We extract editorial reviews, product specifications, pricing deals, and tech news from CNET. 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 Editorial Reviews objects from cnet.com. All fields typed and schema-versioned.
"review_id": "cnet-rev-84920", "title": "Apple iPhone 15 Pro Max Review", "author": "Patrick Holland", "editor_rating": 8.9, "pros": "['Excellent battery life', 'Superb cameras', 'Titanium build']", "cons": "['Expensive', 'Slow charging speeds']", "verdict": "The iPhone 15 Pro Max is Apple's best phone yet, offering meaningful camera upgrades and a lighter chassis."
| # | review_id | url | title | author | publish_date | editor_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Product Specifications objects from cnet.com. All fields typed and schema-versioned.
"product_id": "spec-92817", "name": "Sony PlayStation 5", "brand": "Sony", "category": "Gaming Consoles", "processor": "Custom AMD Zen 2", "display_size": "Up to 8K output", "connectivity": "Wi-Fi 6, Bluetooth 5.1, Gigabit Ethernet"
| # | product_id | name | category | brand | release_date | dimensions |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Tech News Articles objects from cnet.com. All fields typed and schema-versioned.
"article_id": "news-10394", "headline": "OpenAI announces new GPT-4 Turbo model", "subheadline": "The updated model is faster and cheaper for developers.", "author": "Stephen Shankland", "published_date": "2026-11-06T14:30:00Z", "category": "Artificial Intelligence", "tags": "['OpenAI', 'ChatGPT', 'Machine Learning']"
| # | article_id | headline | subheadline | author | published_date | updated_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Deals & Pricing objects from cnet.com. All fields typed and schema-versioned.
"deal_id": "deal-58291", "product_name": "Samsung 65-inch Class S90C OLED TV", "retailer": "Best Buy", "original_price": 2599.99, "deal_price": 1599.99, "discount_pct": 38, "scrape_time": "2026-11-07T08:15:22Z"
| # | deal_id | product_name | retailer | original_price | deal_price | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Buying Guides objects from cnet.com. All fields typed and schema-versioned.
"guide_id": "bg-49102", "title": "Best Laptops for 2026", "category": "Computing", "last_updated": "2026-10-15T10:00:00Z", "author": "Joshua Goldman", "featured_products": "['MacBook Air M3', 'Dell XPS 13', 'Lenovo ThinkPad X1 Carbon']", "summary": "We test dozens of laptops every year to find the best options for students, professionals, and gamers."
| # | guide_id | title | category | last_updated | featured_products | ranking_order |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our CNET scraper extracts structured data across reviews, deep specifications, daily deals, and editorial content. We handle pagination, dynamic content loading, and bot protection automatically.
Extract numeric scores, pros, cons, and bottom-line verdicts from every editorial review published on the platform.
Capture granular technical details including dimensions, processor types, battery life, and connectivity options across all hardware categories.
Monitor CNET's deals section for real-time price drops, discount percentages, and active promo codes across external retailers.
Scrape full article bodies, headlines, publication dates, and author metadata for historical trend analysis or NLP training.
Track which products are featured in top 10 lists and buying guides to measure brand visibility and market positioning.
Map articles and reviews to specific journalists to analyse coverage bias, expertise areas, and publication frequency.
Extract community sentiment, user-generated scores, and comment threads attached to reviews and news articles.
Capture the dynamic pricing tables embedded in reviews showing current costs across Amazon, Best Buy, and Walmart.
Run scheduled pipelines that only extract newly published articles or recently updated reviews to minimise redundancy.
Crawl specific taxonomic trees such as Mobile, Computing, Home Entertainment, or Smart Home to narrow extraction scope.
Brief in. Clean data out.
Provide CNET category URLs, author profiles, or keyword sets. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and ad-tech bypass mechanisms.
Schema validation, null-rate checks, and sample data reviews before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Content publishers deploy complex layouts, aggressive ad-tech, and dynamic widgets. Here is how we maintain reliable extraction.
CNET embeds dynamic price comparison widgets that load via asynchronous JavaScript requests. We use Playwright to execute these scripts and intercept the underlying API calls, ensuring you capture real-time retailer pricing.
A smartphone review layout differs significantly from a vacuum cleaner review. Our extraction logic maps these disparate DOM structures into a single, unified JSON schema, standardising fields like pros, cons, and verdicts.
Media sites rely on aggressive advertising overlays and newsletter pop-ups that obscure content and break simple HTTP scrapers. Our browser sessions block ad domains at the network level, speeding up page loads and ensuring clean DOM access.
Extracting decades of tech news requires traversing complex pagination and infinite-scroll implementations. We maintain stateful crawlers that systematically iterate through category archives without missing records or getting trapped in loops.
CNET frequently updates existing buying guides and reviews to reflect new pricing or software updates. We hash the content of previously scraped URLs and only deliver records when a material change is detected in the text or score.
Hardware manufacturers monitor editor ratings and pros/cons across their product lines versus competitors to inform product development.
Machine learning teams use decades of structured tech journalism and reviews to train domain-specific language models and summarisation engines.
Publishers and marketers track which retailers CNET links to and what deals they promote to optimise their own affiliate strategies.
Financial analysts track review sentiment for major consumer electronics releases to predict sales performance and stock movement.
Product managers extract deep specification databases to map feature trends, such as battery capacity growth or camera megapixel inflation over time.
Media companies analyse CNET's buying guide structures, headline formats, and publication frequencies to inform their own editorial calendars.
"CNET holds decades of authoritative tech journalism and product specifications, but turning their unstructured pages into queryable market intelligence requires dedicated infrastructure."
Extracting CNET data involves navigating aggressive ad-tech overlays, varied article templates, and dynamic price comparison widgets. DataFlirt manages the proxy rotation, JavaScript execution, and schema mapping so your team receives clean, normalised data ready for immediate analysis.
Everything supported by our cnet.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 crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, ad-blocking, and dynamic widget hydration.
We maintain pools of residential ISP proxies to bypass basic bot protection. Rotation happens per request, ensuring high success rates.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About cnet.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from CNET, such as reviews and specifications, is generally permissible under applicable law. DataFlirt targets only public, non-authenticated editorial and product data. We do not extract personal user data or circumvent authentication walls. Clients should review CNET's Terms of Service and consult legal counsel for specific use cases.
We use headless browsers via Playwright to execute the JavaScript that loads these widgets. We intercept the underlying API calls made to their affiliate networks, extracting the exact pricing and retailer data presented to the user.
Yes. We can traverse CNET's sitemaps and category pagination to extract historical content dating back years, which is highly valuable for training language models or conducting longitudinal market research.
Our extraction logic uses multiple selector chains and fallback rules. If a review uses a 2024 layout, it applies one set of rules; if it is a legacy 2018 layout, it applies another. The final output is always mapped to your unified JSON schema.
For news monitoring, we can configure pipelines to poll specific category RSS feeds or index pages at sub-15-minute intervals, delivering new articles via Webhook almost immediately after publication.
This specific pipeline is optimised for cnet.com. However, we can build custom pipelines for affiliated properties or competitors using the same underlying infrastructure and delivery mechanisms.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a historical dump of tech reviews or a continuous feed of daily hardware deals, we scope, build, and operate the pipeline. Tell us what you need.
