We extract apparel listings, staff styling coordinates, sizing tables, and store inventory from ships-ltd.co.jp. 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 ships-ltd.co.jp. All fields typed and schema-versioned.
"product_id": "110130245", "name": "SHIPS: Super140's Wool Chester Coat", "brand": "SHIPS", "price_jpy": 39600, "colours": "['Navy', 'Charcoal Gray', 'Camel']", "sizes": "['S', 'M', 'L', 'XL']", "material": "Wool 100%"
| # | product_id | name | brand | category | price_jpy | sale_price_jpy |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Inventory & Stock objects from ships-ltd.co.jp. All fields typed and schema-versioned.
"sku": "110130245-78-93", "colour_code": "78", "size_code": "93", "online_stock_status": "in_stock", "online_stock_quantity": 14, "store_stock_available": true, "pre_order_flag": false
| # | sku | product_id | colour_code | size_code | online_stock_status | online_stock_quantity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Staff Styling objects from ships-ltd.co.jp. All fields typed and schema-versioned.
"coordinate_id": "c_284910", "staff_name": "Y. Tanaka", "staff_height_cm": 175, "shop_name": "SHIPS Ginza", "likes_count": 142, "items_worn_skus": "['110130245', '112040188']", "date_posted": "2026-10-14"
| # | coordinate_id | staff_name | staff_height_cm | shop_name | image_urls | items_worn_skus |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Sizing Tables objects from ships-ltd.co.jp. All fields typed and schema-versioned.
"product_id": "110130245", "size_label": "M", "length_cm": 92.0, "shoulder_width_cm": 44.5, "chest_cm": 106.0, "sleeve_length_cm": 62.0
| # | product_id | size_label | length_cm | shoulder_width_cm | chest_cm | sleeve_length_cm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Brands objects from ships-ltd.co.jp. All fields typed and schema-versioned.
"brand_name": "Barbour", "brand_slug": "barbour", "category_path": "Men > Outerwear > Jackets", "gender": "Men", "item_count": 48, "sale_items_count": 12, "url": "https://www.ships-ltd.co.jp/brand/barbour/"
| # | brand_name | brand_slug | category_path | gender | item_count | new_arrivals_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our scraper navigates the complexities of Japanese apparel retail: multi-dimensional variant matrices, staff styling content, and physical store inventory lookups.
Title, pricing, descriptions, materials, and care instructions extracted across Men, Women, and Kids categories.
Extract 'Staff Styling' posts linking specific outfits to SKUs, including staff height and store location.
Japanese size measurements mapped to structured fields: length, shoulder width, chest, and sleeve length per size variant.
Track online warehouse stock status alongside physical store availability across SHIPS retail locations.
Monitor third-party brands sold via SHIPS, tracking assortment size and category placement.
Track delivery windows and reservation limits for upcoming seasonal collections.
Capture discount percentages, sale event pricing, and historical price drops.
Extract uncompressed product and coordinate images for visual AI training or catalogue matching.
Run daily catalogue dumps or configure hourly checks for fast-moving inventory and flash sales.
Brief in. Clean data out.
Provide categories, brands, or specific search terms. We design the extraction schema together.
We configure Scrapy and Playwright crawlers for ships-ltd.co.jp, handling dynamic loading and bot protection.
Schema validation, Japanese text encoding checks, and variant mapping verification before launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Apparel scraping involves complex data structures. Here is how we manage the ships-ltd.co.jp pipeline.
Retailers often block foreign datacentre traffic. We route requests through Japanese residential ISP proxies to ensure consistent access and avoid geographic blocking.
Inventory levels and store availability are loaded asynchronously. We use Playwright to execute JavaScript and intercept the underlying API responses for accurate stock data.
We handle character encoding transformations, normalising full-width and half-width alphanumeric characters in Japanese text to ensure clean downstream analytics.
Apparel SKUs exist in multi-dimensional matrices. Our pipeline flattens colour and size combinations into distinct records, linking each to specific stock levels and identifiers.
For daily tracking, we maintain a hash index of last-seen values. Subsequent runs only push diffs for price changes or stock depletion, reducing processing load.
Retailers track SHIPS markdowns, sale events, and initial pricing strategies to adjust their own positioning.
Fashion analysts process staff styling coordinates to identify emerging silhouettes, colour palettes, and styling trends.
Supply chain teams monitor stock depletion rates across specific sizes and colours to optimise their own procurement.
Brands track their representation on SHIPS, monitoring SKU count, category placement, and sell-through indicators.
Merchandisers analyse the distribution of Men, Women, and Kids SKUs to understand category weighting.
Machine learning teams use staff coordinate images and associated SKU metadata to train outfit recommendation engines.
"SHIPS holds critical signals for the Japanese apparel market, but extracting multi-variant stock and styling data requires dedicated infrastructure."
Most teams underestimate the investment required: reliable apparel scraping requires handling complex size matrices, physical store inventory APIs, Japanese text normalisation, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers focus on analysis.
Everything supported by our ships-ltd.co.jp 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 and retry logic. Playwright executes JavaScript to load dynamic stock data and store inventory APIs.
We maintain pools of Japanese residential ISP proxies. Rotation happens per request to bypass datacentre IP blocks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. State is stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About ships-ltd.co.jp scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product and pricing information is generally permissible. DataFlirt targets only public, non-authenticated data. We do not bypass login walls or extract personal customer data.
Our pipeline normalises Japanese text, converting full-width alphanumeric characters to half-width where appropriate, and ensures UTF-8 encoding across all delivered datasets.
Yes. We scrape the Staff Styling section, extracting the main coordinate image, staff height, store location, and the specific product SKUs linked to the outfit.
Yes. We intercept the store inventory API calls to extract stock availability across SHIPS physical retail locations for specific SKUs.
We can configure hourly pipelines for specific high-priority SKUs, or daily catalogue refreshes for the entire site depending on your requirements.
We extract the sizing tables and map them to specific SKUs, ensuring that metrics like shoulder width or inseam are accurately tied to the correct size variant.
Yes. We provide a sample run of up to 500 products as part of the scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous stock-monitoring feed across 40,000 SKUs, we scope, build, and operate the pipeline. Tell us what you need.
