We extract backpack, sleeve, and luggage listings, device compatibility matrices, material specs, and stock levels from Incase. 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 incase.com. All fields typed and schema-versioned.
"sku": "INBP100515-BLK", "title": "ICON Backpack", "category": "Backpacks", "price": 199.95, "colour": "Black", "volume_litres": 17, "compatibility": "['MacBook Pro 16', 'MacBook Pro 15', 'MacBook Pro 13']"
| # | sku | title | category | collection | price | colour |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Device Compatibility objects from incase.com. All fields typed and schema-versioned.
"sku": "INMB100604-GFT", "product_name": "Compact Sleeve in Woolenex", "device_brand": "Apple", "device_family": "MacBook Pro", "device_model": "14-inch", "fit_type": "Form-fitting", "max_laptop_size": "14 inch"
| # | sku | product_name | device_brand | device_family | device_model | release_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Inventory & Pricing objects from incase.com. All fields typed and schema-versioned.
"sku": "INBP100515-BLK", "variant_id": "314592817349", "base_price": 199.95, "sale_price": 149.95, "discount_pct": 25, "currency": "USD", "in_stock": true, "stock_status": "Low Stock"
| # | sku | variant_id | base_price | sale_price | discount_pct | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Material & Specs objects from incase.com. All fields typed and schema-versioned.
"sku": "INMB100604-GFT", "primary_material": "300D and 600D Woolenex polyester", "secondary_material": "Faux-fur lining", "hardware_type": "Vislon zipper", "dimensions_cm": "33.02 x 24.13 x 1.27", "weight_kg": 0.18, "warranty_type": "Limited Lifetime"
| # | sku | primary_material | secondary_material | hardware_type | dimensions_cm | weight_kg |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from incase.com. All fields typed and schema-versioned.
"review_id": "REV-948172", "sku": "INBP100515-BLK", "rating": 5, "reviewer_name": "Alex M.", "review_date": "2025-11-04", "review_title": "Perfect for my 16-inch Mac", "verified_buyer": true
| # | review_id | sku | rating | reviewer_name | review_date | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our pipeline captures the complex variant structures, proprietary material data, and explicit device compatibility matrices that define the Incase product lineup.
Capture titles, descriptions, dimensions, volume capacities, and weight specifications across all bags, sleeves, and accessories.
Extract explicit compatibility lists mapping specific Incase SKUs to Apple device models, sizes, and release years.
Isolate data on proprietary materials like Woolenex, BIONIC, and Ariaprene, including denier counts and hardware types.
Map parent products to all available child colourways, capturing unique SKUs and variant specific imagery.
Track MSRP, active sale prices, and discount percentages across the entire catalogue with daily timestamped runs.
Monitor in stock status and low stock warnings at the variant level to track product velocity and lifecycle.
Extract clean URLs for all product imagery, including lifestyle shots, interior details, and colour specific angles.
Compile customer ratings, review text, and verified buyer status to monitor product reception and quality issues.
Receive only new products, price changes, or stock status updates rather than processing the entire catalogue daily.
Brief in. Clean data out.
Specify the categories, collections, or specific SKUs you need to track on incase.com.
We configure Shopify GraphQL interception, proxy rotation, and variant mapping logic.
Schema validation, null-rate checks, and compatibility matrix verification before launch.
JSON, CSV, or Parquet pushed to your S3 bucket or warehouse on your defined schedule.
Extracting structured data from modern Shopify builds requires intercepting API calls rather than parsing DOM elements. Here is how we build resilient pipelines for sites like incase.com.
Instead of scraping HTML, our pipelines intercept the underlying GraphQL queries used by the headless frontend. This yields cleaner, more structured data and reduces pipeline breakage when UI layouts change.
A single product page on incase.com may contain dozens of colour and size combinations. We extract the full variant matrix, normalising each combination into a distinct, queryable SKU record.
Backend APIs enforce strict rate limits. We distribute extraction requests across a rotating proxy pool and implement intelligent backoff strategies to ensure complete data capture without triggering blocks.
Dimension strings and material descriptions often lack consistent formatting. We parse and normalise these fields into structured columns like volume_litres and primary_material for immediate analytical use.
We maintain state across pipeline runs, identifying exactly which SKUs have changed price, stock status, or specifications. You receive a clean diff payload, minimising warehouse compute costs.
Merchandisers analyse Incase product dimensions and compatibility matrices to ensure their own bag assortments cover the latest Apple hardware.
Direct-to-consumer accessory brands track Incase pricing tiers, discount cadences, and seasonal sale events to inform their own pricing strategies.
Product development teams monitor the adoption of sustainable materials like BIONIC yarn and Woolenex across the Incase catalogue.
Distributors compare direct incase.com pricing against third-party retail channels to identify Minimum Advertised Price violations.
Supply chain analysts track stock-out frequencies on flagship products to estimate production volumes and market demand.
Authorised resellers extract clean product specifications and high-resolution imagery to populate their own e-commerce platforms.
"Incase defines the standard for Apple-compatible carry goods. Mapping their device compatibility matrix against real-time pricing is impossible without a structured pipeline."
Extracting data from modern headless Shopify builds requires intercepting GraphQL queries and mapping complex variant IDs. DataFlirt handles the extraction logic, rate limit management, and schema normalisation so your team can focus on retail analytics rather than maintaining scrapers.
Everything supported by our incase.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.
Our pipelines bypass DOM parsing by intercepting the underlying API requests used by the headless Shopify frontend, ensuring structured and reliable data extraction.
Requests are distributed across a pool of datacenter and residential proxies to manage rate limits and ensure uninterrupted data collection.
Custom Python processing layers clean unstructured dimension strings and compatibility lists into strictly typed database columns.
Data delivered to where your team already works — no new tooling required.
About incase.com scraping, legality, and pipeline operations.
Ask us directly →We support daily, weekly, or monthly runs for the full catalogue. For specific high-priority SKUs, we can configure hourly pipelines to monitor inventory drops or price changes.
Yes. Incase explicitly lists compatible devices for most products. We extract this data and normalise it into an array of specific device models (e.g., MacBook Pro 14-inch 2023).
Yes. Our pipeline identifies all child variants associated with a parent product, extracting the unique SKU, pricing, imagery, and stock status for every available colourway.
We use custom parsing logic to extract length, width, and depth from text descriptions, normalising them into standard metric or imperial columns alongside volume capacities.
We begin tracking price history from the day your pipeline is commissioned. Every run produces a timestamped record, allowing you to build a time-series view of discounts and MSRP changes.
Yes. We can target specific regional domains or apply geolocation parameters to extract localised pricing and inventory availability across different markets.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop manually checking compatibility lists and stock levels. We build and maintain the pipeline to deliver clean, structured Incase data directly to your warehouse.
