We extract product listings, regional pricing, stock depth, and bbstar offers from Bigbasket. 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 bigbasket.com. All fields typed and schema-versioned.
"sku_id": "10000148", "product_name": "Onion - Medium", "brand": "Fresho", "category": "Fruits & Vegetables", "weight": "1 kg", "mrp": 45.0, "sale_price": 32.0, "bbstar_price": 30.0, "stock_status": "IN_STOCK", "rating": 4.1
| # | sku_id | product_name | brand | category | sub_category | weight |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pin-code Pricing & Stock objects from bigbasket.com. All fields typed and schema-versioned.
"sku_id": "10000148", "pin_code": "560001", "city": "Bengaluru", "mrp": 45.0, "sale_price": 32.0, "in_stock": true, "delivery_time": "Today, 4:00 PM - 6:00 PM", "scraped_at": "2026-05-12T08:14:00Z"
| # | sku_id | pin_code | city | mrp | sale_price | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category & Hierarchy objects from bigbasket.com. All fields typed and schema-versioned.
"category_id": "229", "category_name": "Edible Oils & Ghee", "parent_category": "Foodgrains, Oil & Masala", "level": 2, "url_slug": "/c/foodgrains-oil-masala/edible-oils-ghee/", "total_products": 412, "is_active": true
| # | category_id | category_name | parent_category | level | url_slug | total_products |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Offers & bbstar objects from bigbasket.com. All fields typed and schema-versioned.
"sku_id": "40122134", "offer_id": "OFF-8472", "offer_type": "FLAT_DISCOUNT", "discount_value": 50.0, "bank_offer": "HDFC 10% Off", "bbstar_exclusive": true, "bundle_offer": "Buy 2 Get 1 Free"
| # | sku_id | offer_id | offer_type | discount_value | bank_offer | bbstar_exclusive |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from bigbasket.com. All fields typed and schema-versioned.
"keyword": "basmati rice", "pin_code": "400001", "position": 1, "sku_id": "241600", "product_name": "India Gate Basmati Rice - Classic", "sponsored": false, "sale_price": 215.0, "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | pin_code | position | sku_id | product_name | sale_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Bigbasket scraper handles regional state management, dynamic pricing grids, and pin-code specific stock availability with JavaScript rendering and session management built in.
SKU, name, weight variants, brand, images, and category hierarchies extracted directly from the catalogue.
Track price variations and MRP differences across cities, zones, and individual pin-codes.
Monitor in-stock status and inventory depth per warehouse location and delivery zone.
Extract standard prices alongside bbstar loyalty program rates to map discount structures.
Track 10-minute delivery inventory, pricing, and availability separately from slotted delivery data.
Capture FSSAI details, shelf life, dietary preferences, and ingredient lists for FMCG products.
Extract card-specific discounts, bundle deals, and promotional banners tied to specific SKUs.
Track organic versus sponsored placements for category keywords across different pin-codes.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide SKU lists, category URLs, keywords, and target pin-codes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, location spoofing, session management, and API interception for bigbasket.com.
Schema validation, null-rate checks, price-outlier detection, and location-accuracy tests before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Bigbasket relies heavily on location-based state and dynamic APIs. Here is how we maintain reliable extraction at scale.
Bigbasket pricing and stock depend entirely on the user location. We manage thousands of concurrent sessions, injecting specific pin-code coordinates, cookies, and headers to simulate regional users accurately.
Product grids and prices load dynamically via backend APIs. Our Playwright instances intercept these XHR responses directly, ensuring we capture the raw JSON payloads rather than scraping volatile DOM elements.
To prevent IP bans from high-frequency regional queries, we route traffic through Indian residential ISP proxies, rotating IPs per request while maintaining sticky sessions for location persistence.
Bigbasket updates its frontend frameworks frequently. We use a combination of API payload extraction and multi-layered DOM selectors to ensure the pipeline survives structural updates.
For large FMCG catalogues, we maintain a hash index of last-seen values per SKU and pin-code. Subsequent runs only push diffs, reducing downstream processing load.
FMCG brands track MRP compliance, discounting trends, and regional price variations across their product portfolios.
Quick commerce and grocery platforms benchmark their pricing and assortment against Bigbasket and bbnow.
Retail analysts identify out-of-stock trends and regional availability gaps to optimise supply chain distribution.
Brands track share of search and sponsored placement visibility for key category terms across major Indian cities.
Financial analysts monitor commodity price indices by tracking staple grocery prices over time.
Marketing teams analyse discount depths, bank offers, and bundle deals to structure competitive promotions.
"Bigbasket holds the definitive index of Indian FMCG pricing and availability, but extracting it across 3,000+ pin codes requires serious infrastructure."
Most teams underestimate the complexity of regional grocery scraping. Reliable Bigbasket extraction requires managing thousands of concurrent location sessions, API request interception, and residential Indian proxies. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our bigbasket.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 Indian regions. 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 bigbasket.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Bigbasket is generally permissible under applicable law in India. DataFlirt targets only public, non-authenticated product, pricing, and availability data. We do not extract personal data, circumvent authentication walls, or violate GDPR/DPDP. Clients should review Bigbasket's ToS and consult legal counsel for specific use cases.
We manage separate browser sessions for each target pin-code. By injecting specific location cookies and headers, we simulate users in those exact areas, allowing us to extract accurate regional pricing and stock availability.
Yes. We maintain distinct pipelines for Bigbasket's slotted delivery catalogue and the bbnow quick commerce platform, as pricing and availability often differ between the two.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined SKU 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 SKU and pin-code for price, stock status, and bbstar rates from the date your pipeline starts.
Our smallest packages start at a defined SKU list or specific category set across a limited number of pin-codes with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 SKUs across 3 pin-codes 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 catalogue dump or continuous price monitoring across 500 pin codes, we scope, build, and operate the pipeline. Tell us what you need.
