Collect real-time conditions, 14-day forecasts, historical climate archives, severe weather alerts, and hyperlocal microclimate data from multiple authoritative meteorological sources — fused, validated, and delivered to your systems in a clean, consistent schema.
Weather data scraping is the automated collection and normalisation of meteorological data from multiple public and commercial sources — national weather services, commercial weather platforms, airport METAR feeds, personal weather station networks, and marine forecast portals. Rather than relying on a single provider's coverage gaps and API pricing, multi-source aggregation cross-validates readings and fills spatial gaps.
The critical word is 'structured.' Raw meteorological data comes in dozens of formats — METAR strings, GRIB files, XML feeds, JSON APIs, and HTML weather pages — each with different units, time zones, and terminology. DataFlirt normalises all of this into a single consistent schema: standard units, UTC timestamps, WMO weather codes, and clean numeric fields ready for your database or model.
Weather is a fundamental variable for industries from agriculture to retail to insurance. The challenge isn't that weather data doesn't exist — it's that usable, high-coverage, historically deep, multi-source weather data is expensive from commercial providers and technically complex to assemble yourself. DataFlirt provides this as a managed data service at a fraction of commercial API pricing.
Comprehensive extraction built for reliability, accuracy, and scale.
Real-time temperature, humidity, wind speed and direction, pressure, visibility, UV index, and cloud cover for any location.
Hourly forecasts up to 48 hours and daily forecasts up to 14 days from multiple models, with ensemble spread for uncertainty.
Storm warnings, flood watches, tornado advisories, cyclone tracking, and heat index alerts from national weather services.
Decades of historical observations and reanalysis data for any location — essential for climate normalisation and backtesting.
Wave heights, swell period, sea surface temperature, aviation METARs, TAFs, and SIGMETs for maritime and aviation applications.
Data from NOAA, Met Office, Meteoblue, OpenWeather, Weather.com, and local NWS stations fused and cross-validated.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data — reliably.
{ "location": "Bengaluru, IN", "lat": 12.9716, "lon": 77.5946, "observed_at": "2025-06-10T07:30:00Z", "temperature_c": 24.2, "humidity_pct": 71, "wind_kph": 14, "wind_dir": "SW", "precip_mm": 0.0, "condition_wmo": 2, "condition_text": "Partly cloudy", "uv_index": 5, "sources": ["imd.gov.in", "meteoblue.com"] }
Built on proven open-source tools and cloud infrastructure — no vendor lock-in.
Simultaneous collection from NOAA, IMD, Met Office, Meteoblue, and OpenWeather with automatic fallback on source outage.
Supports point-based queries (lat/lon), bounding boxes, and regular grid cells down to 1km spatial resolution.
National Weather Service and WMO alert feeds polled continuously — new alerts delivered within minutes of official issuance.
Weather observations stored in TimescaleDB for efficient time-range queries across millions of location-timestamps.
Raw model output files (GFS, ECMWF, ICON) processed and converted to structured tabular or JSON format on ingestion.
Growing degree days, evapotranspiration, soil moisture indices, and chill hour calculations available as derived fields.
From solo analysts to enterprise data teams — here's how organizations use this data.
Retail demand shifts with temperature. Freight delays spike when storms hit corridors. Crop yields depend on rainfall timing. Solar plant output varies with cloud cover. These relationships are real, measurable, and valuable — but only if you have the structured weather data to quantify them. DataFlirt delivers clean, multi-source meteorological data that integrates directly into your models, dashboards, and decision systems.
Start free and scale as your data needs grow.
For small teams and projects getting started with data.
For growing teams with serious data requirements.
For large organizations with custom requirements.
Everything you need to know before getting started.
Join data teams worldwide using DataFlirt to power products, research, and operations with reliable, structured web data.
