalise inconsistent HTML descriptions into strict width, depth, and height numeric fields, ensuring the data is immediately usable.
Full catalogue refreshes at a daily cadence typically complete within a 2-4 hour window. For specific high-priority categories, we can configure hourly change-detection runs.
Yes. We intercept network requests and parse JSON objects to extract the raw, uncompressed image URLs rather than the compressed thumbnails displayed on the category pages.
Our packages start at weekly full-catalogue exports. We price based on delivery frequency and the complexity of the requested schema. Contact us with your specific requirements.
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, field completeness, and dimension accuracy.
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.
