OpenAQ connector
Set up the OpenAQ connector in Kaivo: authentication, configuration, the 16 BigQuery tables it syncs, and answers to common questions.
Written By Lauri Raivio
Last updated 16 days ago
Kaivo is a fully managed data platform that syncs your OpenAQ data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your data from OpenAQ instead of moving it.
What is the OpenAQ connector
Sync your OpenAQ air-quality measurements, locations, and sensors into BigQuery with Kaivo to analyse pollution levels across sites and countries.
Getting started with the OpenAQ connector
- Sign up for Kaivo and create a workspace.
- Connect your OpenAQ account.
- Choose which tables to sync.
- Wait for the initial sync to finish.
- Query your data in BigQuery or your favourite AI or BI tool.
Authenticating OpenAQ
Authenticate with your API Key.
Configuring the OpenAQ connector
When you set up the connector, you provide:
Tables and columns synced from OpenAQ
Kaivo syncs 16 tables from OpenAQ into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
countries (7 columns)
countries (7 columns)
Subtable: countries__parameters
instruments (7 columns)
instruments (7 columns)
latest_parameters (9 columns)
latest_parameters (9 columns)
license_instrument (10 columns)
license_instrument (10 columns)
licenses (10 columns)
licenses (10 columns)
location_latest_measure (9 columns)
location_latest_measure (9 columns)
locations (21 columns)
locations (21 columns)
Subtable: locations__bounds
Subtable: locations__instruments
Subtable: locations__licenses
Subtable: locations__sensors
manufacturer_instruments (7 columns)
manufacturer_instruments (7 columns)
manufacturers (4 columns)
manufacturers (4 columns)
Subtable: manufacturers__instruments
measurements_daily (32 columns)
measurements_daily (32 columns)
measurements_yearly (32 columns)
measurements_yearly (32 columns)
owners (4 columns)
owners (4 columns)
parameters (7 columns)
parameters (7 columns)
providers (11 columns)
providers (11 columns)
Subtable: providers__bbox__coordinates
Subtable: providers__parameters
sensor_measurements (23 columns)
sensor_measurements (23 columns)
sensors (30 columns)
sensors (30 columns)
How the OpenAQ sync works
After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where OpenAQ supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.
Frequently asked questions
How long does the initial sync take for OpenAQ?
It depends on how much history is in your OpenAQ account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.
Can I sync only some tables or columns?
Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.
What happens when OpenAQ's schema changes?
New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in OpenAQ, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.
How do I handle GDPR or data deletion requests?
Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in OpenAQ instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the OpenAQ connector in Kaivo and all of its synced data is deleted with it.
Common use cases for OpenAQ data
Air-quality trends
Use sensor_measurements and latest_parameters to track pollutant levels over time.
Location analysis
Join locations with sensors to compare air quality across sites.
Parameter coverage
Use parameters to see which pollutants are measured where.
Regional comparison
Group measurements by country to compare air quality across regions.
Use OpenAQ data in your AI and BI tools
Once OpenAQ data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with OpenAQ data.
Claude
Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →
Power BI
Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →
Data Studio
Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →
Tableau
The premium analytics standard, with native BigQuery integration. Setup guide →
Google Sheets
Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →
Excel
Connect via Power Query's BigQuery connector. Setup guide →
Metabase
Open-source BI tool with strong BigQuery support. Setup guide →
See our pricing page for OpenAQ connector pricing and plan details.
Related connectors
- Demo: Sync Demo to BigQuery.
- Care Quality Commission: Sync Care Quality Commission to BigQuery.
- DingConnect: Sync DingConnect to BigQuery.
- CIMIS: Sync CIMIS to BigQuery.
- Navan: Sync Navan to BigQuery.
- Eventbrite: Sync Eventbrite to BigQuery.
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