We extract smartphone specifications, aggregated multi-store pricing, price history graphs, and user reviews from Mysmartprice. 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 Products & Specs objects from mysmartprice.com. All fields typed and schema-versioned.
"product_id": "MSP12045", "title": "Samsung Galaxy S24 Ultra", "brand": "Samsung", "category": "Mobile Phones", "expert_score": 8.9, "user_score": 4.5, "launch_date": "2024-01-17", "key_specs": "['Snapdragon 8 Gen 3', '12GB RAM', '200MP Camera']"
| # | product_id | title | brand | category | expert_score | user_score |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Aggregated Pricing objects from mysmartprice.com. All fields typed and schema-versioned.
"product_id": "MSP12045", "store_name": "Amazon", "price": 129999.0, "stock_status": "In Stock", "delivery_time": "2 Days", "offer_details": "Bank discount INR 5000", "timestamp": "2026-05-12T09:14:00Z", "currency": "INR"
| # | product_id | store_name | price | stock_status | delivery_time | offer_details |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from mysmartprice.com. All fields typed and schema-versioned.
"review_id": "REV98432", "product_id": "MSP12045", "user_name": "Rahul T.", "rating": 5, "review_text": "Excellent display and battery life.", "pros": "['Display', 'Battery']", "cons": "['Heavy']", "date": "2025-11-20"
| # | review_id | product_id | user_name | rating | review_text | pros |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Price History objects from mysmartprice.com. All fields typed and schema-versioned.
"product_id": "MSP12045", "date": "2025-10-01", "lowest_price": 124999.0, "highest_price": 129999.0, "average_price": 127500.0, "store_count": 4, "variant": "256GB"
| # | product_id | date | lowest_price | highest_price | average_price | store_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Upcoming Devices objects from mysmartprice.com. All fields typed and schema-versioned.
"device_name": "OnePlus 13", "expected_price": 69999.0, "expected_launch": "2026-01-15", "rumour_confidence": "High", "brand": "OnePlus", "category": "Mobile Phones", "leaked_specs": "['Snapdragon 8 Gen 4', '50MP Hasselblad']"
| # | device_name | expected_price | expected_launch | leaked_specs | rumour_confidence | brand |
|---|---|---|---|---|---|---|
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Our Mysmartprice scraper handles every layer of the platform: aggregated store pricing, detailed technical specifications, price history graphs, and user reviews with JavaScript rendering built in.
Extract deep technical specifications for mobiles, laptops, and appliances. Mapped into structured JSON fields for easy comparison.
Capture prices across Amazon, Flipkart, Croma, and Reliance Digital as aggregated by Mysmartprice. Timestamped per crawl.
Extract historical price data points rendered via JavaScript charts to track price drops and seasonal discounts.
Scrape expert review scores, detailed pros and cons, and paginated user reviews for sentiment analysis.
Track expected launch dates, leaked specifications, and estimated pricing for unreleased electronics.
Link storage, RAM, and colour variations to their parent product ID for accurate pricing analysis.
Maintain a hash index of last-seen values per field. Subsequent runs only push diffs to reduce downstream load.
Extract data across all categories: mobiles, tablets, laptops, TVs, audio, and home appliances.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Brief in. Clean data out.
Provide category URLs, specific product links, or brand filters. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and Playwright sessions for JavaScript chart rendering.
Schema validation, null-rate checks, and specification accuracy testing before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Extracting aggregated pricing requires rendering complex DOM structures. Here is how we stay resilient.
Mysmartprice relies on JavaScript to render price history graphs and dynamic store offers. We run full Playwright browser sessions to capture data that headless HTTP clients miss entirely.
We route requests through ISP-grade residential proxies with realistic browser fingerprints to avoid rate limits and IP bans during high-volume crawls.
Our selector strategy uses multiple fallback chains per field. If a layout change occurs in the specifications table, our extraction logic adapts without breaking your pipeline.
We maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and storage bloat for frequently updated pricing data.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and schema drift, responding before you notice.
Retailers monitor aggregated pricing across competitors to adjust their own pricing strategies dynamically.
Analysts track specification trends and pricing tiers to identify market gaps for new product launches.
Affiliate marketers analyse store visibility and offer structures to optimise their referral campaigns.
Machine learning teams use structured specification datasets to train product recommendation engines.
Brands monitor expert scores and user reviews of competing products to inform product development.
Product managers track upcoming device leaks and expected pricing to time their own product announcements.
"Mysmartprice aggregates the fragmented Indian electronics market into a single view, but extracting that multi-store pricing data requires resilient infrastructure."
Most teams underestimate the investment required: reliable Mysmartprice scraping requires residential proxies, full JavaScript rendering for price history charts, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our mysmartprice.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 handles JavaScript rendering for dynamic pricing charts.
We maintain pools of residential ISP proxies. Rotation happens per-request to prevent rate limiting.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About mysmartprice.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and specification data. Clients should consult legal counsel for specific use cases.
We use full Playwright browser sessions to execute the page JavaScript, allowing us to intercept the data points used to render the historical price charts.
Pipelines can be configured for daily or sub-daily runs depending on your requirements. Aggregated pricing changes are captured during each scheduled run.
Yes. We extract data from the upcoming devices section, including expected launch dates, leaked specifications, and estimated pricing.
Our packages start at a defined category list with weekly delivery. For larger catalogues, we price based on volume and delivery frequency.
Yes. We provide a sample run of up to 500 products as part of the scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off specification dump or a continuous price-monitoring feed across thousands of devices. Tell us what you need.
