We extract apparel listings, sizing grids, colour variants, pricing signals, and brand intelligence from Zattini. 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 zattini.com.br. All fields typed and schema-versioned.
"sku": "ZAT-8492-BL", "title": "Vestido Midi Canelado", "brand": "Shoestock", "category": "Roupas", "sub_category": "Vestidos", "price": 149.9, "currency": "BRL", "discount_pct": 25, "material": "95% Poliester, 5% Elastano"
| # | sku | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants & Sizing objects from zattini.com.br. All fields typed and schema-versioned.
"parent_sku": "ZAT-8492", "variant_sku": "ZAT-8492-BL-M", "colour_name": "Preto", "size_label": "M", "in_stock": true, "stock_quantity": 14, "price_override": "None"
| # | parent_sku | variant_sku | colour_name | colour_hex | size_label | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promotions objects from zattini.com.br. All fields typed and schema-versioned.
"sku": "ZAT-8492-BL", "current_price": 149.9, "original_price": 199.9, "discount_pct": 25, "campaign_name": "Black Friday Antecipada", "installment_count": 3, "installment_value": 49.96, "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | current_price | original_price | discount_pct | campaign_name | installment_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from zattini.com.br. All fields typed and schema-versioned.
"review_id": "REV-93821", "sku": "ZAT-8492-BL", "reviewer_name": "Mariana S.", "star_rating": 5, "review_title": "Perfeito", "review_body": "O caimento e otimo e o tecido e muito confortavel.", "review_date": "2026-04-18", "recommended_flag": true
| # | review_id | sku | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category & Search objects from zattini.com.br. All fields typed and schema-versioned.
"keyword": "vestido midi", "breadcrumb": "Feminino > Roupas > Vestidos", "position": 1, "sku": "ZAT-8492-BL", "brand": "Shoestock", "price": 149.9, "rating": 4.8, "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | breadcrumb | position | sku | title | brand |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Zattini scraper handles dynamic fashion catalogues: complex sizing grids, colour variant mapping, promotional pricing, and stock availability - with JavaScript rendering and anti-bot circumvention built in.
Title, description, material composition, care instructions, and brand details extracted accurately for every SKU.
Link parent SKUs to child variants across multiple colours and sizes to reconstruct the full product matrix.
Capture current price, list price, discount percentages, and Brazilian installment plans (parcelas).
Monitor stock status across all available sizes for a given apparel item to track inventory depth.
Track product counts, average pricing, and discount depth per brand across the Zattini marketplace.
Extract customer feedback, star ratings, and recommendation flags to gauge product sentiment.
Traverse Zattini category trees and breadcrumbs for accurate classification of apparel items.
Extract CDN URLs for main product images and variant-specific shots for visual AI training.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences.
Brief in. Clean data out.
Provide SKU lists, category URLs, keyword sets, or brand names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, Brazilian proxy rotation, session management, and CAPTCHA handling.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Zattini deploys modern e-commerce bot protections and dynamic rendering. Here is how we stay resilient.
Zattini blocks datacentre IPs and monitors request headers. Our crawlers use Brazilian residential ISP proxies with realistic browser fingerprints and full cookie session management.
Product variants and sizing grids load dynamically via JavaScript. We run full Playwright browser sessions to trigger lazy-loads and hydrate pricing widgets.
Mapping sizes and colours to specific SKUs requires parsing complex JSON objects embedded in the DOM. Our parsers reconstruct the exact product matrix.
E-commerce sites update DOMs frequently. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline.
For large brand catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Brands monitor competitor pricing, discount depth, and promotional campaigns across the Zattini marketplace.
Retailers analyse Zattini category depth and brand representation to optimise their own inventory mix.
Track promotional campaigns and flash sales to understand pricing elasticity in the Brazilian fashion market.
Audit third-party sellers on the marketplace for MAP violations and unauthorised discounting.
Analyse new arrivals and out-of-stock velocities to identify emerging fashion trends.
Train fashion recommendation engines and visual search models using high-resolution image URLs and metadata.
"Zattini holds critical pricing and assortment signals for the Brazilian fashion market, but extracting variant-level stock data requires specialised infrastructure."
Most teams underestimate the investment required: reliable fashion scraping requires Brazilian residential proxies, full JavaScript rendering for sizing grids, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our zattini.com.br 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 in Brazil. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 zattini.com.br scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Zattini 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 Brazilian residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time.
Yes. We map parent SKUs to all child variants and extract the in-stock status and available quantity for each specific size and colour combination.
Real-time streaming pipelines achieve sub-60-minute latency for price signals on a defined SKU set. Full catalogue refreshes complete within a 6-12 hour window.
Yes. We extract the full installment breakdown, including the number of installments, value per installment, and any applicable interest rates displayed on the product page.
Absolutely. We provide a sample run of up to 500 SKUs or 50 category pages as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off apparel catalogue dump or a continuous price-monitoring feed across 300K SKUs - we scope, build, and operate the pipeline. Tell us what you need.
