Extract comprehensive mobile specifications, cross-store price comparisons, and expert ratings from Gadgets360. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery on your schedule.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Device Specifications objects from gadgets360.com. All fields typed and schema-versioned.
"brand": "Samsung", "model": "Galaxy S24 Ultra", "release_date": "2024-01-17", "battery_capacity": "5000 mAh", "processor": "Snapdragon 8 Gen 3", "ram": "12GB", "storage": "256GB"
| # | device_id | brand | model | release_date | form_factor | dimensions |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Price Comparisons objects from gadgets360.com. All fields typed and schema-versioned.
"model": "Galaxy S24 Ultra", "variant": "12GB RAM, 256GB", "store_name": "Amazon", "store_price": 129999.0, "currency": "INR", "in_stock": true, "timestamp": "2024-10-24T12:00:00Z"
| # | device_id | model | variant | store_name | store_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Expert Reviews objects from gadgets360.com. All fields typed and schema-versioned.
"reviewer_name": "Roydon Cerejo", "gadgets360_rating": 9, "performance_rating": 10, "battery_rating": 9, "pros": "['Great display', 'Excellent cameras']", "cons": "['Expensive', 'Bulky']", "verdict": "The best Android phone you can buy right now."
| # | review_id | device_id | reviewer_name | publish_date | gadgets360_rating | design_rating |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for User Ratings objects from gadgets360.com. All fields typed and schema-versioned.
"user_name": "Rahul Sharma", "rating_value": 4.5, "review_title": "Solid performance", "review_text": "Battery life is excellent, but charging is a bit slow.", "date_posted": "2024-02-15", "helpful_votes": 12
| # | rating_id | device_id | user_name | rating_value | review_title | review_text |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Tech News & Articles objects from gadgets360.com. All fields typed and schema-versioned.
"headline": "Samsung Galaxy S24 Ultra Review: The Ultimate Android Flagship?", "author": "Sheldon Pinto", "publish_date": "2024-01-30", "category": "Reviews", "tags": "['Samsung', 'Smartphones', 'Android']", "related_devices": "['Samsung Galaxy S24 Ultra']"
| # | article_id | headline | author | publish_date | category | tags |
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Extract deep hardware specifications, cross-platform pricing data, and expert editorial scores without managing DOM inconsistencies and JavaScript rendering.
Extract detailed hardware and software specifications for mobiles, tablets, laptops, and wearables. Normalised across categories.
Capture prices from Amazon, Flipkart, Croma, and Reliance Digital as displayed on Gadgets360 comparison widgets.
Scrape overall scores and sub-scores for design, display, software, performance, battery life, and cameras.
Extract editorial verdicts, pros, cons, and paginated user reviews with helpful vote counts.
Monitor rumoured specifications, expected launch dates, and anticipated pricing for unreleased hardware.
Map distinct RAM, storage, and colour variants to their parent device models for accurate price tracking.
Scrape tech news articles, feature stories, and buying guides with author metadata and publication timestamps.
Execute full browser sessions to render dynamic price comparison tables and lazy-loaded specification blocks.
Run diffs against historical data to only push updates when a device price drops or a new review is published.
Brief in. Clean data out.
Provide target categories, brands, or specific device URLs. We map the extraction schema together.
We configure Scrapy crawlers, handle Gadgets360's pagination, and set up proxy rotation.
Schema validation, missing field detection, and price outlier checks before full deployment.
JSON, CSV, or Parquet pushed to your S3 bucket or BigQuery dataset on your defined schedule.
Scraping consumer tech data requires navigating inconsistent schemas and dynamic pricing widgets. We manage the complexity.
Gadgets360 loads multi-store price comparisons via client-side JavaScript. We use Playwright to execute and wait for these network requests to resolve before parsing.
Different device categories have completely different specification tables. We normalise these into a consistent JSON schema, handling missing fields gracefully.
News feeds and user review sections rely on infinite scroll and AJAX pagination. Our crawlers simulate user scroll events to extract the complete corpus.
We route requests through Indian residential proxies with realistic TLS fingerprints to avoid rate limits and IP bans during high-frequency price monitoring.
Raw strings like '5000 mAh' or '12 GB' are parsed into typed numeric fields for direct loading into your data warehouse.
Retailers and brands track Gadgets360 price comparisons to monitor market positioning against Amazon and Flipkart.
Hardware manufacturers analyse specification trends, battery capacities, and camera megapixels across price segments.
NLP teams process expert pros/cons and user reviews to gauge public reception of new device launches.
eCommerce platforms supplement their own product listings with Gadgets360's exhaustive hardware specifications.
Marketing agencies monitor outbound affiliate links and store visibility on Gadgets360 comparison pages.
LLM developers train hardware recommendation models using Gadgets360's structured review and specification corpus.
"Gadgets360 holds the most structured consumer electronics database in India, but standard HTTP clients miss the dynamic pricing data entirely."
Extracting accurate specification matrices and real-time price comparisons requires executing JavaScript and parsing highly variable DOM structures across device categories. DataFlirt manages this infrastructure so your team can focus on market analysis, not crawler maintenance.
Everything supported by our gadgets360.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 orchestration and deduplication. Playwright executes JavaScript to load price comparison widgets and infinite scroll feeds.
We route requests through Indian residential IPs to mirror real user traffic, bypassing regional blocks and rate limits.
Pipelines run on AWS ECS with Airflow scheduling. Postgres maintains state for change-detection diffing.
Data delivered to where your team already works — no new tooling required.
About gadgets360.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available factual data like specifications and prices is generally permissible. DataFlirt extracts only public information and does not bypass authentication or scrape personal data.
Yes. We extract the full multi-store comparison table, including prices from Amazon, Flipkart, Croma, and official brand stores.
Our extraction schema dynamically adapts to the device category. Mobile phones include camera and battery specs, while laptops include GPU and port configurations. We normalise these into a consistent JSON structure.
Yes. We track the Upcoming sections to extract expected specifications, rumoured prices, and anticipated launch dates.
We can configure pipelines to run at daily or hourly cadences depending on your requirements. The data reflects the exact prices displayed on Gadgets360 at the time of the run.
We build a time-series database from the moment your pipeline starts, allowing you to track price drops and variant availability over time.
Yes. We capture the overall Gadgets360 rating as well as sub-scores for design, display, software, performance, battery life, and value for money.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full dump of historical mobile specifications or daily price comparison updates, we build and operate the infrastructure. Contact us to define your schema.
