We extract sneaker catalogues, size-level bid/ask spreads, historical trade volumes, and style feeds from Kream. 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 Catalogue objects from kream.co.kr. All fields typed and schema-versioned.
"product_id": "38291", "name_en": "Jordan 1 Retro High OG Chicago Lost and Found", "name_kr": "조던 1 레트로 하이 OG 시카고 로스트 앤 파운드", "brand": "Jordan", "model_no": "DZ5485-612", "retail_price": 239000, "colourway": "Varsity Red/Black/Sail/Muslin", "release_date": "2022-11-19"
| # | product_id | name_en | name_kr | brand | model_no | release_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Bid/Ask Spreads objects from kream.co.kr. All fields typed and schema-versioned.
"product_id": "38291", "size": "270", "lowest_ask": 580000, "highest_bid": 575000, "ask_volume": 14, "bid_volume": 22, "recent_trade_price": 578000, "timestamp": "2026-05-12T09:14:00Z"
| # | product_id | size | lowest_ask | highest_bid | ask_volume | bid_volume |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Trade History objects from kream.co.kr. All fields typed and schema-versioned.
"trade_id": "TRD-9928174", "product_id": "38291", "size": "270", "trade_price": 578000, "trade_date": "2026-05-12T08:45:22Z", "currency": "KRW", "condition": "NEW", "authentication_status": "VERIFIED"
| # | trade_id | product_id | size | trade_price | trade_date | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Style Feeds objects from kream.co.kr. All fields typed and schema-versioned.
"post_id": "ST-482910", "user_name": "sneakerhead_kr", "product_tags": "['38291', '19284']", "likes_count": 1492, "comments_count": 34, "upload_date": "2026-05-11T14:22:00Z", "hashtags": "['#jordan1', '#chicago', '#streetwear']"
| # | post_id | user_id | user_name | product_tags | likes_count | comments_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Brand Index objects from kream.co.kr. All fields typed and schema-versioned.
"brand_id": "BR-092", "brand_name_en": "Nike", "brand_name_kr": "나이키", "total_items": 14291, "followers": 849201, "upcoming_releases": 12, "scraped_at": "2026-05-12T09:15:00Z"
| # | brand_id | brand_name_en | brand_name_kr | total_items | followers | upcoming_releases |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Kream scraper handles the highly dynamic nature of a stock-market style platform: parsing bid/ask order books, paginating deep trade histories, and tracking size-level valuations while circumventing aggressive WAFs.
Extract product names (EN/KR), model numbers, retail prices, release dates, and high-resolution image assets across all streetwear and sneaker categories.
Capture the lowest ask and highest bid per size variant. Track spread margins and volume depth at high frequencies.
Paginate through thousands of historical transactions per product. Capture exact trade prices, sizes, and timestamps to build valuation curves.
Extract user-generated content from Kream Style. Map tagged products to posts, track engagement metrics, and identify trending items.
Sneaker prices vary wildly by size. We extract pricing and trade data mapped strictly to individual size variants, not just product averages.
Monitor upcoming drops, raffle schedules, and pre-release market sentiment indicators across all major brands.
Kream utilises aggressive regional blocking. We route requests through residential Korean ISP proxies to maintain consistent access.
Configure pipelines to poll highly volatile items at sub-hourly intervals to capture transient arbitrage opportunities.
Receive only new trades and updated bid/ask spreads since the last run, minimising warehouse compute and storage costs.
Brief in. Clean data out.
Provide brand lists, category URLs, or specific model numbers. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, KR proxy rotation, and cursor pagination logic for Kream's SPA.
Schema validation, null-rate checks, and order book accuracy verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Kream operates like a financial exchange with the security profile of a major bank. Here is how we maintain data flow.
Kream heavily restricts traffic originating outside South Korea. We utilise dedicated residential IP pools from Korean ISPs (KT, SK Broadband) to ensure high success rates and avoid geo-blocks.
Kream is a heavily JavaScript-rendered Single Page Application. We run full Playwright browser sessions to execute JS, hydrate state, and extract data from the virtual DOM that static HTTP clients cannot see.
Trade histories load via infinite scroll using complex GraphQL queries. We reverse-engineer these API calls to paginate deeply into historical data, capturing years of transaction history without browser memory exhaustion.
Extracting high-frequency bid/ask updates triggers strict rate limits. Our infrastructure employs adaptive throttling and jittered request timing, distributing load across thousands of IPs to remain undetected.
Order books change by the second. We enforce strict timestamping at the moment of extraction, ensuring your pricing models receive accurate, point-in-time snapshots of market state.
Global sneaker funds and professional resellers compare Kream spreads against StockX and GOAT to identify cross-border arbitrage opportunities.
Financial analysts and PE firms track trade volumes and price premiums to value the secondary streetwear market in Asia.
Apparel brands analyse Kream Style feeds and search volumes to forecast upcoming trends and inform production cycles.
Retailers monitor secondary market premiums on specific releases to gauge brand heat and adjust primary retail strategies.
Machine learning teams use high-resolution product imagery and metadata to train computer vision models for counterfeit detection.
Consignment stores use historical trade velocity and bid depth to determine which sizes and models to stock.
"Kream dictates the Asian streetwear market, but its bid/ask spreads and trade volumes are locked behind heavy JavaScript and aggressive regional WAFs."
Scraping Kream requires more than basic HTTP requests. You need Korean residential IP pools to bypass regional blocks, full Playwright execution to render dynamic order books, and highly tuned cursor pagination to extract historical trade data. DataFlirt manages this infrastructure so you receive clean pricing signals without engineering overhead.
Everything supported by our kream.co.kr 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 orchestrates the crawl while Playwright handles complex JavaScript execution, GraphQL request interception, and SPA state hydration required for Kream.
We maintain dedicated pools of residential ISP proxies within South Korea. Rotation happens per-request with TLS fingerprint spoofing to evade Incapsula/Cloudflare.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling for high-frequency polling, ensuring bid/ask spreads are delivered within SLA windows.
Data delivered to where your team already works — no new tooling required.
About kream.co.kr scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available market data from Kream is generally permissible. DataFlirt targets only public product catalogues, anonymous trade histories, and visible bid/ask spreads. We do not extract personal user data or circumvent authentication walls.
We route all requests through premium residential ISP proxies physically located in South Korea. Combined with realistic browser fingerprinting via Playwright, this ensures consistent access without triggering WAF blocks.
For targeted lists of high-volatility items, we can configure pipelines to poll and deliver updates at sub-hourly intervals. Full catalogue refreshes are typically scheduled daily.
Yes. We reverse-engineer Kream's pagination mechanisms to extract the complete historical transaction log for specified products, capturing every recorded trade price, size, and timestamp.
We extract both the native Korean name (name_kr) and the English name (name_en) as provided by Kream's platform, allowing you to map entities accurately across global datasets.
Our smallest packages start at a defined list of 1,000 target products with daily order book and trade history updates. Contact us for volume pricing on larger catalogues.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical trade dump or continuous bid/ask monitoring for arbitrage — we scope, build, and operate the pipeline. Tell us what you need.
