We extract product reviews, specification tables, editorial scores, buying guides, and affiliate link data from Digital Trends. 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 Reviews objects from digitaltrends.com. All fields typed and schema-versioned.
"url": "https://www.digitaltrends.com/mobile/apple-iphone-15-pro-review/", "title": "Apple iPhone 15 Pro review: the best iPhone in years", "review_score": 9, "pros": "['Lighter titanium build', 'USB-C port', 'Excellent cameras']", "cons": "['Battery life is just okay', 'Action Button needs more options']", "verdict": "The iPhone 15 Pro is a meaningful upgrade that refines the core experience.", "msrp": 999.0
| # | url | title | subtitle | author | publish_date | review_score |
|---|---|---|---|---|---|---|
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| 2 | ||||||
| 3 |
Complete list of extractable fields for News Articles objects from digitaltrends.com. All fields typed and schema-versioned.
"url": "https://www.digitaltrends.com/computing/intel-meteor-lake-release-date-specs-pricing/", "headline": "Intel Core Ultra: everything you need to know about Meteor Lake", "author": "Jacob Roach", "publish_date": "2023-12-14T14:00:00Z", "category": "Computing", "tags": "['Intel', 'Processors', 'Meteor Lake', 'Hardware']"
| # | url | headline | subheadline | author | publish_date | category |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Buying Guides objects from digitaltrends.com. All fields typed and schema-versioned.
"url": "https://www.digitaltrends.com/mobile/best-smartphones/", "title": "The best smartphones in 2024", "last_updated": "2024-01-15T09:30:00Z", "category": "Mobile", "top_pick_name": "Samsung Galaxy S24 Ultra", "top_pick_price": 1299.0, "affiliate_links": "['https://go.redirectingat.com/?id=...']"
| # | url | title | last_updated | author | category | top_pick_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Deals & Offers objects from digitaltrends.com. All fields typed and schema-versioned.
"url": "https://www.digitaltrends.com/deals/best-buy-tv-deals-lg-c3-oled/", "product_name": "LG 65-inch C3 OLED TV", "original_price": 2099.0, "deal_price": 1599.0, "discount_pct": 23, "merchant": "Best Buy", "affiliate_url": "https://howl.me/..."
| # | url | title | product_name | original_price | deal_price | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Author Profiles objects from digitaltrends.com. All fields typed and schema-versioned.
"author_id": "andy-boxall", "name": "Andy Boxall", "role": "Senior Writer, Mobile", "twitter_handle": "@AndyBoxall", "article_count": 3412, "recent_articles": "['https://www.digitaltrends.com/mobile/...', 'https://www.digitaltrends.com/mobile/...']"
| # | author_id | name | role | bio | twitter_handle | linkedin_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our scraper handles the entire editorial catalogue: deep product reviews, dynamic deal tracking, and complex specification tables, with session management built in.
Capture the complete review text, editorial scores, pros, cons, and final verdicts for every hardware and software review.
Extract structured key-value pairs from complex HTML specification tables embedded within product reviews.
Track outgoing affiliate links to merchants like Amazon and Best Buy to map editorial coverage to retail channels.
Identify and aggregate products that receive the coveted Editors Choice award across all categories.
Scrape headlines, body text, publish dates, and author metadata for thousands of daily tech news updates.
Extract original prices, deal prices, and discount percentages from daily deals and buying guides.
Compile author profiles, publication history, and social handles to map key influencers in tech media.
Maintain the exact category and tag hierarchy used by Digital Trends for precise content classification.
Run continuous pipelines to capture new articles and deal updates within minutes of publication.
Brief in. Clean data out.
Provide categories, author names, or specific article types. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for digitaltrends.com.
Schema validation, null-rate checks, and sample article parsing before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Tech publications use complex DOM structures and dynamic content loading. Here is how our infrastructure maintains clean data extraction.
Digital Trends uses infinite scroll for category feeds and lazy loading for images and affiliate widgets. We use Playwright to simulate user scrolling, ensuring all asynchronous content hydrates before extraction.
Product specifications are often embedded in inconsistent HTML tables. Our parsers use heuristic matching to normalise these tables into clean JSON key-value pairs regardless of layout shifts.
Editorial links are wrapped in affiliate redirectors (like Skimlinks or Howl). We optionally follow these redirect chains to expose the final merchant URL and product ID.
Media sites frequently A/B test layouts. We employ multiple fallback chains per field, combining CSS selectors, XPath, and JSON-LD metadata extraction to maintain pipeline stability.
To prevent IP bans during bulk historical extraction, we distribute requests across a pool of US residential proxies with randomised timing profiles.
Brands track product reviews, sentiment, and editorial scores to measure the impact of their PR campaigns.
Marketing teams analyse outbound affiliate links to understand which merchants and products tech publishers prioritise.
Hardware manufacturers aggregate pros, cons, and review scores to feed sentiment analysis models for future product development.
Content teams scrape buying guides and category structures to reverse-engineer successful tech SEO strategies.
Analysts track publication frequency across specific technology tags (e.g., AI, VR) to identify emerging consumer tech trends.
Retailers monitor deal articles to see which competitor discounts are receiving media amplification.
"Digital Trends produces thousands of high-signal hardware reviews and deal alerts, providing critical sentiment data for the consumer electronics market."
Extracting structured data from media sites requires handling inconsistent article layouts, lazy-loaded affiliate widgets, and infinite scroll feeds. DataFlirt manages the complete extraction lifecycle, delivering clean, normalised review and pricing data directly to your warehouse.
Everything supported by our digitaltrends.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 and deduplication. Playwright manages JavaScript execution for infinite scroll and lazy-loaded affiliate widgets.
We route requests through US residential proxy pools to bypass rate limits and ensure location-specific deal pricing is accurate.
Pipelines run on AWS ECS. Airflow manages scheduling for daily news updates, ensuring fresh data is delivered within defined SLA windows.
Data delivered to where your team already works — no new tooling required.
About digitaltrends.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We specifically target the editorial summary boxes to extract the pros, cons, numeric score, and final verdict as structured data fields.
Yes. We can configure the crawler to follow affiliate redirect chains (e.g., Skimlinks) to provide the final destination URL and merchant name.
We can extract the entire public archive of Digital Trends by traversing their historical sitemaps and category pagination.
We capture the published deal price and any stated expiration dates. Continuous pipelines can revisit deal pages daily to detect when a price reverts or a product goes out of stock.
Our pipelines use multiple fallback selectors, including JSON-LD metadata extraction. If a visual layout changes, our system alerts us to null-rate spikes, and our engineers update the parsers immediately.
Yes. We can scope the extraction pipeline to target specific category URLs, tags, or even specific authors based on your requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full historical archive of product reviews or a daily feed of tech deals, we build and manage the extraction infrastructure. Contact us to define your schema.
