Tempo connector
Set up the Tempo connector in Kaivo: authentication, configuration, the 4 BigQuery tables it syncs, and answers to common questions.
Tempo connector pricing, tier and credits on kaivo.io
Kaivo is a fully managed data platform that syncs your Tempo 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 Tempo connector
Section titled “What is the Tempo connector”Sync your Tempo time tracking data into BigQuery with Kaivo to analyse billable time and project profitability.
| Category | Project Management |
|---|---|
| Authentication | API key |
| Setup | Self-service |
Getting started with the Tempo connector
Section titled “Getting started with the Tempo connector”- Sign up for Kaivo and create a workspace.
- Connect your Tempo 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 Tempo
Section titled “Authenticating Tempo”Authenticate with your API token.
| Field | Description |
|---|---|
| API token | |
| Tempo API Token. Go to Tempo>Settings, scroll down to Data Access and select API integration. |
|
Tables and columns synced from Tempo
Section titled “Tables and columns synced from Tempo”Kaivo syncs 4 tables from Tempo into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
accounts (24 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
category__type__name |
STRING |
|
category__id |
INT64 |
|
category__key |
STRING |
|
category__name |
STRING |
|
category__self |
STRING |
|
contact__type |
STRING |
|
contact__account_id |
STRING |
|
contact__self |
STRING |
|
customer__id |
INT64 |
|
customer__key |
STRING |
|
customer__name |
STRING |
|
customer__self |
STRING |
|
global |
BOOL |
|
id |
INT64 |
|
key |
STRING |
|
lead__account_id |
STRING |
|
lead__self |
STRING |
|
links__self |
STRING |
|
monthly_budget |
INT64 |
|
name |
STRING |
|
self |
STRING |
|
status |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
customers (6 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
INT64 |
|
key |
STRING |
|
name |
STRING |
|
self |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
workload-schemes (8 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
default_scheme |
BOOL |
The defaultScheme of the tempo Workload Scheme. |
description |
STRING |
The description of the tempo Workload Scheme. |
id |
INT64 |
The ID of the tempo Workload Scheme. |
member_count |
INT64 |
The memberCount of the tempo Workload Scheme. |
name |
STRING |
The name of the tempo Workload Scheme. |
self |
STRING |
The URL of the Workload Scheme |
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: workload-schemes__days
Section titled “Subtable: workload-schemes__days”| 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. |
day |
STRING |
|
required_seconds |
INT64 |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
worklogs (16 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
attributes__self |
STRING |
|
author__account_id |
STRING |
|
author__self |
STRING |
|
billable_seconds |
INT64 |
Billable time spent working |
created_at |
STRING |
Created at date of the worklog |
description |
STRING |
Description of the worklog |
issue__id |
INT64 |
|
issue__self |
STRING |
|
self |
STRING |
The URL of the worklog |
start_date |
STRING |
Start Date of the worklog |
start_time |
STRING |
Start time of the worklog |
tempo_worklog_id |
INT64 |
The ID of the tempo worklog. |
time_spent_seconds |
INT64 |
Time spend in seconds of the worklog |
updated_at |
STRING |
Updated at date of the worklog |
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: worklogs__attributes__values
Section titled “Subtable: worklogs__attributes__values”| 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. |
key |
STRING |
|
value |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
How the Tempo sync works
Section titled “How the Tempo sync works”After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Tempo 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 Tempo?
Section titled “How long does the initial sync take for Tempo?”It depends on how much history is in your Tempo 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 Tempo’s schema changes?
Section titled “What happens when Tempo’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 Tempo, 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 Tempo 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 Tempo connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Tempo data
Section titled “Common use cases for Tempo data”Billable time
Section titled “Billable time”Compare worklogs billable_seconds against time_spent_seconds to see how much logged time is billable.
Time by customer
Section titled “Time by customer”Join worklogs with accounts and customers to break down hours by client.
Contributor view
Section titled “Contributor view”Group worklogs by author to see how time is spent across the team.
Capacity planning
Section titled “Capacity planning”Use workload-schemes to compare planned capacity against logged time.
Use Tempo data in your AI and BI tools
Section titled “Use Tempo data in your AI and BI tools”Once Tempo 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 Tempo 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 Tempo connector pricing and plan details.