We extract bag listings, colour variations, size matrices, pricing, stock depth, and review corpora from Dagne Dover. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
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 dagnedover.com. All fields typed and schema-versioned.
"product_id": "4592819203", "handle": "dakota-neoprene-backpack", "title": "Dakota Neoprene Backpack", "collection": "Backpacks", "material_type": "Premium Neoprene", "weight_kg": 0.95
| # | product_id | handle | title | collection | material_type | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Variants & Pricing objects from dagnedover.com. All fields typed and schema-versioned.
"variant_id": "319204857", "sku": "DAK-MED-ONYX", "colour_name": "Onyx", "size": "Medium", "price": 155.0, "in_stock": true
| # | variant_id | product_id | sku | colour_name | colour_family | size |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from dagnedover.com. All fields typed and schema-versioned.
"review_id": "REV-928471", "rating": 5, "author_name": "Sarah M.", "verified_buyer": true, "review_title": "Perfect work bag", "created_at": "2023-10-14T08:22:00Z"
| # | review_id | product_id | rating | author_name | verified_buyer | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Bundles & Kits objects from dagnedover.com. All fields typed and schema-versioned.
"kit_id": "KIT-TRAVEL-101", "kit_title": "The Weekender Set", "included_skus": "['LAN-LRG-ONYX', 'HUN-MED-ONYX']", "bundle_price": 245.0, "discount_percentage": 15, "stock_status": "in_stock"
| # | kit_id | kit_title | included_skus | total_retail_value | bundle_price | discount_percentage |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Materials & Specs objects from dagnedover.com. All fields typed and schema-versioned.
"sku": "DAK-MED-ONYX", "primary_material": "Repreve Recycled Polyester", "hardware_finish": "Colour-plated Zinc Alloy", "laptop_fit_inches": 13, "volume_litres": 16, "vegan_certified": true
| # | sku | primary_material | lining_material | hardware_finish | laptop_fit_inches | volume_litres |
|---|---|---|---|---|---|---|
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Our pipeline captures the full dimensional matrix of Dagne Dover's inventory, resolving complex size-colour permutations, seasonal drops, and real-time stock availability across their Shopify infrastructure.
Capture SKU, title, description, material specifications, and care instructions across all collections and categories.
Map every colour and size permutation to distinct variant IDs, capturing base and seasonal colourways accurately.
Monitor stock levels, waitlist status, and low-stock warnings directly from frontend JSON state objects.
Extract base price, bundle pricing, and seasonal sale discounts mapped to specific variant SKUs.
Extract user-generated content, star ratings, and verified buyer tags from integrated review platforms.
Parse Repreve polyester usage, vegan certification flags, and hardware details for material analysis.
Extract CDN URLs for all variant-specific product photos, interior shots, and lifestyle imagery.
Resolve nested SKUs within Dagne Dover's curated travel and diaper kits to calculate exact discount margins.
Run daily catalogue sweeps or hourly stock monitors with intelligent change-detection diffing.
Brief in. Clean data out.
Provide target collections, specific product handles, or full catalogue requirements. We map the extraction schema.
We configure crawlers to parse Shopify JSON state, manage rate limits, and resolve the full variant matrix.
Schema validation, null-rate checks, and variant-to-parent mapping verification before full pipeline deployment.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on your specified cadence.
Extracting structured data from modern headless Shopify builds requires handling dynamic JSON state and complex variant routing rather than simple DOM parsing.
Rather than scraping fragile HTML nodes, our pipeline extracts product data directly from the embedded JSON state objects within the page source, ensuring complete and highly accurate variant data.
Dagne Dover products feature multiple dimensions including size and colour. We iterate through the JSON variant arrays to map every possible combination to its distinct SKU and stock status.
We respect CDN and edge rate limits during deep catalogue crawls, utilising distributed request patterns and connection pooling to prevent blocks while maintaining throughput.
For inventory monitoring, we maintain a hash index of last-seen variant states. Subsequent runs only push data when stock levels, pricing, or waitlist status changes.
Frontend architectures evolve. We monitor for structural changes in the JSON payload or missing nodes, alerting our engineering team before it impacts your data delivery.
Luggage and bag brands track premium D2C pricing strategies, bundle discounts, and seasonal sale cadences.
Retail analysts monitor size and colour stockouts to infer demand patterns for specific product lines.
Tracking the adoption and pricing impact of recycled neoprene, vegan materials, and sustainable hardware in premium bags.
NLP models parse customer feedback on durability, hardware quality, and pocket configurations to inform product design.
Resale platforms track original retail prices and seasonal colourway rarity to optimise pricing algorithms.
Monitoring kit bundling logic and promotional discount timing to understand customer acquisition strategies.
"Dagne Dover's variant matrix contains critical demand signals. Knowing which seasonal colours sell out first in specific sizes provides invaluable predictive inventory data."
Extracting flat product lists is trivial, but mapping Dagne Dover's complex multi-dimensional variants to precise stock levels requires deep integration with their frontend state. DataFlirt handles the headless Shopify complexities, rate limits, and JSON extraction so your team receives clean, normalised relational data.
Everything supported by our dagnedover.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 manages orchestration and deduplication while Playwright handles complex frontend interactions and dynamic payload extraction.
Custom middleware designed to parse and normalise Shopify JSON state arrays, bypassing fragile DOM-based scraping entirely.
Pipelines execute on AWS Lambda and ECS, orchestrated by Apache Airflow to guarantee SLA compliance and reliable delivery schedules.
Data delivered to where your team already works — no new tooling required.
About dagnedover.com 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 data. We do not extract PII or circumvent authentication walls. Clients should consult legal counsel regarding their specific use cases.
Yes. Our pipeline extracts data at the variant level, meaning we track inventory status, waitlist availability, and pricing for specific size and colour combinations independently.
We utilise distributed residential proxy pools, implement strict concurrency limits, and employ exponential backoff retry logic to respect edge infrastructure while ensuring complete catalogue extraction.
Yes. We parse the kit configuration data to identify the base SKUs included in bundles, allowing you to calculate exact discount percentages and component values.
For targeted SKU lists, we can configure pipelines to run at hourly intervals. Full catalogue sweeps are typically scheduled daily to balance data freshness with compute efficiency.
Yes. We provide a sample extraction of up to 50 SKUs during the scoping phase, allowing your engineering team to validate the schema structure and variant mapping accuracy.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily catalogue snapshot or high-frequency inventory tracking across their variant matrix - we scope, build, and operate the pipeline. Tell us what you need.
