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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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Openweather connector in Airbyte

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

Set up BigQuery for your extracted Openweather data

Select where you want to import data from your 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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"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

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How to Sync to Manually

Step 1: Register and Get OpenWeather API Key

To begin, sign up for an account on the OpenWeather website. Once registered, navigate to the API section and generate an API key. This key will authorize your requests to the OpenWeather API, allowing you to fetch weather data.

Log into the Google Cloud Console and create a new project dedicated to this task. This will help you manage resources and permissions more effectively. Ensure that billing is enabled for the project, as BigQuery usage can incur costs.

Within your Google Cloud project, navigate to the API & Services dashboard. Enable the BigQuery API to allow programmatic access to BigQuery resources. This step is crucial for interacting with BigQuery via code.

Develop a Python script to fetch weather data from OpenWeather. Use libraries such as `requests` to make HTTP GET requests to the OpenWeather API, using your API key in the request header. Parse the JSON response to extract relevant weather data.

Once data is retrieved, transform it into a format compatible with BigQuery. This typically involves creating a structured JSON or CSV file, ensuring the data types and schema align with your intended BigQuery table structure.

Save the transformed data file locally. Use Google Cloud SDK's `gsutil` command-line tool to upload this file to a bucket within Google Cloud Storage. This acts as an intermediary storage to facilitate loading data into BigQuery.

Access the Google Cloud Console and navigate to BigQuery. Use the BigQuery web UI to create a new dataset and a table that matches the data schema. Use the "Create Table" function and specify the Google Cloud Storage file as your data source. Configure the schema and data format settings, then execute the load operation to transfer the weather data into BigQuery.

By following these steps, you can systematically move data from OpenWeather to BigQuery without relying on third-party connectors or integrations.