Sample Data: OpenWeatherMap¶
OpenWeatherMap is a repository of recent historical and forecasted weather data in JSON format. Snowflake imports this weather data and makes it available to all Snowflake accounts free of charge so you can experiment with our unique, high-performance semi-structured columnar functionality using real-world data.
The sample weather data is provided for evaluation and testing purposes. The data is updated regularly in Snowflake, but is not maintained in real-time, which may result in occasional lapses in updates (i.e. we do not guarantee that the data is always current and/or gap-free).
As such, we do not recommend using the data in production systems.
In this Topic:
The data set includes the following tables, all stored in native JSON format and accumulated over time:
|Table Name||Description||JSON Description|
|DAILY_14_TOTAL||12 days of daily weather forecasts for 20,000+ cities.||Click here|
|DAILY_16_TOTAL||12 days of daily weather forecasts for 200,000+ cities (lower frequency of updates).||Click here|
|HOURLY_14_TOTAL||4 days of hourly weather forecasts for 20,000+ cities.||Click here|
|HOURLY_16_TOTAL||4 days of hourly weather forecasts for 200,000+ cities (lower frequency of updates).||Click here|
|WEATHER_14_TOTAL||Recent weather for 20,000 cities.||Click here|
The following query retrieves the recent high and low temperature readings for New York City, converted from celsius to fahrenheit temperatures, along with the latitude and longitude for the readings:
select (V:main.temp_max - 273.15) * 1.8000 + 32.00 as temp_max_far, (V:main.temp_min - 273.15) * 1.8000 + 32.00 as temp_min_far, cast(V:time as TIMESTAMP) time, V:city.coord.lat lat, V:city.coord.lon lon, V from snowflake_sample_data.weather.WEATHER_14_TOTAL where v:city.name = 'New York' and v:city.country = 'US' order by time desc limit 10;
The following query compares weather forecasts to actual weather readings:
with forecast as (select ow.V:time as prediction_dt, ow.V:city.name as city, ow.V:city.country as country, cast(f.value:dt as timestamp) as forecast_dt, f.value:temp.max as forecast_max_k, f.value:temp.min as forecast_min_k, f.value as forecast from snowflake_sample_data.weather.daily_16_total ow, lateral FLATTEN(input => V, path => 'data') f), actual as (select V:main.temp_max as temp_max_k, V:main.temp_min as temp_min_k, cast(V:time as timestamp) as time_dt, V:city.name as city, V:city.country as country from snowflake_sample_data.weather.WEATHER_14_TOTAL) select cast(forecast.prediction_dt as timestamp) prediction_dt, forecast.forecast_dt, forecast.forecast_max_k, forecast.forecast_min_k, actual.temp_max_k, actual.temp_min_k from actual left join forecast on actual.city = forecast.city and actual.country = forecast.country and date_trunc(day, actual.time_dt) = date_trunc(day, forecast.forecast_dt) where actual.city = 'New York' and actual.country = 'US' order by forecast_dt desc, prediction_dt desc;