We extract product listings, pricing signals, category rankings, seller intelligence, EMI options, reviews, and Q&A from Flipkart. 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 flipkart.com. All fields typed and schema-versioned.
"fsn": "MOBGTAGPTB3VS24N", "title": "Samsung Galaxy S24 FE 5G (Blue, 8GB RAM, 256GB Storage)", "brand": "Samsung", "price": 44999.00, "mrp": 54999.00, "discount_pct": 18, "flipkart_assured": true, "category_rank": 6, "rating": 4.3, "review_count": 22841, "in_stock": true
| # | fsn | title | brand | manufacturer | model_number | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from flipkart.com. All fields typed and schema-versioned.
"fsn": "MOBGTAGPTB3VS24N", "price": 44999.00, "mrp": 54999.00, "discount_pct": 18, "bank_offer_discount": 3000, "emi_min_monthly": 2083, "exchange_offer_available": true, "exchange_max_value": 17000, "price_timestamp": "2026-05-12T10:15:00Z"
| # | fsn | price | mrp | discount_pct | discount_abs | bank_offer_description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from flipkart.com. All fields typed and schema-versioned.
"review_id": "fk_rv_7248193042", "fsn": "MOBGTAGPTB3VS24N", "star_rating": 5, "certified_buyer": true, "review_title": "Best Samsung phone in this segment", "upvotes": 312, "review_date": "2026-04-20"
| # | review_id | fsn | reviewer_name | certified_buyer | star_rating | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Data objects from flipkart.com. All fields typed and schema-versioned.
"seller_id": "RetailNet India", "seller_name": "RetailNet India", "seller_rating": 4.6, "reviews_count": 94218, "fulfilled_by_flipkart": true, "flipkart_assured_seller": true, "active_listings_count": 5821
| # | seller_id | seller_name | seller_rating | reviews_count | ships_from | fulfilled_by_flipkart |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Flipkart scraper covers every layer of India's largest e-commerce platform: product listings, dynamic pricing, Flipkart Assured rankings, seller intelligence, EMI and bank offers, and the full review corpus.
Title, highlights, description, specifications, images, variants, and every metadata field Flipkart surfaces — scraped at FSN level with parent-child variant mapping.
Capture price, MRP, discount percentage, bank offers, EMI options, exchange offer values, and Flipkart Plus pricing — timestamped per crawl.
Extract category rankings across primary and sub-categories. Track rank movement over time to identify trending products and category shifts.
Full review text, star ratings, helpful votes, upvotes/downvotes, certified buyer flags, and variant reviewed — paginated across all review pages.
Seller name, rating, Flipkart Assured status, fulfilment type, return policy, replacement policy, and active listing count — for every seller.
Identify and track the Flipkart Assured badge — a quality certification that significantly influences ranking, conversion, and buyer trust on the platform.
Track organic vs sponsored position for any keyword on Flipkart — with Assured badge, price drop, and bank offer capture.
Monitor bank-specific discount offers, no-cost EMI tenures, minimum monthly instalments, and exchange offer values — critical for consumer finance analytics.
One-off bulk exports or continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide FSN lists, category URLs, keyword sets, or seller IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for flipkart.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.
Flipkart invests heavily in scraping detection. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
Flipkart's bot detection operates on TLS fingerprints, browser headers, mouse-movement heuristics, and IP reputation. Our crawlers use Indian residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Flipkart is a React SPA — product pages, seller panels, pricing widgets, and EMI calculators are all JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution and dynamic widget hydration — capturing data that headless HTTP clients miss entirely.
Flipkart changes its DOM structure frequently. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, text-pattern matching, and structured data extraction (LD+JSON) — so a layout change doesn't break your data pipeline overnight.
For large FSN catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost, storage bloat, and downstream processing load. You get a clean changelog rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops — and respond before you notice. SLA uptime is contractual, not aspirational.
Brands and marketplace sellers monitor Flipkart pricing, bank offer windows, and Big Billion Day pricing to reprice and protect margin on Flipkart and cross-channel.
Brands audit third-party sellers for MRP violations, counterfeit listings, and unauthorised resellers on Flipkart's marketplace — protecting brand equity at scale.
Analysts track category rank movements, new product launches, and Assured badge dynamics to identify whitespace and investment opportunities in Indian e-commerce.
ML teams use Flipkart datasets to train recommendation engines, NLP classifiers, and sentiment models trained on Indian consumer reviews.
Banks and fintech firms monitor EMI option availability, tenure structures, and bank offer prevalence across Flipkart categories for consumer credit product design.
PE firms and analysts track category leaders, seller growth curves, and Flipkart Assured adoption rates to evaluate brands and marketplace companies operating in India.
"Flipkart is India's largest e-commerce platform and the richest price-signal dataset in the Indian market — but none of it is queryable unless you build the pipeline."
Most teams underestimate what reliable Flipkart scraping requires: Indian residential proxies, full Playwright rendering of a React SPA, CAPTCHA handling, EMI widget interaction, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our flipkart.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 Flipkart's React SPA rendering, cookie sessions, and EMI widget interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of Indian ISP residential proxies to match Flipkart's geo-targeting. 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 flipkart.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Flipkart is generally permissible under applicable Indian law and aligned with international precedents such as the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data, circumvent authentication walls, or violate applicable privacy law. We recommend clients review Flipkart's ToS independently and consult legal counsel for specific use cases.
We use Indian ISP residential proxies that appear as real Indian consumer traffic, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes don't break the pipeline. We monitor for 503/CAPTCHA rate spikes in real time and trigger pool rotation or solver queues automatically.
Flipkart Assured is a quality and fulfilment badge that significantly influences search ranking, buy box placement, and buyer conversion on Flipkart. We capture the Assured flag at FSN level on every run — allowing you to track badge grant and removal over time and correlate it with rank and price movements.
Yes. We capture bank-specific discount offer details, no-cost EMI tenures, minimum monthly instalment amounts, and exchange offer values per FSN. This is particularly valuable for consumer finance teams, fintech companies, and brands running co-branded bank partnerships.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined FSN set. Full catalogue refreshes at daily cadence complete within a 6–12 hour window depending on size. Historical snapshots are available from the day your pipeline is commissioned.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per FSN for category rank, price, MRP, review count, and Assured status. History is available from the date your pipeline starts.
Our smallest packages start at a defined FSN list (typically 1,000–50,000 FSNs) with weekly delivery. For larger catalogues, ongoing monitoring contracts, or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 FSNs or 50 search result pages as part of the pre-engagement scoping process — so you can 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 2M FSNs — we scope, build, and operate the pipeline. Tell us what you need.
