How to load data from Openweather to BigQuery

Learn how to use Airbyte to synchronize your Openweather data into BigQuery within minutes.

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Bespoke pipelines are:
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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Openweather connector in Airbyte

Connect to Openweather or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Openweather data

Select BigQuery where you want to import data from your Openweather source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Openweather to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync Openweather to BigQuery Manually

How to Sync Openweather to BigQuery Manually - Method 2:

FAQs

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

OpenWeather is a team of IT experts and data scientists that has been practicing deep weather data science. OpenWeather App is an ad-free & free-to-use application that will assist you to plan your time around the weather in a concise and minimalistic manner. OpenWeather provides different APIs to get weather data from a location. You need to test if your connection has been properly composed. OpenWeather connector on Meta-API provides you access to all data and actions available on this API.

OpenWeather's API provides access to a wide range of weather-related data. The following are the categories of data that can be accessed through the API:  

1. Current weather data: This includes real-time weather conditions such as temperature, humidity, wind speed, and direction.  
2. Weather forecasts: This includes hourly, daily, and weekly weather forecasts for a specific location.  
3. Historical weather data: This includes past weather conditions for a specific location, including temperature, humidity, and precipitation.  
4. Air pollution data: This includes information on air quality, including levels of pollutants such as carbon monoxide, sulfur dioxide, and nitrogen dioxide.  
5. UV index data: This includes information on the level of ultraviolet radiation in a specific location.  
6. Weather maps: This includes various types of weather maps, such as temperature maps, precipitation maps, and wind maps.
7. Weather alerts: This includes alerts for severe weather conditions such as hurricanes, tornadoes, and thunderstorms.  

Overall, OpenWeather's API provides a comprehensive set of weather-related data that can be used for a wide range of applications, from weather forecasting to air quality monitoring.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up OpenWeather to BigQuery as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from OpenWeather to BigQuery and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

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