We extract live event schedules, ticket pricing tiers, venue metadata, artist lineups, and dining out offers from District. 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 Live Events objects from district.in. All fields typed and schema-versioned.
"event_id": "EV-9921", "title": "Diljit Dosanjh: India Tour", "category": "Music", "city": "Bengaluru", "min_price": 2999.0, "is_sold_out": false, "artist_lineup": "['Diljit Dosanjh']"
| # | event_id | title | category | sub_category | start_time | end_time |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Movies & Showtimes objects from district.in. All fields typed and schema-versioned.
"movie_id": "MV-402", "title": "Dune: Part Two", "language": "English", "format": "IMAX 2D", "cinema_name": "PVR Nexus", "available_seats": 42, "price_range": "450-850"
| # | movie_id | title | language | format | duration_mins | release_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Venues & Locations objects from district.in. All fields typed and schema-versioned.
"venue_id": "VN-881", "name": "Manpho Convention Centre", "city": "Bengaluru", "latitude": 13.041, "longitude": 77.621, "capacity": 5000, "parking_available": true
| # | venue_id | name | type | address | city | latitude |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Ticket Pricing objects from district.in. All fields typed and schema-versioned.
"event_id": "EV-9921", "tier_name": "VIP Fan Pit", "price": 8999.0, "currency": "INR", "availability_status": "Filling Fast", "remaining_tickets": 120, "max_tickets_per_user": 4
| # | event_id | tier_name | price | currency | availability_status | remaining_tickets |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Dining Out objects from district.in. All fields typed and schema-versioned.
"restaurant_id": "RES-102", "name": "Toit Brewpub", "locality": "Indiranagar", "district_offer_title": "15% off on total bill", "discount_percentage": 15, "validity": "2024-12-31", "rating": 4.8
| # | restaurant_id | name | cuisine | locality | city | district_offer_title |
|---|---|---|---|---|---|---|
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Our District scraper extracts real-time event schedules, ticket availability, and pricing tiers across all Indian cities. We handle the complex Next.js hydration and API rate limits automatically.
Monitor sell-out velocity and remaining ticket counts across all pricing tiers for high-demand live events.
Track surge pricing, early bird expiration, and phase-wise price increments for concerts and festivals.
Extract exact coordinates, seating capacities, and facility metadata for event locations and cinemas.
Parse performing artists, stage schedules, and support acts for multi-day music festivals and comedy shows.
Compile daily schedules, screen formats, and seat availability across all multiplexes listed on District.
Extract exclusive Zomato District dining discounts, validity periods, and terms across restaurants.
Execute minute-level updates during flash sales to capture true demand and inventory depletion rates.
Standardise event taxonomy and category mapping across Mumbai, Delhi, Bengaluru, and tier-2 cities.
Bypass web frontend limitations by directly querying District's internal mobile API endpoints for cleaner JSON payloads.
Brief in. Clean data out.
Provide target cities, event categories, or specific artist names. We design the extraction schema together.
We configure Scrapy crawlers, intercept mobile APIs, and set up residential proxies for district.in.
Schema validation, null-rate checks, and inventory accuracy verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Scraping ticketing platforms involves high concurrency requirements and strict rate limits. Here is how we maintain pipeline stability.
Ticketing platforms implement strict IP rate limiting during high-demand sales. We route requests through a massive pool of Indian residential ISP proxies, ensuring our crawlers blend in with normal mobile user traffic.
District uses Next.js for its web frontend. Instead of parsing fragile DOM elements, our parsers extract structured JSON directly from the __NEXT_DATA__ script tags, ensuring perfect schema alignment.
For real-time ticket inventory, web scraping is too slow. We reverse engineer and authenticate against District's internal mobile APIs, allowing us to poll seat availability at millisecond latency.
When a major concert goes live, inventory disappears in minutes. Our Kubernetes-based extraction workers scale horizontally to poll thousands of event pages simultaneously during peak sale windows.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops, responding before you notice any data gaps.
Ticket resellers and secondary platforms monitor primary market sell-out rates and pricing tiers to calibrate their own listings.
Event organisers track rival concert pricing, VIP tier inclusions, and discount strategies across different cities.
Analysts predict footfall and local economic impact by measuring ticket depletion velocity for major festivals.
Talent agencies track booking velocity and venue sizes per artist to negotiate better guarantees for future tours.
Real estate analysts monitor event frequency and capacity utilisation across major convention centres and arenas.
Fintech and loyalty apps compile dining and event discounts to benchmark their own credit card reward programs.
"District aggregates the highest intent offline consumer behaviour in India, but accessing that inventory data programmatically requires dedicated infrastructure."
Tracking flash sales for live events requires sub-minute polling and aggressive bot mitigation. We handle the CAPTCHAs, proxy rotation, and reverse engineered mobile APIs so your team can focus entirely on pricing strategy and demand forecasting.
Everything supported by our district.in 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 API querying. Playwright handles JavaScript rendering for complex venue seating charts. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across India. Rotation happens per-request to bypass strict ticketing rate limits and WAF protections.
Pipelines run on AWS Lambda for flash sale bursts and ECS for sustained crawls. Airflow handles scheduling and dependency management.
Data delivered to where your team already works — no new tooling required.
About district.in scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available event listings, venue details, and pricing from District is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal user data or circumvent authentication walls. Clients should review platform Terms of Service and consult legal counsel for specific use cases.
We scale our Kubernetes worker nodes horizontally and utilise internal mobile API endpoints to poll inventory status at high frequencies. This avoids the heavy overhead of rendering the web frontend during critical sale windows.
We track inventory at the ticket tier level (e.g., VIP, General Admission). For venues with specific seat maps, we extract the available seat count per block, subject to the data exposed by the District API.
For standard event monitoring, we run daily or hourly crawls. For high-demand flash sales, we configure sub-minute polling pipelines to capture rapid inventory depletion.
Yes. We extract public dining out offers, discount percentages, and validity periods for all restaurants listed on the platform.
Our minimum engagement starts at tracking a defined set of cities or event categories with daily delivery. Custom schemas for specific artist tracking or flash sale monitoring are priced based on compute volume.
Yes. We provide a sample run of up to 500 events or venues as part of the scoping process. This allows you to validate schema fit and data quality before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off venue database or continuous tracking of live event ticket inventory, we scope, build, and operate the pipeline. Tell us what you need.
