We extract yoga mat specifications, apparel sizing, inventory depth, pricing signals, and review data from Manduka. 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 manduka.com. All fields typed and schema-versioned.
"sku": "111011010", "title": "Manduka PRO Yoga Mat 6mm", "collection": "PRO Series", "price": 138.0, "currency": "USD", "in_stock": true, "rating": 4.8, "review_count": 5421
| # | sku | title | collection | category | price | compare_at_price |
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Complete list of extractable fields for Pricing & Offers objects from manduka.com. All fields typed and schema-versioned.
"sku": "111011010", "variant_id": "394827162", "price": 138.0, "compare_at_price": 138.0, "discount_pct": 0, "sale_badge": false, "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | variant_id | price | compare_at_price | discount_pct | currency |
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Complete list of extractable fields for Reviews & Ratings objects from manduka.com. All fields typed and schema-versioned.
"review_id": "REV-992817", "sku": "111011010", "star_rating": 5, "verified_buyer": true, "review_title": "Best mat I have ever owned", "review_date": "2026-04-18", "helpful_votes": 12
| # | review_id | sku | reviewer_name | verified_buyer | star_rating | review_title |
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Complete list of extractable fields for Materials & Specs objects from manduka.com. All fields typed and schema-versioned.
"sku": "111011010", "weight_lbs": 7.5, "thickness_mm": 6.0, "material_type": "PVC", "latex_free": true, "closed_cell": true, "country_of_origin": "Germany"
| # | sku | weight_lbs | weight_kg | length_inches | width_inches | thickness_mm |
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Complete list of extractable fields for Inventory & Variants objects from manduka.com. All fields typed and schema-versioned.
"sku": "111011010", "variant_id": "394827162", "colour_name": "Black Magic", "size": "Standard 71"", "stock_status": "in_stock", "backorder_eligible": false
| # | sku | variant_id | colour_name | colour_hex | size | stock_status |
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Our scraper handles the dynamic structures of modern commerce platforms: variant hydration, nested product state objects, and complex category taxonomies.
Extract exact dimensions, weight, thickness, and material composition for PRO, eKO, and Foundation series mats.
Capture men's and women's activewear details, including size availability, fabric blends, and fit guides.
Map parent SKUs to child variants across all colourways and lengths. Track specific variant stock levels.
Monitor base prices, markdown events, and bundle discounts across the entire equipment and apparel range.
Extract eco-friendly material claims, OEKO-TEX certifications, and zero-waste manufacturing details.
Pull full review text, star ratings, and verified buyer tags to analyse customer sentiment on specific products.
Track in-stock, out-of-stock, and backorder status for every individual variant.
Identify which products carry the PRO series lifetime guarantee versus standard warranty terms.
Run pipelines daily or weekly to track new product drops, seasonal colour additions, and price changes.
Brief in. Clean data out.
Provide target categories or specific product URLs. We map the required data fields.
We configure crawlers to handle dynamic page loads and extract embedded JSON state objects.
Schema validation, null-rate checks, and variant mapping verification before production.
Structured records pushed to your S3 bucket, BigQuery dataset, or via Webhook.
Modern storefronts hide data in complex state objects and API calls. We handle the technical extraction so you get clean tables.
Product variants, stock levels, and pricing rules are often stored in complex JavaScript state objects rather than the DOM. Our parsers extract and normalise these internal data structures directly.
A single product page might contain 30 colour and size combinations. We expand these into distinct, queryable rows, ensuring no variant data is lost in nested arrays.
Commerce platforms enforce strict rate limits. We distribute requests across our proxy network and optimise request timing to maintain high throughput without triggering blocks.
Reviews and related products often require user interaction to load. We use Playwright to simulate scrolling and clicking, capturing data that static HTML parsers miss.
Storefront updates can break extraction rules. Our automated tests detect schema drift and alert our engineers to update selectors before your downstream pipelines fail.
Fitness retailers track Manduka's pricing strategy, discount frequency, and bundle offers to optimise their own pricing models.
Analysts monitor product line expansion, material trends, and seasonal colour introductions in the premium yoga segment.
Product teams analyse Manduka review data to understand customer preferences regarding mat thickness, grip, and durability.
Brands track Manduka's eco-friendly material claims and certifications to benchmark their own sustainability initiatives.
Track out-of-stock frequencies across specific mat lengths and colours to identify supply constraints or high-demand variants.
Wholesale buyers use complete catalogue data to plan their seasonal purchasing and optimise floor space allocation.
"Manduka represents the premium tier of yoga equipment. Extracting their material specifications, pricing models, and inventory depth provides direct visibility into high margin fitness retail."
Most teams underestimate the complexity of modern headless commerce scraping. Reliable Manduka extraction requires handling dynamic variant hydration, nested JSON state objects, and strict rate limits. DataFlirt absorbs that infrastructure overhead so your engineers can focus on data modelling.
Everything supported by our manduka.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.
We bypass fragile DOM selectors by extracting data directly from internal JavaScript state objects, ensuring schema stability during site redesigns.
Playwright handles lazy-loaded images, dynamic review widgets, and complex variant selection logic that standard HTTP clients fail to render.
Raw commerce data is cleaned, typed, and normalised into predictable warehouse-ready schemas before delivery.
Data delivered to where your team already works — no new tooling required.
About manduka.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We map every possible combination of colour and size to its specific variant ID, capturing the unique price, stock status, and image URL for each child SKU.
We extract the complete review corpus for each product, paginating through all available reviews to capture text, ratings, dates, and verified buyer status.
Yes. We capture the current inventory state for every variant. By running pipelines on a schedule, you can build a time-series dataset of stock availability.
We parse the product descriptions and specification tabs to extract specific material types, weight, dimensions, and sustainability certifications.
Pipelines can run on daily, weekly, or custom schedules depending on your requirements for tracking price changes and inventory fluctuations.
We deliver structured data in JSON, CSV, or Parquet formats, pushed directly to your S3 bucket, data warehouse, or via Webhook for real-time integration.
20-minute scoping call. Pilot dataset within the week. Production within two. Specify your target categories and data fields. We deploy the infrastructure and deliver clean product records to your warehouse.
