We extract clothing catalogues, pricing signals, sizing availability, reviews, and fabric composition from Joules. 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 joules.com. All fields typed and schema-versioned.
"sku": "218493-NAVY", "title": "Coast Waterproof Mac", "brand": "Joules", "category": "Womens", "description": "Our classic waterproof mac with a curved hem.", "fabric_composition": "100% Cotton"
| # | sku | title | brand | category | sub_category | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Promotions objects from joules.com. All fields typed and schema-versioned.
"sku": "218493-NAVY", "price": 69.95, "original_price": 99.95, "currency": "GBP", "discount_pct": 30, "promo_text": "Save 30% in the Mid-Season Sale"
| # | sku | price | original_price | currency | discount_pct | promo_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sizing & Inventory objects from joules.com. All fields typed and schema-versioned.
"sku": "218493-NAVY", "colour_name": "French Navy", "size_label": "UK 12", "in_stock": true, "low_stock_warning": true, "variant_id": "v-839210"
| # | sku | colour_name | colour_hex | size_label | in_stock | low_stock_warning |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews objects from joules.com. All fields typed and schema-versioned.
"review_id": "rev-94821", "sku": "218493-NAVY", "star_rating": 5, "review_title": "Perfect for rainy dog walks", "author": "Sarah M.", "date_posted": "2025-10-12"
| # | review_id | sku | star_rating | review_title | review_body | author |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category & Nav objects from joules.com. All fields typed and schema-versioned.
"category_id": "cat-womens-coats", "category_name": "Coats & Jackets", "parent_category": "Womens", "url_slug": "/womens/coats-jackets", "product_count": 142, "scraped_at": "2026-01-14T08:00:00Z"
| # | category_id | category_name | parent_category | url_slug | product_count | breadcrumb_path |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Joules scraper handles category pagination, dynamic sizing grids, colourway mapping, and promotional pricing logic — delivering clean, structured apparel data.
Extract titles, descriptions, fabric compositions, care instructions, and high-resolution image URLs for every garment.
Capture availability per size (UK 6 to UK 20), low stock warnings, and out-of-stock indicators at the variant level.
Map parent SKUs to all available colour variants, capturing specific pricing and inventory status for each colour.
Track current price, original RRP, discount percentages, and specific promotional banner text applied to items.
Reconstruct the exact site hierarchy from Women's, Men's, Children's, and Homeware down to specific sub-categories.
Extract star ratings, review text, author details, and helpfulness votes to gauge product sentiment.
Capture 'Wear it with' and 'You may also like' product recommendations for market basket analysis.
Extract standard and next-day delivery dispatch estimates displayed on the product page.
Receive only the records that changed since yesterday — perfect for tracking daily stock depletion.
Brief in. Clean data out.
Provide category URLs, search terms, or target departments. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for joules.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting accurate size-level inventory from Joules requires rendering JavaScript and managing dynamic state.
Sizing grids and stock availability on Joules load dynamically via API calls. We use Playwright to render the page fully, ensuring accurate stock capture per variant.
Apparel scraping requires unwrapping parent products into individual colour and size combinations. Our parsers normalise this into a flat, queryable structure.
We route requests through UK-based residential proxies to ensure we capture accurate GBP pricing and avoid geo-blocks or currency redirects.
We maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes or schema drift and respond immediately.
Retailers track Joules' pricing and promotional cadence to adjust their own mid-season sales strategies.
Analysts monitor size-level stock depletion to estimate sales velocity for specific product lines.
Fashion analysts aggregate colourways, patterns, and fabric compositions to identify seasonal trends.
Brands map category depth and price architecture to understand Joules' market positioning.
Product teams mine customer reviews to identify fit issues or quality complaints for similar apparel.
Suppliers track Joules to ensure third-party brands sold on the site adhere to minimum advertised pricing.
"Joules presents a highly structured apparel catalogue, but tracking daily size-level availability and promotional pricing requires dedicated infrastructure."
Most teams underestimate the investment required: reliable Joules scraping requires residential proxies, full JavaScript rendering for sizing grids, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers focus on analysis.
Everything supported by our joules.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 and deduplication. Playwright handles JavaScript rendering for dynamic sizing grids.
We maintain pools of UK residential ISP proxies to ensure localised pricing and bypass regional blocking.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management for daily runs.
Data delivered to where your team already works — no new tooling required.
About joules.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We render the product page using Playwright to interact with the size selector, capturing in-stock, out-of-stock, and low-stock indicators for every individual size variant.
For Joules, we typically recommend daily runs to capture pricing changes and stock depletion accurately. Hourly runs can be configured for specific high-priority categories.
Yes. We extract the current selling price, original RRP, discount percentages, and any promotional banner text (e.g., '20% off at checkout') applied to the item.
Yes. Our change detection system can be configured to emit only newly discovered SKUs, or specifically flag items that have dropped out of stock since the last run.
Yes. We can paginate through the review sections to extract star ratings, review text, and helpfulness metrics for sentiment analysis.
We deliver in JSON, CSV, or Parquet. We push directly to your S3 bucket, BigQuery, Snowflake, or via Webhook for integration into your existing systems.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily catalogue sync or historical pricing analysis — we scope, build, and operate the pipeline. Tell us what you need.
