We extract sneaker releases, apparel catalogues, size-level inventory, and pricing signals from Kickz. 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 Sneaker Catalogue objects from kickz.com. All fields typed and schema-versioned.
"sku": "NK-DZ5485-612", "title": "Air Jordan 1 Retro High OG", "brand": "Jordan", "silhouette": "Air Jordan 1", "colourway": "Lost & Found", "price": 189.95, "currency": "EUR"
| # | sku | title | brand | silhouette | colourway | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Apparel & Accessories objects from kickz.com. All fields typed and schema-versioned.
"product_id": "AP-7849201", "name": "Heavyweight Graphic Hoodie", "brand": "Carhartt WIP", "category": "Apparel", "sub_category": "Hoodies", "price": 89.0, "discount_pct": 15
| # | product_id | name | brand | category | sub_category | fit_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Release Calendar objects from kickz.com. All fields typed and schema-versioned.
"drop_id": "REL-9021", "shoe_name": "Yeezy Boost 350 V2", "brand": "adidas", "release_timestamp": "2026-06-01T08:00:00Z", "retail_price": 230.0, "raffle_type": "FCFS", "draw_status": "upcoming"
| # | drop_id | shoe_name | brand | silhouette | release_timestamp | retail_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Promos objects from kickz.com. All fields typed and schema-versioned.
"sku": "NK-DZ5485-612", "base_price": 189.95, "current_price": 189.95, "currency": "EUR", "discount_pct": 0, "promo_eligible": false, "scraped_at": "2026-05-12T09:14:00Z"
| # | sku | base_price | current_price | currency | discount_abs | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Sizes objects from kickz.com. All fields typed and schema-versioned.
"sku": "NK-DZ5485-612", "size_eu": "44", "size_us": "10", "size_uk": "9", "in_stock": true, "low_stock_warning": true, "scraped_at": "2026-05-12T09:14:33Z"
| # | sku | size_eu | size_us | size_uk | in_stock | stock_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Kickz scraper extracts the complete streetwear and sneaker catalogue: size availability, pricing changes, colourways, and release drops — with proxy rotation and anti-bot circumvention built in.
Title, brand, silhouette, colourway, materials, images, and every metadata field Kickz surfaces — scraped at SKU level.
Capture in-stock status and low-stock warnings for every specific size (EU, US, UK) across the entire footwear and apparel range.
Track current price, list price, discount percentages, and sale categorisation — timestamped per crawl.
Extract upcoming drops, release timestamps, retail prices, and raffle types for highly anticipated sneaker launches.
Categorised extraction of hoodies, tees, outerwear, and accessories with fit types, fabric composition, and care instructions.
Link parent products to all available colour variants, ensuring complete coverage of the product matrix.
Extract localized pricing and availability across Kickz regional domains.
Monitor stock changes and drop statuses at high frequency to capture fleeting inventory signals.
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, brand filters, or target SKUs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for kickz.com.
Schema validation, null-rate checks, price-outlier detection, and sample records before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Sneaker retailers deploy aggressive bot protection to defend against scalpers. Here's how we stay resilient for legitimate data extraction.
Sneaker sites utilize strict TLS fingerprinting and behavioral analysis. Our crawlers use European residential ISP proxies with realistic browser fingerprints, randomised request timing, and full session management.
Size selectors, stock status, and release timers are heavily JavaScript-rendered. We run full Playwright browser sessions to trigger lazy-loads and hydrate dynamic inventory widgets.
Frontend structures change during major sales or drops. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, and JSON-LD extraction — ensuring continuous data flow.
Tracking sneaker drops requires sub-minute precision. We distribute requests across massive IP pools to monitor release pages without triggering rate limits or IP bans.
For large catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs — reducing compute cost, storage bloat, and downstream processing load.
Retailers and brands monitor Kickz pricing, discount depth, and promotional calendars to optimise their own pricing strategies.
Analysts track size-level stock depletion rates to gauge demand for specific silhouettes and colourways.
Resellers and trading platforms correlate retail availability on Kickz with secondary market premiums to identify arbitrage opportunities.
Brands audit Kickz's brand mix, category depth, and visual merchandising to understand market positioning.
Sneaker news outlets and community platforms ingest drop dates and raffle mechanics to populate consumer-facing calendars.
ML teams use structured footwear metadata and imagery to train visual search models and product recommendation engines.
"Kickz holds critical inventory signals for the European sneaker market, but tracking size-level availability requires sub-minute polling and aggressive bot mitigation."
Most teams underestimate the investment required: reliable sneaker 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 kickz.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.
We maintain pools of residential ISP proxies across EU regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About kickz.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Kickz is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and inventory data. We do not extract personal data or circumvent authentication walls.
We use 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 and trigger pool rotation or solver queues automatically.
Yes. Our pipeline extracts stock availability for every specific size (EU, US, UK) listed on the product page, including low-stock indicators.
Real-time streaming pipelines achieve sub-minute latency for high-priority SKUs during drops. Full catalogue refreshes at daily cadence complete within a 4-8 hour window depending on size.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per SKU for price, discount depth, and availability from the date your pipeline starts.
Our smallest packages start at a defined brand list or category subset with weekly delivery. For full catalogue tracking, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off apparel catalogue dump or a continuous inventory-monitoring feed across sneaker drops — we scope, build, and operate the pipeline. Tell us what you need.
