We extract premium luggage listings, bundle configurations, Peter McKinnon collaboration specs, and review data from Nomatic. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 nomatic.com. All fields typed and schema-versioned.
"sku": "TRVL-PACK-20L", "title": "Nomatic Travel Pack", "category": "Backpacks", "price": 299.99, "currency": "USD", "volume_litres": 20, "in_stock": true, "features": "['TSA-Approved', 'Magnetic Water Bottle Pocket', 'RFID Safe']"
| # | sku | title | category | collection | price | compare_at_price |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Bundle Configurations objects from nomatic.com. All fields typed and schema-versioned.
"bundle_id": "BNDL-MCKINNON-PRO", "bundle_title": "McKinnon Camera Pack Bundle", "bundle_price": 499.99, "base_price": 579.98, "discount_pct": 13.8, "components": "['Camera Pack 35L', 'Cube Pack', 'Accessory Case']", "availability": "In Stock"
| # | bundle_id | bundle_title | base_price | bundle_price | discount_amount | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Customer Reviews objects from nomatic.com. All fields typed and schema-versioned.
"review_id": "REV-993841", "product_sku": "TRVL-PACK-20L", "rating": 5, "author": "James W.", "date": "2023-11-14", "verified_buyer": true, "helpful_votes": 12, "title": "Perfect for weekend trips"
| # | review_id | product_sku | rating | author | date | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Technical Specs objects from nomatic.com. All fields typed and schema-versioned.
"sku": "TRVL-PACK-20L", "exterior_material": "Tarpaulin / Ballistic Nylon", "zipper_type": "YKK Weatherproof", "laptop_compartment_size": "Up to 16 inch", "dimensions_cm": "47 x 30 x 15", "weight_kg": 1.89, "warranty_type": "Lifetime"
| # | sku | exterior_material | interior_material | zipper_type | laptop_compartment_size | tablet_compartment_size |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Inventory & Pricing objects from nomatic.com. All fields typed and schema-versioned.
"sku": "TRVL-PACK-20L", "current_price": 299.99, "msrp": 299.99, "stock_status": "in_stock", "low_stock_warning": false, "scraped_at": "2023-11-20T08:14:00Z"
| # | sku | current_price | msrp | currency | stock_status | inventory_quantity |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our pipeline parses complex Shopify structures, bundle logic, and technical specifications directly from Nomatic's frontend and hidden JSON objects.
Extract core product data alongside colour and size variants. We map SKUs directly to their parent product models.
Parse Nomatic's bundle offers. We extract the constituent products, base pricing, and the applied discount logic.
Capture structured technical data including volume, weight, dimensions, laptop sleeve sizing, and exterior materials.
Isolate data for specific collections like the Peter McKinnon camera gear or Navigator series.
Scrape complete review histories from Nomatic's review provider, including verified buyer badges and helpful votes.
Track MSRP, current selling price, and active promotional discounts across the entire catalogue.
Monitor stock availability states and low-stock warnings exposed via Shopify frontend APIs.
Extract high-resolution image URLs and video asset links associated with each product variant.
Receive only the changed records — price adjustments, new reviews, or stock changes — to reduce processing load.
Brief in. Clean data out.
Provide target categories, product lines, or specific SKUs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, handle Shopify rate limits, and map the frontend JSON objects.
Schema validation, null-rate checks, and bundle constituent mapping before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Nomatic relies on complex frontend frameworks and dynamic inventory loading. Here is how we extract structured data reliably.
Rather than relying solely on HTML parsing, our crawlers intercept and parse the hidden Shopify product JSON objects, ensuring complete variant data and precise pricing without DOM scraping errors.
Nomatic frequently sells modular systems as bundles. We trace bundle SKUs back to their base components, calculating the exact discount percentage and mapping inventory dependencies.
Product reviews are often loaded via asynchronous JavaScript from third-party providers. We execute full Playwright sessions to trigger these network requests and paginate through the complete review corpus.
We distribute requests across residential IP pools and manage concurrency strictly, ensuring we collect data at scale without triggering aggressive WAF blocking or anti-bot captchas.
D2C brands update their storefront themes frequently. We monitor schema drift and use fallback selectors based on JSON-LD and meta tags to maintain pipeline stability during site redesigns.
Luggage and travel gear brands monitor Nomatic's pricing, bundle discounts, and promotional cadences to adjust their own positioning.
Product development teams analyse dimensional data, volume-to-weight ratios, and material choices (e.g., Tarpaulin vs Ballistic Nylon) across the catalogue.
Consumer research firms extract review text to identify common complaints or praised features in premium travel backpacks.
eCommerce strategists study how Nomatic structures its modular accessories and camera cubes to increase average order value.
Retail analysts monitor stock availability indicators to estimate sales velocity on flagship items like the Travel Pack.
Agencies track the expansion of Nomatic's collaboration lines (e.g., Peter McKinnon) to understand influencer-driven product strategies.
"Nomatic's product structure relies heavily on bundles and modular accessories. Extracting flat product data misses the commercial strategy entirely."
Scraping modern D2C Shopify storefronts requires parsing complex variant graphs and hidden inventory JSONs. DataFlirt manages the extraction pipeline so your analysts can focus on pricing strategy and material trends, not reverse-engineering frontend code.
Everything supported by our nomatic.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 executes JavaScript to load third-party widgets and complex variant selectors.
We maintain pools of residential ISP proxies to avoid rate-limiting and WAF blocks commonly deployed on high-traffic D2C storefronts.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About nomatic.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and review data is generally permissible. DataFlirt targets only public frontend data on nomatic.com. We do not extract personal user data or circumvent authentication walls.
We use residential ISP proxies and strict concurrency limits. By parsing the structured JSON payloads rather than aggressively crawling HTML paths, we minimise server load and avoid triggering WAF blocks.
Yes. We trace bundle listings back to their constituent SKUs, calculating the base value of the items and the effective discount percentage applied to the bundle.
Yes. We extract technical data including litres, dimensions in cm/inches, weight, exterior materials, and specific features like laptop compartment sizing.
Yes. We paginate through the third-party review widgets to extract the full corpus of customer feedback, including star ratings, text bodies, and verified buyer flags.
For standard catalogue tracking, we run daily pipelines. If you require higher frequency for inventory monitoring during sales events, we can configure hourly runs.
Yes. We provide a sample run covering a subset of the catalogue (e.g., the Travel Pack line) so you can validate schema fit and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or a continuous pricing and inventory feed — we scope, build, and operate the pipeline. Tell us what you need.
