We extract product listings, technical specifications, pricing signals, and inventory states from Kelty. 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 Backpacks & Gear objects from kelty.com. All fields typed and schema-versioned.
"sku": "22615620", "title": "Coyote 60 Backpack", "category": "Backpacks", "price": 179.95, "volume_litres": 60, "weight_kg": 2.1, "torso_fit_range": "15 to 20 inches", "frame_type": "Internal"
| # | sku | title | category | price | volume_litres | weight_kg |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tents & Shelters objects from kelty.com. All fields typed and schema-versioned.
"sku": "40812223", "title": "Late Start 2 Tent", "capacity": "2 Person", "packed_weight": "2.4 kg", "floor_area": "2.7 sq m", "peak_height": "101.6 cm", "pole_type": "Aluminum", "price": 159.95
| # | sku | title | capacity | seasons | packed_weight | floor_area |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Inventory objects from kelty.com. All fields typed and schema-versioned.
"sku": "22615620-BLU", "variant_id": "8472910", "colour": "Lyons Blue", "price": 179.95, "list_price": 179.95, "discount_pct": 0, "in_stock": true, "scraped_at": "2026-05-12T09:14:00Z"
| # | sku | parent_id | variant_id | colour | size | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sleeping Bags objects from kelty.com. All fields typed and schema-versioned.
"sku": "35416220", "title": "Cosmic Down 20", "temp_rating": "20F / -7C", "insulation_type": "550 Fill Down", "total_weight": "1.19 kg", "fits_up_to": "183 cm", "price": 169.95
| # | sku | title | temp_rating | insulation_type | fill_weight | total_weight |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Hierarchy objects from kelty.com. All fields typed and schema-versioned.
"category_id": "cat_tents", "name": "Tents", "url": "/categories/tents", "parent_category": "Camping", "breadcrumb": "Home > Camping > Tents", "product_count": 42, "meta_title": "Camping Tents & Shelters | Kelty"
| # | category_id | name | url | parent_category | breadcrumb | product_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Kelty scraper navigates product hierarchies, extracts nested technical specifications, and tracks pricing across all colour and size variants.
Extract volume, weight, materials, dimensions, and fit ranges for every backpack, tent, and sleeping bag in the catalogue.
Capture prices, MSRP, and discount percentages for specific colourways and sizes. Track promotional changes over time.
Check stock availability and inventory levels for every SKU variant to monitor sell-through rates and restocks.
Reconstruct Kelty's exact category taxonomy, mapping products to their specific outdoor activity segments.
Extract high-resolution image URLs, lifestyle photos, and product manual PDFs linked on product pages.
Run daily or weekly pipelines. Our change detection system emits only updated records to minimise redundant data.
Requests route through residential ISPs to avoid rate limits and IP bans during high-frequency catalogue crawls.
Messy HTML tables and unstructured description text are parsed into strictly typed JSON fields.
Extract customer ratings, review text, and recommendation flags across the entire product line.
Brief in. Clean data out.
Provide specific categories, product lines, or request a full site crawl. We map the required technical fields.
We configure Scrapy spiders, set up proxy rotation, and build parsers for Kelty's specific DOM structure.
Schema validation, null-rate checks, and data typing verification before full production launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on an agreed cadence.
Extracting technical data requires handling dynamic variant loading and nested product specifications.
Kelty's frontend loads specific pricing and inventory data via JavaScript when a user selects a colour or size. We use Playwright to simulate these interactions, ensuring we capture the exact state of every variant.
Technical specs are often embedded in complex HTML tables or bulleted lists. Our parsers use specific regex and XPath rules to map raw text into typed fields like volume_litres and weight_kg.
To avoid triggering CDN blocks, our crawlers implement adaptive concurrency and request delays, routing traffic through a rotating pool of residential IPs.
Retail sites frequently update their themes. We rely on LD+JSON structured data where available, falling back to CSS selectors to maintain pipeline stability during site updates.
We clean and normalise extracted data, converting mixed unit strings into standard numeric formats to ensure immediate usability in your data warehouse.
Outdoor brands track Kelty's pricing strategies and discount cadences across specific gear categories.
Retailers analyse Kelty's product mix, colour options, and sizing to inform their own buying decisions.
Design teams aggregate technical specifications like pack volume and tent weights to benchmark new products.
Brands monitor listed prices to ensure retail partners comply with minimum advertised price policies.
Analysts track the introduction of new materials and frame types across outdoor gear catalogues.
Supply chain analysts monitor stock-out frequencies on popular items to gauge market demand.
"Kelty's catalogue contains highly structured technical specifications for outdoor gear, but extracting it requires parsing complex variant matrices."
Outdoor equipment retailers rely on precise technical data. We handle the extraction of complex variant matrices, dynamic inventory states, and nested specifications. DataFlirt manages the proxy rotation and session states so your team receives clean, normalised datasets ready for immediate analysis.
Everything supported by our kelty.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 the crawl orchestration and deduplication. Playwright handles JavaScript execution required to load dynamic pricing and variant availability.
We utilise residential IP pools to distribute requests, preventing IP bans and ensuring uninterrupted access to the Kelty catalogue.
Pipelines are deployed on Kubernetes and scheduled via Apache Airflow, providing reliable execution and automated alerting for SLA compliance.
Data delivered to where your team already works — no new tooling required.
About kelty.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our scraper iterates through all available variant combinations on a product page, capturing specific SKUs, prices, and inventory statuses for each option.
We parse the specification tables and lists on product pages, mapping raw text to structured fields. We normalise units like litres and kilograms for immediate analytical use.
Inventory data reflects the state of the website at the time of the crawl. We can configure pipelines to run hourly for critical SKUs to approximate real-time monitoring.
Yes. We extract URLs for PDF manuals, setup videos, and care instructions linked within the product descriptions.
Yes. We capture aggregate ratings, individual review text, dates, and helpful votes across all product pages.
Our pipelines use fallback selector chains and structured data extraction. We monitor schema drift and update our parsers automatically if Kelty deploys a new frontend design.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across all variants, we scope, build, and operate the pipeline. Tell us what you need.
