We extract product listings, shop profiles, pricing data, favourites signals, review corpus, and keyword rankings from Etsy. 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 etsy.com. All fields typed and schema-versioned.
"listing_id": "1487293041", "title": "Personalised Birth Flower Necklace | Sterling Silver", "shop_name": "BlossomAndCo", "price": 34.99, "currency": "USD", "favourites_count": 2847, "rating": 4.9, "review_count": 1203, "is_customisable": true, "is_digital": false
| # | listing_id | title | shop_id | shop_name | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Shop Profiles objects from etsy.com. All fields typed and schema-versioned.
"shop_id": "blossomandco", "shop_name": "BlossomAndCo", "sales_count": 18472, "listing_count": 94, "rating": 4.9, "star_seller": true, "admirers_count": 7291, "etsy_since": "2016-08-14"
| # | shop_id | shop_name | owner_name | location | currency | sales_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from etsy.com. All fields typed and schema-versioned.
"review_id": "etsy_rv_29471038", "listing_id": "1487293041", "star_rating": 5, "review_body": "Absolutely beautiful, arrived quickly and beautifully packaged.", "review_date": "2026-04-30", "variation_purchased": "Birth Flower: Rose | Chain: 18""
| # | review_id | listing_id | shop_id | reviewer_name | star_rating | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from etsy.com. All fields typed and schema-versioned.
"keyword": "personalised silver necklace", "position": 2, "listing_id": "1487293041", "is_ad": false, "free_shipping": true, "favourites_count": 2847, "scraped_at": "2026-05-12T08:44:19Z"
| # | keyword | position | listing_id | title | shop_name | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Etsy scraper covers every layer of the marketplace: product listings, shop intelligence, favourites and demand signals, review corpus, and search rankings — tailored to the handmade and vintage economy.
Title, description, tags, materials, processing time, variation options, images, and every metadata field Etsy surfaces — scraped at listing-ID level.
Capture favourites count, views, and listing age — key demand-proxy signals unavailable on most other marketplaces.
Sales count, total listings, rating, Star Seller badge, admirers count, shop policies, and full about-page content for every shop.
Full review text, star ratings, variation purchased, reviewer name, and image uploads — paginated across all review pages.
Monitor organic vs promoted listing position for any keyword on Etsy — with ad detection, free shipping, and Star Seller badge capture.
Capture base prices, variant-level pricing, currency, and shipping cost — timestamped per crawl for pricing trend analysis.
Identify customisable listings, digital downloads, and made-to-order items — important for segmentation and demand modelling.
etsy.com with country and currency filtering — normalised pricing for GB, DE, AU, CA, FR, and more on request.
One-off bulk exports or continuous pipelines at daily or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide listing IDs, category URLs, keyword sets, or shop names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for etsy.com.
Schema validation, null-rate checks, favourites-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Etsy's dynamic listings, personalised feeds, and bot-detection layers require specialised infrastructure. Here's how we stay resilient.
Etsy's fraud prevention operates on IP reputation, TLS fingerprints, and behavioural heuristics. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Etsy listing pages, shop profiles, and search results are JavaScript-rendered React applications. We run full Playwright sessions with JavaScript execution and infinite-scroll triggering — capturing data that headless HTTP clients miss.
Etsy updates its DOM structure regularly. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, text-pattern matching, and LD+JSON structured data extraction — so layout changes don't break your pipeline.
Etsy's public API heavily restricts favourites and views data. Our web scraping layer captures these demand-proxy signals directly from listing pages — giving you market intelligence that API-only approaches simply can't provide.
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.
Brands and investors use Etsy data to map demand for handmade, vintage, and custom product categories — identifying trends before they cross into mainstream retail.
Etsy sellers track competitor listings, pricing strategies, review velocity, and favourites growth to optimise their own shops.
Fashion, home décor, and gift brands use Etsy favourites and search rank data as an early-signal trend indicator ahead of mainstream retail adoption.
ML teams use Etsy datasets — listing titles, tags, descriptions, and images — to train product classification, tagging, and recommendation models.
Analysts correlate favourites, review velocity, and price points to build demand models for handmade and craft goods categories.
Acquirers and investors evaluating Etsy-native brands use shop-level sales counts, review trends, and Star Seller history as performance proxies.
"Etsy's favourites count is one of the most honest demand signals in e-commerce — a free, unbiased measure of consumer desire that Amazon and Walmart simply don't publish."
Most teams underestimate what reliable Etsy scraping requires: residential proxies, full JavaScript rendering, infinite-scroll handling, CAPTCHA bypass, and daily selector maintenance. DataFlirt absorbs that complexity so your team can focus on the trends — not the infrastructure.
Everything supported by our etsy.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 React-rendered listing pages, infinite-scroll triggering, and cookie session management.
We maintain pools of residential ISP proxies across US/UK/DE/AU 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 etsy.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Etsy is generally permissible under applicable law — reinforced by the hiQ v. LinkedIn ruling and similar precedents. DataFlirt targets only public, non-authenticated listing, shop, and review data. We do not extract personal data, circumvent authentication walls, or violate GDPR. We recommend clients review Etsy's ToS independently and consult legal counsel for specific use cases.
Etsy's public API is heavily restricted — it does not expose favourites counts, views, search rankings, or full review corpora. Web scraping captures these demand-proxy signals directly from listing pages, giving you market intelligence that API-only approaches simply can't provide.
We use residential ISP proxies that appear as real consumer traffic, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes don't break the pipeline.
Yes. Favourites count and view count are scraped directly from listing pages. These are demand-proxy signals unavailable via Etsy's API and among the most valuable fields in our Etsy dataset.
For trending keyword monitoring, we support daily pipeline cadences. For large listing catalogues, full refreshes complete within a 6–12 hour window. Historical data is available from the date your pipeline starts.
Our smallest packages start at a defined listing set (typically 1,000–30,000 listings) with weekly delivery. For larger catalogues, ongoing shop monitoring, or custom schema requirements, we price based on volume and cadence. Contact us with your use case for a scoped quote.
Yes. Shop-level sales count, listing count, review count, and Star Seller status are captured per run. Time-series shop profiles are available from the date your pipeline is commissioned.
Absolutely. We provide a sample run of up to 500 listings 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 category trends dump or a continuous shop-monitoring feed across 500K listings — we scope, build, and operate the pipeline. Tell us what you need.
