CIMIS connector

Set up the CIMIS connector in Kaivo: authentication, configuration, the 4 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 CIMIS 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 CIMIS instead of moving it.

What is the CIMIS connector

Sync CIMIS weather station readings into BigQuery with Kaivo to analyse temperature, rainfall, and evapotranspiration across California.

Category	Other
Authentication	API key
Setup	Self-service

Getting started with the CIMIS connector

Sign up for Kaivo and create a workspace.
Connect your CIMIS 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 CIMIS

Authenticate with your API Key.

Field	Description
API Key	Your CIMIS App Key. Register at et.water.ca.gov, then open your account page and select Get AppKey.

Configuring the CIMIS connector

When you set up the connector, you provide:

Field	Description
Targets Type	The kind of location the Targets refer to. All targets must be of this same type.
Targets	The locations to pull data for, matching the selected Targets Type — e.g. WSN station numbers (2, 8, 127), California zip codes, decimal-degree coordinates, or street addresses.
Daily Data Items	Daily measurements to retrieve (e.g. day-air-tmp-avg, day-precip, day-eto). Leave empty to use the CIMIS default daily report.
Hourly Data Items	Hourly measurements to retrieve (e.g. hly-air-tmp, hly-precip, hly-eto). Leave empty to omit hourly data.
Start Date	Any data before this date will not be fetched.
Unit of Measure	Units for the returned values: English (°F, inches, mph) or Metric (°C, mm).

Tables and columns synced from CIMIS

Kaivo syncs 4 tables from CIMIS into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.

data (50 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`date`	`STRING`
`day_air_tmp_avg__qc`	`STRING`
`day_air_tmp_avg__unit`	`STRING`
`day_air_tmp_avg__value`	`STRING`
`day_air_tmp_max__qc`	`STRING`
`day_air_tmp_max__unit`	`STRING`
`day_air_tmp_max__value`	`STRING`
`day_air_tmp_min__qc`	`STRING`
`day_air_tmp_min__unit`	`STRING`
`day_air_tmp_min__value`	`STRING`
`day_asce_eto__qc`	`STRING`
`day_asce_eto__unit`	`STRING`
`day_asce_eto__value`	`STRING`
`day_dew_pnt__qc`	`STRING`
`day_dew_pnt__unit`	`STRING`
`day_dew_pnt__value`	`STRING`
`day_precip__qc`	`STRING`
`day_precip__unit`	`STRING`
`day_precip__value`	`STRING`
`day_rel_hum_avg__qc`	`STRING`
`day_rel_hum_avg__unit`	`STRING`
`day_rel_hum_avg__value`	`STRING`
`day_rel_hum_max__qc`	`STRING`
`day_rel_hum_max__unit`	`STRING`
`day_rel_hum_max__value`	`STRING`
`day_rel_hum_min__qc`	`STRING`
`day_rel_hum_min__unit`	`STRING`
`day_rel_hum_min__value`	`STRING`
`day_soil_tmp_avg__qc`	`STRING`
`day_soil_tmp_avg__unit`	`STRING`
`day_soil_tmp_avg__value`	`STRING`
`day_sol_rad_avg__qc`	`STRING`
`day_sol_rad_avg__unit`	`STRING`
`day_sol_rad_avg__value`	`STRING`
`day_vap_pres_avg__qc`	`STRING`
`day_vap_pres_avg__unit`	`STRING`
`day_vap_pres_avg__value`	`STRING`
`day_wind_run__qc`	`STRING`
`day_wind_run__unit`	`STRING`
`day_wind_run__value`	`STRING`
`day_wind_spd_avg__qc`	`STRING`
`day_wind_spd_avg__unit`	`STRING`
`day_wind_spd_avg__value`	`STRING`
`julian`	`STRING`
`scope`	`STRING`
`standard`	`STRING`
`station`	`STRING`
`zip_codes`	`STRING`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

spatial_zipcodes (6 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`connect_date`	`STRING`
`disconnect_date`	`STRING`
`is_active`	`STRING`
`zip_code`	`STRING`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

station_zipcodes (7 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`connect_date`	`STRING`
`disconnect_date`	`STRING`
`is_active`	`STRING`
`station_nbr`	`FLOAT64`
`zip_code`	`STRING`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

stations (16 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`city`	`STRING`
`connect_date`	`STRING`
`county`	`STRING`
`disconnect_date`	`STRING`
`elevation`	`STRING`
`ground_cover`	`STRING`
`hms_latitude`	`STRING`
`hms_longitude`	`STRING`
`is_active`	`STRING`
`is_eto_station`	`STRING`
`name`	`STRING`
`regional_office`	`STRING`
`siting_desc`	`STRING`
`station_nbr`	`STRING`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Subtable: stations__zip_codes

Column	Type	Description
`_kaivo_parent_id`	`STRING`	Foreign key referencing _kaivo_id in the parent table. Auto-generated by Kaivo.
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`value`	`STRING`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

How the CIMIS sync works

After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where CIMIS 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 CIMIS?

It depends on how much history is in your CIMIS 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 CIMIS'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 CIMIS, 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.

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 CIMIS 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 CIMIS connector in Kaivo and all of its synced data is deleted with it.

Common use cases for CIMIS data

Weather trends

Use data with DayAirTmpAvg and DayPrecip to track temperature and rainfall over time by station.

Evapotranspiration

Analyse DayAsceEto to support irrigation and water planning.

Station coverage

Join stations with station_zipcodes to find the nearest station for a location.

Regional comparison

Compare readings across stations by county and elevation.

Use CIMIS data in your AI and BI tools

Once CIMIS 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 CIMIS 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 CIMIS connector pricing and plan details.

Demo: Sync Demo to BigQuery.
Care Quality Commission: Sync Care Quality Commission to BigQuery.
DingConnect: Sync DingConnect to BigQuery.
Navan: Sync Navan to BigQuery.
Eventbrite: Sync Eventbrite to BigQuery.
Fleetio: Sync Fleetio to BigQuery.

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