We extract apparel listings, sizing matrices, discount signals, brand intelligence, and user reviews from Myntra. 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 Apparel Listings objects from myntra.com. All fields typed and schema-versioned.
"product_id": "23849102", "title": "Men Slim Fit Casual Shirt", "brand": "Roadster", "mrp": 1499.0, "selling_price": 749.0, "discount_pct": 50, "colours_available": "['Navy Blue', 'Olive']", "size_matrix": "['S', 'M', 'L', 'XL']"
| # | product_id | title | brand | category | sub_category | mrp |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Inventory objects from myntra.com. All fields typed and schema-versioned.
"product_id": "23849102", "current_price": 749.0, "coupon_code": "MYNTRA200", "coupon_discount": 200.0, "size": "M", "in_stock": true, "low_stock_warning": false, "price_timestamp": "2026-05-12T10:00:00Z"
| # | product_id | current_price | mrp | discount_amount | coupon_code | coupon_discount |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Fit Data objects from myntra.com. All fields typed and schema-versioned.
"review_id": "REV9876543", "product_id": "23849102", "star_rating": 4.0, "review_text": "Good fabric, fits slightly loose.", "helpful_votes": 12, "verified_buyer": true, "fit_feedback": "Runs Large", "size_purchased": "L"
| # | review_id | product_id | user_name | star_rating | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Myntra scraper navigates complex React SPAs to extract deeply nested SKU data: size availability matrices, dynamic bank offers, and fit feedback — with anti-bot circumvention built in.
Brand, title, material specs, care instructions, and high-res image URLs across all categories.
Track availability across all size variants (XS to XXL, shoe sizes) with low-stock warnings per SKU.
Capture MRP, selling price, coupon codes, bank offers, and flash sale discounts tied to specific user flows.
Extract user ratings, review text, and critical fit feedback classifications (e.g., 'Runs Small', 'True to Size').
Scrape trend-focused collections, influencer curations, and lookbook metadata directly from the Studio feed.
Extract fulfillment partner details, seller ratings, and estimated delivery timelines simulated per pin code.
Run one-off bulk exports or configure continuous pipelines with change-detection diffing for pricing.
Brief in. Clean data out.
Provide brand lists, category URLs, or search terms. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and anti-bot circumvention for myntra.com.
Schema validation, null-rate checks, price-outlier detection, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Myntra utilises aggressive WAFs and complex frontend architectures. Here's how we stay resilient.
Myntra utilises aggressive WAF and bot protection. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full TLS session management to bypass blocks.
Myntra's frontend is a heavily optimised Single Page Application. We run full Playwright browser sessions to execute JavaScript, hydrate product grids, and load lazy-loaded image assets.
Apparel data is notoriously nested. We map complex parent-child relationships, extracting individual SKUs, stock states, and pricing for every colour-size combination.
For large fashion catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, structural DOM changes, and coverage drops — and respond before you notice.
Fashion brands and private labels monitor discount depth, flash sales, and coupon strategies to optimise their pricing.
Retailers analyse brand coverage, category depth, and new collection drops to identify whitespace in their own catalogues.
Correlate out-of-stock velocity across specific sizes and colours to predict upcoming seasonal fashion trends.
Audit third-party sellers for unauthorised discounting, counterfeit listings, and MAP compliance across the marketplace.
Aggregate fit feedback and fabric complaints to improve product design and manufacturing QA.
Train computer vision and recommendation engines using Myntra's high-res product imagery and metadata.
"Myntra holds the definitive pulse on Indian fashion trends, pricing, and consumer fit feedback — but extracting structured SKU data across highly dynamic React interfaces requires serious infrastructure."
Most teams underestimate the investment required: reliable Myntra scraping requires residential proxies, full JavaScript rendering for SPA hydration, continuous selector maintenance, and anomaly monitoring for complex size matrices. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our myntra.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 SPA hydration.
We maintain pools of residential ISP proxies across Indian regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About myntra.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Myntra is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls. Clients should review Myntra's ToS 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. Our selectors have multi-layer fallback chains so DOM changes don't break the pipeline.
Yes. We configure real-time streaming pipelines that achieve sub-60-minute latency for price and availability signals on a defined SKU set during high-velocity sale events.
Yes. We extract the full size matrix per colour variant, capturing binary in-stock status and low-stock warning indicators for every individual size option.
Real-time streaming pipelines achieve sub-60-minute latency for specific SKUs. Full catalogue refreshes at daily cadence complete within a 6-12 hour window depending on scale.
Our smallest packages start at a defined brand or category list with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
Yes. We paginate through the full review corpus, extracting star ratings, text, verified buyer status, and specific fit feedback classifiers like 'Runs Small' or 'True to Size'.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across 500K SKUs — we scope, build, and operate the pipeline. Tell us what you need.
