We extract product listings, supplement facts panels, ingredient lists, and variant matrices from Dymatize. 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 dymatize.com. All fields typed and schema-versioned.
"sku": "DYM-ISO100-5LB", "title": "ISO100 Hydrolyzed Protein Powder", "category": "Protein", "sub_category": "Whey Isolate", "base_price": 79.99, "currency": "USD", "average_rating": 4.8, "review_count": 3412
| # | sku | title | category | sub_category | base_price | currency |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Supplement Facts objects from dymatize.com. All fields typed and schema-versioned.
"sku": "DYM-ISO100-5LB-CHOC", "serving_size": "1 Scoop (32g)", "servings_per_container": 71, "calories": 120, "protein_g": 25, "bcaa_g": 5.5, "leucine_g": 2.7, "allergens": "['Milk', 'Soy']"
| # | sku | serving_size | servings_per_container | calories | protein_g | carbs_g |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Flavour Variants objects from dymatize.com. All fields typed and schema-versioned.
"parent_sku": "DYM-ISO100", "variant_sku": "DYM-ISO100-5LB-CHOC", "flavour": "Gourmet Chocolate", "weight_lbs": 5.0, "price": 79.99, "in_stock": true, "stock_status_text": "In Stock", "upc": "705016353112"
| # | parent_sku | variant_sku | flavour | weight_lbs | weight_g | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Customer Reviews objects from dymatize.com. All fields typed and schema-versioned.
"review_id": "REV-982341", "sku": "DYM-ISO100-5LB-CHOC", "reviewer_name": "John D.", "star_rating": 5, "review_title": "Mixes perfectly", "review_date": "2026-03-12", "verified_buyer": true, "helpful_votes": 14
| # | review_id | sku | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Store Locator objects from dymatize.com. All fields typed and schema-versioned.
"store_id": "LOC-4821", "store_name": "Vitamin Shoppe #142", "address_line_1": "123 Fitness Blvd", "city": "Austin", "state": "TX", "zip_code": "78701", "latitude": 30.2672, "retailer_type": "Specialty Nutrition"
| # | store_id | store_name | address_line_1 | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Dymatize scraper parses complex HTML supplement tables, JavaScript variant dropdowns, and store locator APIs to deliver structured catalogue data.
We extract and normalise macronutrients, micronutrients, and amino acid profiles from complex HTML table structures into flat JSON.
Parse comma-separated ingredient lists and bolded allergen warnings to build queryable compliance databases.
Iterate through JavaScript-rendered dropdowns to capture every combination of flavour, size, and pricing variant.
Query the underlying store locator APIs to map retail distribution networks across states and zip codes.
Extract paginated customer reviews to analyse sentiment on specific flavours, mixability, and digestion.
Monitor direct-to-consumer pricing changes on the official site to benchmark against third-party retailers.
Track out-of-stock statuses across specific variant combinations to identify supply chain constraints.
Extract workout routines, recipes, and nutritional articles published on the Dymatize content hub.
Run scheduled diffs on nutritional panels to detect silent formulation updates or ingredient substitutions.
Brief in. Clean data out.
Select target categories, specific product lines (like ISO100 or Elite), or store locator regions.
We configure Playwright to handle variant dropdowns and build custom parsers for the nutritional tables.
We test the parsers against edge cases, ensuring serving sizes and macro totals align correctly.
Structured data is pushed to your preferred destination in JSON, CSV, or Parquet format.
Supplement brands use complex frontends for nutritional tables and variant selection. We handle the parsing logic so you receive flat, queryable records.
Nutritional panels are built with nested divs and inconsistent HTML structures. We use custom extraction rules to map rows like 'Total Fat' and 'Protein' into strict schema fields, handling variations in serving size formats.
Selecting a new flavour or tub size often triggers a JavaScript event that updates the price, SKU, and nutritional panel. We use Playwright to systematically select every combination and capture the resulting state.
Instead of scraping the visual map for store locations, we intercept the underlying XHR requests to the store locator API, extracting clean JSON payloads containing exact coordinates and retailer metadata.
Supplement brands occasionally update their formulas. We hash the ingredient lists and macro profiles during each run, emitting a diff only when a product's nutritional profile changes.
Dymatize is a smaller brand site compared to massive marketplaces. We configure our crawlers with polite concurrency limits and caching layers to extract data without impacting their server performance.
Rival supplement brands map Dymatize's macro profiles, pricing per serving, and amino acid ratios to position their own products.
Distributors cross-reference the official MSRP and product imagery against their own catalogues to ensure accurate listings.
Food scientists track the use of specific sweeteners, emulsifiers, and protein blends across the Dymatize product line.
Analysts monitor flavour availability and new product launches to gauge consumer preferences in the sports nutrition sector.
Marketing teams aggregate reviews to determine which flavours are highly rated and which suffer from mixability complaints.
Sales teams scrape the store locator to identify key retail partners and regional distribution gaps.
"Nutritional panels are notoriously difficult to parse from DOM structures. We convert complex supplement facts into flat, queryable schemas."
Most engineering teams waste weeks writing custom regex for supplement tables. DataFlirt maintains stable parsers for Dymatize's HTML structures, handling variant matrices and ingredient lists automatically. You get clean data without the maintenance overhead.
Everything supported by our dymatize.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 queue and deduplication, while Playwright executes the JavaScript required to load dynamic nutritional panels and variant selectors.
We deploy specialised Python parsers designed specifically for FDA-style supplement fact tables, ensuring accurate extraction of nested serving data.
Pipelines run on AWS infrastructure, orchestrated by Apache Airflow. All state and historical diffs are stored securely in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About dymatize.com scraping, legality, and pipeline operations.
Ask us directly →Extracting publicly available product information, nutritional facts, and store locations is generally permissible. DataFlirt only targets public endpoints and does not bypass authentication walls. Clients should review applicable terms of service and consult legal counsel for specific use cases.
Highly accurate. We use deterministic parsers tailored to the specific DOM structure of Dymatize's nutritional tables. We run automated validation checks to ensure macro totals align with the stated caloric values.
Yes. Our crawlers use Playwright to systematically select every option in the variant dropdowns, capturing the specific SKU, price, and nutritional profile for that exact combination.
For a catalogue of this size, we can easily run daily or weekly refreshes depending on your requirements. Most clients opt for weekly runs to monitor formulation and price changes.
We deliver data in JSON, CSV, Parquet, and XLS. We can push directly to AWS S3, Google BigQuery, Snowflake, or send payloads via Webhook.
Yes. We provide sample datasets containing a subset of products during the scoping phase, allowing you to verify the schema and extraction quality before proceeding.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous tracking of formulation changes across their product line, we build and operate the pipeline. Tell us what you need.
