Diarium connector
Set up the Diarium connector in Kaivo: authentication, configuration, the 7 BigQuery tables it syncs, and answers to common questions.
Diarium connector pricing, tier and credits on kaivo.io
Kaivo is a fully managed data platform that syncs your Diarium 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 project management data instead of moving it.
What is the Diarium connector
Section titled “What is the Diarium connector”Sync your Diarium employee and organisation records into BigQuery with Kaivo to report on your workforce alongside the rest of your business data.
| Category | Project Management |
|---|---|
| Authentication | API key |
| Setup | Self-service |
Getting started with the Diarium connector
Section titled “Getting started with the Diarium connector”- Sign up for Kaivo and create a workspace.
- Connect your Diarium 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 Diarium
Section titled “Authenticating Diarium”Authenticate with your API Key.
| Field | Description |
|---|---|
| API Key | |
Your Diarium API key. Paste the raw key, or the full DiariumToken realm="api", apikey="KEY" header — both are accepted. |
|
Configuring the Diarium connector
Section titled “Configuring the Diarium connector”When you set up the connector, you provide:
| Field | Description |
|---|---|
| Customer ID | |
Your numeric Diarium customer ID (e.g. 123). |
| | Date Range Start |
Start of the date range for calendar slots and appointments.
|
Tables and columns synced from Diarium
Section titled “Tables and columns synced from Diarium”Kaivo syncs 7 tables from Diarium into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
appointments (9 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
INT64 |
|
customer_id |
INT64 |
|
start |
STRING |
|
end |
STRING |
|
cancellable |
BOOL |
|
cancelled |
BOOL |
|
cancel_text |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
calendar_slots (10 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
STRING |
|
user_id |
INT64 |
|
code_id |
INT64 |
|
corporate_id |
INT64 |
|
start |
STRING |
|
duration |
INT64 |
|
slotgroup |
STRING |
|
treatment_group_id |
INT64 |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
corporates (9 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
INT64 |
|
name |
STRING |
|
business_id |
STRING |
|
address__street_address |
STRING |
|
address__postal_code |
STRING |
|
address__post_office |
STRING |
|
reservation_info |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: corporates__offices
Section titled “Subtable: corporates__offices”| 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. |
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
customers (10 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
INT64 |
|
first_name |
STRING |
|
second_name |
STRING |
|
third_name |
STRING |
|
fourth_name |
STRING |
|
last_name |
STRING |
|
postal_code |
STRING |
|
marketing_messages |
INT64 |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
employees (12 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
INT64 |
|
first_name |
STRING |
|
last_name |
STRING |
|
name |
STRING |
|
phone_number |
STRING |
|
email_address |
STRING |
|
profession |
STRING |
|
description |
STRING |
|
images__small |
STRING |
|
images__medium |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: employees__corporates
Section titled “Subtable: employees__corporates”| 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 |
INT64 |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: employees__codes
Section titled “Subtable: employees__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 |
INT64 |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
therapies (9 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
INT64 |
|
customer_id |
INT64 |
|
therapy_group_id |
INT64 |
|
date |
STRING |
|
therapist_id |
INT64 |
|
corporate_id |
INT64 |
|
is_cancelled |
BOOL |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
therapy_groups (9 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
INT64 |
|
customer_id |
INT64 |
|
therapy_group_type |
STRING |
|
start |
STRING |
|
end |
STRING |
|
max_therapies |
INT64 |
|
unused_therapies |
INT64 |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
How the Diarium sync works
Section titled “How the Diarium sync works”After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Diarium 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
Section titled “Frequently asked questions”How long does the initial sync take for Diarium?
Section titled “How long does the initial sync take for Diarium?”It depends on how much history is in your Diarium 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?
Section titled “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 Diarium’s schema changes?
Section titled “What happens when Diarium’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 Diarium, 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?
Section titled “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 Diarium 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 Diarium connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Diarium data
Section titled “Common use cases for Diarium data”Workforce reporting
Section titled “Workforce reporting”Use employees to report headcount and staffing across the organisation over time.
Organisation view
Section titled “Organisation view”Use corporates to break employee data down by company and organisational unit.
Combine with projects
Section titled “Combine with projects”Join Diarium employee records with project or time data in BigQuery for fuller resourcing reports.
Use Diarium data in your AI and BI tools
Section titled “Use Diarium data in your AI and BI tools”Once Diarium 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 Diarium data.
Claude
Section titled “Claude”Use Kaivo’s MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →
Power BI
Section titled “Power BI”Microsoft’s BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →
Data Studio
Section titled “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
Section titled “Tableau”The premium analytics standard, with native BigQuery integration. Setup guide →
Google Sheets
Section titled “Google Sheets”Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →
Connect via Power Query’s BigQuery connector. Setup guide →
Metabase
Section titled “Metabase”Open-source BI tool with strong BigQuery support. Setup guide →
See our pricing page for Diarium connector pricing and plan details.