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get_meteo

get_meteo(latitude, longitude, date)

Description

Get meteo for latitude , longitude and date

Usage

Call or Deploy get_meteo ?
Call get_meteo directly

The easiest way to use bigfunctions

  • get_meteo function is deployed in 39 public datasets for all of the 39 BigQuery regions.
  • It can be called by anyone. Just copy / paste examples below in your BigQuery console. It just works!
  • (You need to use the dataset in the same region as your datasets otherwise you may have a function not found error)

Public BigFunctions Datasets

Region Dataset
eu bigfunctions.eu
us bigfunctions.us
europe-west1 bigfunctions.europe_west1
asia-east1 bigfunctions.asia_east1
... ...
Deploy get_meteo in your project

Why deploy?

  • You may prefer to deploy get_meteo in your own project to build and manage your own catalog of functions.
  • This is particularly useful if you want to create private functions (for example calling your internal APIs).
  • Get started by reading the framework page

Deployment

get_meteo function can be deployed with:

pip install bigfunctions
bigfun get get_meteo
bigfun deploy get_meteo

Examples

select bigfunctions.eu.get_meteo(52.52, 13.41, "2023-05-10")

select bigfunctions.us.get_meteo(52.52, 13.41, "2023-05-10")

select bigfunctions.europe_west1.get_meteo(52.52, 13.41, "2023-05-10")

+-------+
| meteo |
+-------+
| {...} |
+-------+

Use cases

This get_meteo function appears to retrieve meteorological data (likely temperature, precipitation, wind, etc.) based on a given latitude, longitude, and date.

Here's a potential use case:

Analyzing the impact of weather on sales for a retail chain.

Imagine a retail company with stores across various locations. They want to understand how weather conditions influence daily sales. They could use this function in the following way:

  1. Data Preparation: They have a BigQuery table with daily sales data for each store, including the store's location (latitude and longitude) and the date of the sales.

  2. Enriching Sales Data with Weather: They can use the get_meteo function within a BigQuery query to add weather information to their sales data. For example:

SELECT
    sales.*,
    bigfunctions.us.get_meteo(sales.latitude, sales.longitude, sales.date) AS weather_data
FROM
    `project.dataset.sales_table` AS sales;

(Assuming the sales table is in the US region. Adjust the dataset name according to the table's location).

  1. Analysis: Now they have a combined table with sales and corresponding weather data. They can analyze this data to identify correlations and patterns. For example:

    • Do rainy days lead to increased sales of umbrellas or indoor games?
    • Does hot weather boost sales of ice cream and cold drinks?
    • Does extreme weather (heavy snow, heat waves) negatively impact overall sales?

  2. Predictive Modeling: This enriched data can be used to train machine learning models to predict future sales based on weather forecasts.

Other potential use cases:

  • Agriculture: Analyzing historical weather patterns to optimize planting and harvesting schedules.
  • Real Estate: Understanding the climate of different locations for property valuation and development.
  • Tourism: Providing weather information to tourists planning their trips.
  • Insurance: Assessing weather-related risks for pricing policies.

Essentially, anytime you need to combine location-based data with historical or current weather information within BigQuery, the get_meteo function could be a valuable tool. The documentation emphasizes its ease of use by being directly callable within BigQuery without needing to deploy it separately.

Need help or Found a bug?
Get help using get_meteo

The community can help! Engage the conversation on Slack

We also provide professional suppport.

Report a bug about get_meteo

If the function does not work as expected, please

  • report a bug so that it can be improved.
  • or open the discussion with the community on Slack.

We also provide professional suppport.

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