Teamwork connector
Set up the Teamwork connector in Kaivo: authentication, configuration, the 21 BigQuery tables it syncs, and answers to common questions.
Written By Lauri Raivio
Last updated 15 days ago
Kaivo is a fully managed data platform that syncs your Teamwork 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 Teamwork connector
Sync your Teamwork project data into BigQuery with Kaivo to report on tasks, time, and project profitability across your work.
Getting started with the Teamwork connector
- Sign up for Kaivo and create a workspace.
- Connect your Teamwork 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 Teamwork
Connect with your Teamwork login. You provide:
Configuring the Teamwork connector
When you set up the connector, you provide:
Tables and columns synced from Teamwork
Kaivo syncs 21 tables from Teamwork into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
companies (28 columns)
companies (28 columns)
Subtable: companies__tags
dashboards (17 columns)
dashboards (17 columns)
forms (30 columns)
forms (30 columns)
Subtable: forms__content__definition
Subtable: forms__content__definition__conditional_logic__actions
Subtable: forms__content__definition__conditional_logic__conditions
Subtable: forms__content__definition__conditional_logic__conditions__conditions
latestactivity (30 columns)
latestactivity (30 columns)
me_timers (21 columns)
me_timers (21 columns)
Subtable: me_timers__intervals
milestones (26 columns)
milestones (26 columns)
Subtable: milestones__responsible_parties
Subtable: milestones__responsible_party_ids
milestones_deadlines (4 columns)
milestones_deadlines (4 columns)
notebooks (35 columns)
notebooks (35 columns)
Subtable: notebooks__change_followers__user_ids
Subtable: notebooks__change_followers__users
Subtable: notebooks__comment_followers__user_ids
Subtable: notebooks__comment_followers__users
notebooks_comments.json (33 columns)
notebooks_comments.json (33 columns)
Subtable: notebooks_comments.json__files
people (25 columns)
people (25 columns)
projectcategories (9 columns)
projectcategories (9 columns)
projects (87 columns)
projects (87 columns)
Subtable: projects__custom_field_values
Subtable: projects__customfield_values
Subtable: projects__tag_ids
Subtable: projects__tags
Subtable: projects__workflows
projects_active (3 columns)
projects_active (3 columns)
projects_billable (4 columns)
projects_billable (4 columns)
tags (8 columns)
tags (8 columns)
tasklists (14 columns)
tasklists (14 columns)
tasks (35 columns)
tasks (35 columns)
Subtable: tasks__assignee_companies
Subtable: tasks__assignee_teams
Subtable: tasks__assignee_user_ids
Subtable: tasks__assignee_users
Subtable: tasks__assignees
Subtable: tasks__attachments
Subtable: tasks__change_followers
Subtable: tasks__comment_followers
Subtable: tasks__tag_ids
Subtable: tasks__workflow_stages
time_entries (23 columns)
time_entries (23 columns)
timelog_totals (15 columns)
timelog_totals (15 columns)
timesheets (11 columns)
timesheets (11 columns)
workload_planners (17 columns)
workload_planners (17 columns)
Subtable: workload_planners__capacities___2024_09_02__tasks
Subtable: workload_planners__capacities___2024_09_03__tasks
Subtable: workload_planners__capacities___2024_09_04__tasks
Subtable: workload_planners__capacities___2024_09_05__tasks
How the Teamwork sync works
After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Teamwork 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 Teamwork?
It depends on how much history is in your Teamwork 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 Teamwork'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 Teamwork, 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 Teamwork 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 Teamwork connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Teamwork data
Project progress
Track tasks and milestones against deadlines to see what is on time and what is slipping.
Time and billing
Use time_entries and timelog_totals to compare logged time against budgets.
Profitability
Join projects_billable with time entries to measure margin by project.
Team workload
Bring people and workload_planners together to balance load across the team.
Use Teamwork data in your AI and BI tools
Once Teamwork 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 Teamwork 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 Teamwork connector pricing and plan details.
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