We extract sporting goods catalogues, pricing signals, store-level inventory, and product reviews from Rebel Sport. 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 Footwear & Apparel objects from rebel.com.au. All fields typed and schema-versioned.
"sku": "59812401", "title": "Nike Air Zoom Pegasus 40 Mens Running Shoes", "brand": "Nike", "category": "Mens Footwear", "price": 199.99, "colours": "['Black/White', 'Grey/Blue']", "sizes": "['US 8', 'US 9', 'US 10', 'US 11']"
| # | sku | title | brand | category | sub_category | price |
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Complete list of extractable fields for Pricing & Promos objects from rebel.com.au. All fields typed and schema-versioned.
"sku": "59812401", "price": 199.99, "list_price": 220.0, "discount_pct": 9, "member_price": 189.99, "promo_text": "Save $20", "clearance_flag": false, "currency": "AUD"
| # | sku | price | list_price | discount_pct | member_price | promo_text |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Store Inventory objects from rebel.com.au. All fields typed and schema-versioned.
"sku": "59812401", "store_id": "RBL-NSW-104", "store_name": "Rebel Sydney CBD", "state": "NSW", "postcode": "2000", "stock_status": "In Stock", "click_collect_eligible": true
| # | sku | store_id | store_name | state | postcode | stock_status |
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Complete list of extractable fields for Reviews & Ratings objects from rebel.com.au. All fields typed and schema-versioned.
"review_id": "REV-891234", "sku": "59812401", "star_rating": 4.5, "review_title": "Great daily trainer", "reviewer_nickname": "RunnerDan", "review_date": "2025-10-14", "recommended": true
| # | review_id | sku | star_rating | review_title | review_body | reviewer_nickname |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Categories objects from rebel.com.au. All fields typed and schema-versioned.
"category_url": "https://www.rebelsport.com.au/mens/footwear/running-shoes", "breadcrumb_1": "Mens", "breadcrumb_2": "Footwear", "breadcrumb_3": "Running Shoes", "total_results": 412, "page_number": 1, "sort_applied": "Recommended"
| # | category_url | breadcrumb_1 | breadcrumb_2 | breadcrumb_3 | total_results | page_number |
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Our Rebel Sport scraper handles every layer of the platform: product catalogues, dynamic pricing, store-level inventory, and the review corpus - with JavaScript rendering, session management, and anti-bot circumvention built in.
Title, description, dimensions, colours, sizing matrices, and every metadata field Rebel surfaces - scraped at SKU level.
Capture price, list price, promo text, clearance flags, and Rebel Active member pricing - timestamped per crawl.
Extract Click & Collect availability and stock status across specific postcodes or all Australian store locations.
Full review text, star ratings, helpful vote counts, and recommendation flags - paginated across all review pages.
Map the entire category hierarchy from Fan Gear to Fitness Equipment, including total result counts and active filters.
Monitor discount windows and percentage-off promotions across thousands of SKUs to track retail markdown cycles.
Link parent product URLs to all child SKUs, ensuring complete coverage of every size and colour combination available.
Track organic position for any keyword or brand query, capturing default sort order and product visibility.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, brand names, or SKU lists. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and API interception for rebel.com.au.
Schema validation, null-rate checks, price-outlier detection, and sample variants before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Retail sites use aggressive caching and dynamic inventory loading. Here is how we maintain data accuracy.
Retail CDNs block data centre IPs aggressively. Our crawlers use Australian residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass perimeter defences.
Rebel Sport loads store-level inventory asynchronously. We intercept the backend API calls with specific location payloads to extract accurate Click & Collect stock status without rendering the full DOM.
Apparel and footwear require complex variant mapping. We extract the full JSON state embedded in the page source to link every size and colour combination to its specific SKU and price.
For large catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops - and respond before you notice.
Sporting goods retailers monitor Rebel Sport pricing, promotional windows, and clearance events to adjust their own pricing strategies.
Merchandising teams analyse brand representation, category depth, and size availability to inform their own buying decisions.
Analysts track store-level stock status to map regional demand and identify supply chain bottlenecks across specific postcodes.
Major apparel brands audit their product listings for MAP violations, correct imagery, and accurate product descriptions.
Firms track new product launches, category expansion, and review sentiment to evaluate the Australian sporting goods market.
ML teams use structured product descriptions, features, and categorisation to train retail-specific NLP models and recommendation engines.
"Rebel Sport holds the definitive catalogue of Australian sporting goods and apparel pricing, but extracting store-level inventory requires intercepting regional API calls at scale."
Retail scraping fails when crawlers cannot handle dynamic product variants and regional stock APIs. DataFlirt manages the residential proxies, JavaScript execution, and schema maintenance required to extract accurate pricing and inventory data at scale. You receive clean data, not scraping headaches.
Everything supported by our rebel.com.au 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 crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across AU regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About rebel.com.au scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Rebel Sport is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We use Australian residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for block rate spikes in real time and trigger pool rotation automatically.
Full catalogue refreshes at daily cadence complete within a 4-8 hour window depending on size. Sub-category or specific brand tracking can run at hourly intervals.
Yes. We intercept the backend API calls to extract Click & Collect availability and stock levels across specific postcodes or all Australian store locations.
Our smallest packages start at a defined category or brand list with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process - so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off product catalogue dump or a continuous price-monitoring feed across the entire site - we scope, build, and operate the pipeline. Tell us what you need.
