Microsoft Teams connector
Set up the Microsoft Teams connector in Kaivo: authentication, configuration, the 12 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 Microsoft Teams 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 customer support data instead of moving it.
What is the Microsoft Teams connector
Sync your Microsoft Teams data into BigQuery with Kaivo to analyse channels, membership, and usage across your organisation.
Getting started with the Microsoft Teams connector
- Sign up for Kaivo and create a workspace.
- Connect your Microsoft Teams 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 Microsoft Teams
Authenticate with your Microsoft Teams credentials. You provide:
Configuring the Microsoft Teams connector
When you set up the connector, you provide:
Tables and columns synced from Microsoft Teams
Kaivo syncs 12 tables from Microsoft Teams into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
channel_members (10 columns)
channel_members (10 columns)
Subtable: channel_members__roles
channel_tabs (18 columns)
channel_tabs (18 columns)
channels (11 columns)
channels (11 columns)
conversation_posts (17 columns)
conversation_posts (17 columns)
Subtable: conversation_posts__categories
conversation_threads (10 columns)
conversation_threads (10 columns)
Subtable: conversation_threads__unique_senders
conversations (8 columns)
conversations (8 columns)
Subtable: conversations__unique_senders
group_members (13 columns)
group_members (13 columns)
Subtable: group_members__business_phones
group_owners (14 columns)
group_owners (14 columns)
Subtable: group_owners__business_phones
groups (28 columns)
groups (28 columns)
Subtable: groups__creation_options
Subtable: groups__group_types
Subtable: groups__on_premises_provisioning_errors
Subtable: groups__proxy_addresses
Subtable: groups__resource_behavior_options
Subtable: groups__resource_provisioning_options
Subtable: groups__service_provisioning_errors
team_device_usage_report (18 columns)
team_device_usage_report (18 columns)
team_drives (20 columns)
team_drives (20 columns)
users (12 columns)
users (12 columns)
Subtable: users__business_phones
How the Microsoft Teams sync works
After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Microsoft Teams 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 Microsoft Teams?
It depends on how much history is in your Microsoft Teams 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 Microsoft Teams'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 Microsoft Teams, 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 Microsoft Teams 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 Microsoft Teams connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Microsoft Teams data
Usage reporting
Use team_device_usage_report to track how teams and channels are used over time.
Membership view
Join groups with group_members and channel_members to map who belongs where.
Channel activity
Use channels and conversation_posts to see where conversation happens.
Access review
Bring users and group_owners together to review ownership and access.
Use Microsoft Teams data in your AI and BI tools
Once Microsoft Teams 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 Microsoft Teams 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 Microsoft Teams connector pricing and plan details.
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