How To Send your Mapify data to BigQuery

Here’s how you can send Mapify data to a BigQuery table for advanced analysis, using air quality data from Catalonia’s Open Data.

We’ll start with accessing air quality data provided through an API in JSON format. The first step is to bring this data into Mapify by creating a Data Feed. Use the API URL to define the schema for the incoming data. This setup allows the Data Feed to continuously receive and process data from the API.

Next, we’ll create a Workflow in Mapify to process this data. This Workflow subscribes to the Data Feed and applies any required calculations or business rules specific to your use case. The processed data is then saved into a Dataset within Mapify, enabling seamless visualization of the air quality data. For this example, we’ll use a Dataset called Barcelona Air Quality within Google’s console.

Once the Dataset is ready, create a table within it, defining all necessary fields like station name, timestamps, and pollutant levels. Return to your Workflow in Mapify and add a new Sink Node to integrate with BigQuery. Select the “Send data to Google BigQuery table” option, then configure it by entering your Google Cloud Project ID and Service Account JSON key for authentication. Map the fields from your Mapify Dataset to the respective BigQuery table fields using Mapify’s JMESPath Expression Helper.

With the Workflow configured, Mapify begins sending real-time data to BigQuery. Once the data arrives, you can explore it using BigQuery’s powerful analytical tools. Write SQL queries to extract insights and confirm the data’s successful import.

The possibilities expand further when you connect BigQuery to Looker Studio. This integration lets you visualize the air quality data, creating dashboards that showcase key metrics and trends. From here, you can monitor, analyze, and share your findings, ensuring a robust understanding of air quality variations across Catalonia.

By using Mapify’s no-code platform and BigQuery’s analytical capabilities, you achieve a streamlined, efficient process for handling complex data workflows. This setup empowers you to make smarter, data-driven decisions with minimal effort.