We extract electronics listings, discount signals, seller ratings, and daily deals from Snapdeal. 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 Listings objects from snapdeal.com. All fields typed and schema-versioned.
"supc": "SDL123456789", "title": "Boat Rockerz 255 Pro+ Wireless Neckband", "brand": "boAt", "category": "Electronics", "price": 1299.0, "mrp": 3990.0, "discount_pct": 67, "rating": 4.1, "review_count": 4521, "in_stock": true
| # | supc | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from snapdeal.com. All fields typed and schema-versioned.
"supc": "SDL123456789", "price": 1299.0, "mrp": 3990.0, "discount_pct": 67, "bank_offers": "['10% Instant Discount on HDFC Cards']", "cod_available": true, "delivery_charge": 0.0, "price_timestamp": "2026-05-12T09:14:00Z"
| # | supc | price | mrp | discount_pct | bank_offers | emi_options |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from snapdeal.com. All fields typed and schema-versioned.
"review_id": "REV987654321", "supc": "SDL123456789", "star_rating": 5, "review_title": "Great bass and battery life", "review_date": "2026-04-18", "helpful_votes": 34, "verified_purchase": true
| # | review_id | supc | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Data objects from snapdeal.com. All fields typed and schema-versioned.
"seller_name": "Appario Retail", "seller_rating": 4.5, "seller_score": 92, "fulfillment_type": "Snapdeal Fulfilled", "return_policy": "7 Days Return", "active_listings": 1250
| # | seller_name | seller_rating | seller_score | supc | fulfillment_type | ships_from |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from snapdeal.com. All fields typed and schema-versioned.
"keyword": "wireless earphones", "position": 1, "supc": "SDL123456789", "title": "Boat Rockerz 255 Pro+", "price": 1299.0, "rating": 4.1, "discount_pct": 67, "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | position | supc | title | price | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Snapdeal scraper handles every layer of the platform: product listings, daily deals, seller intelligence, and the review corpus. We build JavaScript rendering, session management, and anti-bot circumvention directly into the pipeline.
Title, highlights, description, specifications, images, and every metadata field Snapdeal surfaces, scraped at SUPC level.
Capture price, MRP, daily deal tags, bank offers, EMI options, and delivery charges, timestamped per crawl.
Full review text, star ratings, helpful vote counts, and verified purchase flags, paginated across all review pages.
Seller name, rating score, fulfillment type, and return policy for every offer on a listing.
Track organic position for any keyword or category, capturing rank movement over time.
Check stock status, delivery timelines, and cash-on-delivery eligibility across specific target pincodes.
Monitor flash sale windows, discount percentages, and promotional banners across the electronics category.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Extract colour and storage variations, linking child products to their parent category structure.
Brief in. Clean data out.
Provide SUPC lists, category URLs, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for snapdeal.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Snapdeal uses dynamic loading and aggressive rate limits. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Snapdeal limits aggressive IP scraping. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Snapdeal product pages and search results rely on JavaScript for pricing and stock updates. We run full Playwright browser sessions with JavaScript execution and lazy-load triggering.
Our selector strategy uses multiple fallback chains per field, including CSS selectors, XPath, and JSON data extraction, so a layout change does not break your data pipeline.
Delivery charges and stock availability vary by location. We inject specific pincodes into the session state to extract accurate, localised pricing and delivery timelines.
For large catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
eCommerce brands monitor pricing and daily deals to reprice their own catalogues and protect margins.
Analysts track sub-category saturation trends to identify whitespace and investment opportunities in tier-2 markets.
Brands audit sellers for MAP violations, counterfeit listings, and unauthorised resellers.
ML teams use Snapdeal datasets to train recommendation engines, NLP classifiers, and sentiment models.
Supply chain teams correlate review velocity and stock depth indicators with sales velocity to improve procurement models.
Analysts track category leaders, seller growth curves, and review-to-rating ratios to evaluate marketplace performance.
"Snapdeal holds critical pricing signals for value-conscious consumers in tier-2 markets. Extracting that data requires a dedicated pipeline."
Most teams underestimate the investment required: reliable Snapdeal scraping requires residential proxies, full JavaScript rendering for dynamic pricing, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our snapdeal.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, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across India. 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 snapdeal.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Snapdeal is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review platform terms and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for CAPTCHA rate spikes in real time and trigger pool rotation or solver queues automatically.
Yes. Delivery charges and stock availability vary by location on Snapdeal. We inject target pincodes into the session state to extract accurate, localised pricing and delivery timelines.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined SUPC set. Full catalogue refreshes at daily cadence complete within a 6-12 hour window depending on size.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per seller for rating scores, active listings, and feedback metrics from the date your pipeline starts.
Our smallest packages start at a defined SUPC list (typically 1,000 to 50,000 items) with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
Yes. We provide a sample run of up to 500 products or 50 search result pages as part of the pre-engagement scoping process. This lets you 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 500K listings, we scope, build, and operate the pipeline. Tell us what you need.
