We extract fitness product listings, material specs, pricing signals, inventory levels, and reviews from Gaiam. 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 gaiam.com. All fields typed and schema-versioned.
"sku": "05-64478", "title": "Premium Print Yoga Mat (6mm)", "category": "Yoga", "sub_category": "Yoga Mats", "price": 34.98, "currency": "USD", "thickness_mm": 6.0, "material_composition": "PVC. Latex-free. 6P Free.", "in_stock": true, "rating": 4.6, "review_count": 412
| # | sku | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Inventory objects from gaiam.com. All fields typed and schema-versioned.
"sku": "05-64478", "base_price": 34.98, "sale_price": 29.98, "discount_pct": 14, "currency": "USD", "stock_status": "IN_STOCK", "promotion_badge": "SALE", "scraped_at": "2026-05-12T10:15:00Z"
| # | sku | base_price | sale_price | discount_pct | currency | stock_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from gaiam.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "sku": "05-64478", "reviewer_name": "Sarah M.", "rating": 5, "review_title": "Perfect thickness for hard floors", "review_text": "Provides great cushioning for my knees during practice.", "helpful_votes": 12, "verified_buyer": true, "date_posted": "2026-03-14"
| # | review_id | sku | reviewer_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Variants & Options objects from gaiam.com. All fields typed and schema-versioned.
"parent_sku": "05-64478", "variant_sku": "05-64478-BLU", "colour_name": "Midnight Blue", "colour_hex": "#191970", "size": "Standard", "thickness_mm": 6.0, "price_modifier": 0.0, "stock_status": "IN_STOCK"
| # | parent_sku | variant_sku | colour_name | colour_hex | size | thickness_mm |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Categories & Taxonomy objects from gaiam.com. All fields typed and schema-versioned.
"category_id": "cat_yoga_mats", "category_name": "Yoga Mats", "parent_category": "Yoga", "breadcrumb": "Home > Yoga > Yoga Mats", "url_slug": "/collections/yoga-mats", "product_count": 142, "meta_title": "Yoga Mats | Gaiam", "active": true
| # | category_id | category_name | parent_category | breadcrumb | url_slug | product_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Gaiam scraper handles category pagination, dynamic variant loading, and customer review extraction. Built with residential proxies and schema validation to ensure clean data delivery.
Extract thickness, material composition, dimensions, and care instructions across all hardgoods.
Capture parent-child relationships for clothing, including size, colour options, and variant-specific pricing.
Monitor base price, sale price, and promotional badges across the entire product catalogue.
Track in-stock status and inventory indicators to monitor product availability and backorder dates.
Paginate through product reviews to capture text, ratings, verified buyer flags, and helpful votes.
Extract high-resolution image URLs and instructional video links associated with products.
Map the full site structure, capturing breadcrumbs and sub-category hierarchies for correct product placement.
Extract product rankings and visibility metrics for specific fitness keywords on the internal search engine.
Run pipelines daily or weekly to capture catalogue additions, price changes, and new reviews.
Brief in. Clean data out.
Provide target categories, product URLs, or search terms. We design the extraction schema together.
We configure Scrapy crawlers, handle Shopify variant hydration, and set up proxy rotation.
Schema validation, null-rate checks, and data normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
Extracting structured data from modern eCommerce storefronts requires handling dynamic state and rate limits. Here is how we build resilience.
Gaiam loads variant pricing and stock status dynamically. We intercept backend API calls to extract the full JSON payload for all product variants, bypassing the need for slow browser automation.
Customer reviews are loaded via third-party widgets. Our pipeline targets the underlying review API directly, paginating through thousands of records without rendering the DOM.
Frequent catalogue requests trigger IP blocks. We utilise residential proxy pools to distribute requests, ensuring uninterrupted extraction during full-site crawls.
Material details are often buried in unstructured description text. We use regex and NLP to parse and normalise thickness, material type, and dimensions into queryable columns.
We hash product records to detect changes in price or stock. Downstream deliveries only contain updated rows, optimising your warehouse compute costs.
Fitness retailers monitor Gaiam's pricing and promotional cadences to adjust their own pricing strategies.
Merchandisers analyse Gaiam's category breadth, colour variants, and material choices to identify market trends.
Analysts track new product launches and category expansion to evaluate the fitness accessories market.
Product developers extract material compositions (e.g., TPE vs PVC) and correlate them with customer ratings.
Brands mine Gaiam product reviews to identify common complaints regarding durability or grip, informing their own product design.
Data science teams use the structured catalogue to train product categorization and recommendation models.
"Gaiam holds the definitive catalogue for yoga and fitness accessories, but extracting structured material specs and variant data requires a dedicated pipeline."
Fitness retailers underestimate the complexity of scraping variant-heavy storefronts. Extracting accurate material compositions, dynamic stock levels, and paginated review data requires continuous maintenance. DataFlirt handles the infrastructure so your team can focus on assortment analysis.
Everything supported by our gaiam.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 concurrent crawling, request retries, and data export. Custom middlewares manage rate limits and proxy rotation.
We route traffic through residential IP networks to prevent blocking and ensure consistent access to the catalogue.
Airflow schedules extraction jobs, validates schemas, and pushes the final dataset to your designated cloud storage.
Data delivered to where your team already works — no new tooling required.
About gaiam.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We configure pipelines to target specific URLs, categories, or search terms, ensuring you only pay for the data you need.
We extract the parent product data and map all child variants (e.g., different colours or 4mm vs 6mm thickness) as separate records or nested JSON objects, depending on your schema preference.
Yes. We target the underlying review widget API to paginate through all available reviews for a product, capturing text, ratings, and helpful votes.
Pipelines can be scheduled daily, weekly, or monthly. Daily runs are typical for monitoring pricing and stock availability.
We extract the high-resolution URLs for all product images and videos. We do not host the media files, but provide the direct links in the dataset.
We push formatted files (JSON, CSV, Parquet) directly to your AWS S3 bucket, Snowflake stage, or provide access via API.
20-minute scoping call. Pilot dataset within the week. Production within two. Scope your category requirements and let DataFlirt handle the extraction infrastructure. Get clean product data delivered directly to your warehouse.
