We extract product catalogues, dimension specifications, pricing signals, stock depth, and reviews from Antler. 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 Luggage Products objects from antler.co.uk. All fields typed and schema-versioned.
"sku": "ANT-CLF-CAB-SYC", "title": "Clifton Cabin", "category": "Cabin Luggage", "price": 170.0, "capacity_litres": 37, "dimensions_cm": "20 x 40 x 55", "weight_kg": 2.8, "material": "Polycarbonate", "in_stock": true
| # | sku | title | category | price | capacity_litres | dimensions_cm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Stock objects from antler.co.uk. All fields typed and schema-versioned.
"sku": "ANT-CLF-CAB-SYC", "current_price": 170.0, "original_price": 170.0, "discount_pct": 0, "stock_status": "In Stock", "low_stock_warning": false, "currency": "GBP", "scraped_at": "2026-05-12T09:14:00Z"
| # | sku | current_price | original_price | discount_pct | stock_status | low_stock_warning |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Variants objects from antler.co.uk. All fields typed and schema-versioned.
"parent_sku": "ANT-CLF", "variant_sku": "ANT-CLF-CAB-SYC", "colour_name": "Sycamore", "colour_hex": "#4A5D23", "price": 170.0, "stock_status": "In Stock", "size_category": "Cabin"
| # | parent_sku | variant_sku | colour_name | colour_hex | image_urls | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from antler.co.uk. All fields typed and schema-versioned.
"review_id": "REV-98234", "sku": "ANT-CLF-CAB-SYC", "rating": 5, "author": "Sarah M.", "date": "2026-04-18", "title": "Perfect for EasyJet flights", "body": "Fits exactly in the overhead bin. Wheels are very smooth.", "verified_buyer": true
| # | review_id | sku | rating | author | date | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Specifications objects from antler.co.uk. All fields typed and schema-versioned.
"sku": "ANT-CLF-CAB-SYC", "shell_type": "Hard Shell", "wheels": "4x Double Spinner Wheels", "lock_type": "TSA Combination Lock", "airline_compatibility": "['EasyJet', 'British Airways', 'Ryanair Priority']", "expandable": false, "interior_features": "Packing straps, D-zip pocket, waterproof pocket"
| # | sku | shell_type | wheels | lock_type | interior_features | exterior_features |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Antler scraper normalises complex product variations, physical dimensions, and real-time inventory levels into structured data ready for analysis.
Extract and normalise height, width, depth, weight, and capacity metrics into queryable numeric fields rather than raw text strings.
Link parent products to all colour variants, capturing specific SKUs, hex codes, and variant-specific imagery.
Parse cabin size compliance data to map specific luggage models against airline dimension restrictions.
Monitor stock statuses, low stock warnings, and out-of-stock flags across all product variants.
Capture current prices, crossed-out original prices, and promotional text across the entire catalogue.
Extract customer ratings, review text, and verified buyer status to analyse product feedback.
Isolate details on lock types, wheel configurations, and handle mechanisms for feature comparison.
Categorise products by material composition, distinguishing between polycarbonate, soft shell, and hybrid constructions.
Receive only records that have changed since the last extraction run, minimising downstream processing overhead.
Brief in. Clean data out.
Specify product categories, specific SKUs, or full-site extraction requirements. We map the target schema.
We configure Scrapy spiders to navigate Antler's frontend, resolving variant data and handling pagination.
Schema validation ensures dimensions are numeric, prices are accurate, and variant mappings are correct.
JSON, CSV, or Parquet files delivered to your AWS S3 bucket or data warehouse on a scheduled cadence.
Extracting data from dynamic retail sites requires handling complex variant structures and background API calls. Here is how we process antler.co.uk.
Luggage products often have multiple colour and size variants that load dynamically. Our pipeline intercepts backend API responses or executes JavaScript to capture the full matrix of SKUs without missing hidden variants.
Dimensions expressed as '55 x 40 x 20 cm' are parsed into distinct height, width, and depth columns. Weights are converted to standard decimal formats, making the output immediately useful for database queries.
Frontend 'In Stock' badges can be delayed. We target the underlying inventory JSON endpoints to extract the most accurate stock statuses and low-stock thresholds.
We construct the highest resolution image URLs from the site's CDN patterns, bypassing low-quality thumbnails to deliver production-ready asset links.
Infinite scroll and dynamic loading are handled via headless browser automation, ensuring every product in a category is captured regardless of frontend rendering techniques.
Competing luggage brands analyse Antler's product mix, capacity ranges, and colour offerings to identify gaps in their own catalogues.
Retailers monitor Antler's pricing strategies, discount depths, and promotional cadences to adjust their own market positioning.
Travel aggregators and booking sites track exact luggage dimensions to update their passenger allowance guidelines.
Analysts track the introduction of new materials and hardware features across premium luggage brands.
Product teams mine customer feedback on wheel durability, lock reliability, and shell strength to inform future designs.
Supply chain analysts monitor out-of-stock rates on specific colour variants to model consumer demand trends.
"Accurate physical dimension data and real-time inventory signals are the foundation of competitive intelligence in the travel retail sector."
Extracting retail data requires more than simple HTML parsing. It demands handling complex variant matrices, intercepting background inventory APIs, and normalising physical specifications into queryable formats. DataFlirt manages this infrastructure so your team can focus on market analysis.
Everything supported by our antler.co.uk 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 across UK regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 antler.co.uk scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and review information is generally permissible. DataFlirt targets only public, non-authenticated retail data. We do not extract personal user data or circumvent authentication walls.
Our selectors use multiple fallback chains per field, including CSS, XPath, and JSON-LD structured data extraction. If the frontend layout changes, our monitoring detects schema drift and alerts our engineers to update the pipeline.
Yes. Our pipeline maps the parent product to all child variants, ensuring distinct SKUs, prices, and stock statuses are captured for every colour and size combination.
Pipelines can be scheduled to run daily, weekly, or at custom intervals depending on your requirement for pricing and stock freshness.
Yes. We parse raw dimension strings into distinct numeric fields for height, width, depth, and volume, standardising the units for immediate database insertion.
The pipeline captures the current inventory status flag. Out-of-stock items remain in the dataset with updated boolean flags, allowing you to track inventory depletion rates over time.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue extraction or continuous competitor price monitoring, we scope, build, and operate the pipeline. Tell us what you need.
