We extract ethnic apparel listings, pricing signals, fabric specifications, and artisan seller intelligence from Craftsvilla. 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 craftsvilla.com. All fields typed and schema-versioned.
"product_id": "CV10924", "title": "Red Georgette Embroidered Saree", "brand": "Craftsvilla", "price": 1499.0, "discount_pct": 50, "fabric": "Georgette", "colour": "Red", "in_stock": true
| # | product_id | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from craftsvilla.com. All fields typed and schema-versioned.
"product_id": "CV10924", "price": 1499.0, "list_price": 2999.0, "discount_pct": 50, "shipping_fee": 50.0, "cod_available": true, "price_timestamp": "2026-05-12T09:14:00Z"
| # | product_id | price | list_price | discount_pct | shipping_fee | cod_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Data objects from craftsvilla.com. All fields typed and schema-versioned.
"seller_id": "SEL9842", "seller_name": "Jaipur Ethnic Weavers", "artisan_region": "Rajasthan", "rating": 4.6, "review_count": 342, "total_products": 128, "ships_from": "Jaipur"
| # | seller_id | seller_name | artisan_region | rating | review_count | total_products |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from craftsvilla.com. All fields typed and schema-versioned.
"review_id": "REV88321", "product_id": "CV10924", "star_rating": 5, "review_text": "Excellent fabric quality and embroidery.", "review_date": "2026-04-18", "verified_purchase": true, "helpful_votes": 12
| # | review_id | product_id | reviewer_name | star_rating | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Category objects from craftsvilla.com. All fields typed and schema-versioned.
"keyword": "georgette saree", "category_path": "Women > Sarees > Georgette", "position": 4, "product_id": "CV10924", "title": "Red Georgette Embroidered Saree", "price": 1499.0, "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | category_path | position | product_id | title | price |
|---|---|---|---|---|---|---|
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Our Craftsvilla scraper handles every layer of the platform: ethnic wear catalogues, dynamic pricing, fabric specifications, artisan seller intelligence, and the review corpus. JavaScript rendering, session management, and anti-bot circumvention built in.
Title, description, dimensions, weight, images, variations, and every metadata field Craftsvilla surfaces, scraped at product level with parent-child variant mapping.
Capture price, list price, deal badges, coupons, and festival pricing, timestamped per crawl.
Extract granular specifications including weave type, fabric blend, embroidery style, and care instructions.
Full review text, star ratings, helpful vote counts, verified purchase flags, and variant reviewed, paginated across all review pages.
Seller name, feedback score, fulfillment type, artisan region, active listing count, and return policy for every offer.
Track organic position for any keyword, category, and device type, with best seller badge capture.
Monitor inventory depth across all size and colour permutations to predict stockouts.
Monitor deal eligibility windows, claim percentages, and coupon stacking opportunities during peak sale events.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, keyword sets, or seller IDs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for craftsvilla.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
E-commerce portals deploy strict scraping detection. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
Craftsvilla bot detection operates on TLS fingerprints, browser headers, and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management, trained on real user behaviour patterns.
Craftsvilla product pages and search results are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution, lazy-load triggering, and dynamic price widget hydration, capturing data that headless HTTP clients miss entirely.
E-commerce DOM structures change frequently. Our selector strategy uses multiple fallback chains per field, including CSS selectors, XPath, and text-pattern matching, so a layout change does not break your data pipeline overnight.
For large ethnic wear catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost, storage bloat, and downstream processing load. You get a clean changelog rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops, and respond before you notice. SLA uptime is contractual, not aspirational.
Fashion brands and third-party sellers monitor pricing, deal windows, and shipping fees to reprice and protect margin.
Merchandisers track popular fabric types, colour palettes, and category saturation trends to identify whitespace and inventory opportunities.
Brands audit third-party sellers for MAP violations, counterfeit listings, and unauthorised resellers, protecting brand equity at scale.
ML teams use Craftsvilla datasets to train visual recommendation engines, fabric classifiers, and sentiment models.
Supply chain teams correlate category signals, review velocity, and stock depth indicators with sales velocity to improve procurement models.
Analysts track regional seller growth curves and review-to-rating ratios to evaluate artisan marketplace dynamics.
"Craftsvilla aggregates the fragmented ethnic fashion market, but extracting structured fabric and artisan data requires a resilient pipeline."
Most teams underestimate the investment required. Reliable Craftsvilla scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, 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 craftsvilla.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 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 craftsvilla.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Craftsvilla is generally permissible under applicable law in India. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review Craftsvilla ToS and consult legal counsel for specific use cases.
We use 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. We monitor for CAPTCHA rate spikes in real time and trigger pool rotation or solver queues automatically.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined product set. Full catalogue refreshes at daily cadence complete within a 6-12 hour window depending on size. Historical snapshots are available from the day your pipeline is commissioned.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per product for price, review count, and availability from the date your pipeline starts.
Our smallest packages start at a defined URL list (typically 1,000 to 50,000 products) with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Yes, including full pagination across all star-filter views, not just the top 10 reviews. Each review record includes rating, body, helpful votes, verified purchase flag, and review date.
Absolutely. We provide a sample run of up to 500 products or 50 search result pages as part of the pre-engagement scoping process, so you can validate schema fit, field completeness, and data quality before signing any contract.
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 400K SKUs, we scope, build, and operate the pipeline. Tell us what you need.
