Microsoft Outlook connector
Set up the Microsoft Outlook connector in Kaivo: authentication, configuration, the 5 BigQuery tables it syncs, and answers to common questions.
Microsoft Outlook connector pricing, tier and credits on kaivo.io
Kaivo is a fully managed data platform that syncs your Microsoft Outlook 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 survey and email data instead of moving it.
What is the Microsoft Outlook connector
Section titled “What is the Microsoft Outlook connector”Sync your Outlook email metadata into BigQuery with Kaivo to analyse volume, response activity, and unread backlog across mailboxes.
| Category | Surveys & Email |
|---|---|
| Authentication | API key |
| Setup | Self-service |
Getting started with the Microsoft Outlook connector
Section titled “Getting started with the Microsoft Outlook connector”- Sign up for Kaivo and create a workspace.
- Connect your Microsoft Outlook 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 Outlook
Section titled “Authenticating Microsoft Outlook”Authenticate with your Microsoft Outlook credentials. You provide:
| Field | Description |
|---|---|
| OAuth Client ID | |
| The Client ID of your Microsoft Azure application |
| | Tenant ID (Optional) |
Azure AD Tenant ID (optional for multi-tenant apps, defaults to ‘common’)
| | OAuth Client Secret |
The Client Secret of your Microsoft Azure application
| | Refresh Token |
Refresh token obtained from Microsoft OAuth flow
|
Prerequisites
Outlook uses Microsoft OAuth, so you need your own Azure app registration:
- In the Azure Portal under Microsoft Entra ID → App registrations, register an app and copy its Application (client) ID (and Directory (tenant) ID for a single-tenant app).
- Under Certificates & secrets, create a Client Secret and copy its value.
- Complete the OAuth authorization-code flow once to obtain a Refresh Token (see Microsoft’s guide).
Need help, or want an easier way to connect? Reach our team via the chat in the bottom-right corner.
Tables and columns synced from Microsoft Outlook
Section titled “Tables and columns synced from Microsoft Outlook”Kaivo syncs 5 tables from Microsoft Outlook into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
conversations (3 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
conversation_id |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
mailboxes (8 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
STRING |
|
display_name |
STRING |
|
parent_folder_id |
STRING |
|
total_item_count |
FLOAT64 |
|
unread_item_count |
FLOAT64 |
|
child_folder_count |
FLOAT64 |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
messages (14 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
STRING |
|
from__email_address__name |
STRING |
|
from__email_address__address |
STRING |
|
is_read |
BOOL |
|
subject |
STRING |
|
importance |
STRING |
|
body_preview |
STRING |
|
sent_date_time |
TIMESTAMP |
|
conversation_id |
STRING |
|
has_attachments |
BOOL |
|
received_date_time |
TIMESTAMP |
|
internet_message_id |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: messages__to_recipients
Section titled “Subtable: messages__to_recipients”| 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. |
email_address__name |
STRING |
|
email_address__address |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
messages_details (16 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
STRING |
|
body__content |
STRING |
|
body__content_type |
STRING |
|
from__email_address__name |
STRING |
|
from__email_address__address |
STRING |
|
is_read |
BOOL |
|
subject |
STRING |
|
importance |
STRING |
|
body_preview |
STRING |
|
sent_date_time |
TIMESTAMP |
|
conversation_id |
STRING |
|
has_attachments |
BOOL |
|
received_date_time |
TIMESTAMP |
|
internet_message_id |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: messages_details__attachments
Section titled “Subtable: messages_details__attachments”| 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. |
id |
STRING |
|
name |
STRING |
|
size |
FLOAT64 |
|
content_type |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: messages_details__cc_recipients
Section titled “Subtable: messages_details__cc_recipients”| 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. |
email_address__name |
STRING |
|
email_address__address |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: messages_details__to_recipients
Section titled “Subtable: messages_details__to_recipients”| 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. |
email_address__name |
STRING |
|
email_address__address |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: messages_details__bcc_recipients
Section titled “Subtable: messages_details__bcc_recipients”| 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. |
email_address__name |
STRING |
|
email_address__address |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
profile (8 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
id |
STRING |
|
mail |
STRING |
|
job_title |
STRING |
|
display_name |
STRING |
|
office_location |
STRING |
|
user_principal_name |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
How the Microsoft Outlook sync works
Section titled “How the Microsoft Outlook sync works”After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Microsoft Outlook 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 Microsoft Outlook?
Section titled “How long does the initial sync take for Microsoft Outlook?”It depends on how much history is in your Microsoft Outlook 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 Microsoft Outlook’s schema changes?
Section titled “What happens when Microsoft Outlook’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 Outlook, 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 Microsoft Outlook 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 Outlook connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Microsoft Outlook data
Section titled “Common use cases for Microsoft Outlook data”Volume and senders
Section titled “Volume and senders”Use messages to track email volume by sender and date over time.
Unread backlog
Section titled “Unread backlog”Use is_read across messages and mailboxes to see where unread mail piles up.
Conversation activity
Section titled “Conversation activity”Group messages by conversation_id to measure thread length and back-and-forth.
Priority and attachments
Section titled “Priority and attachments”Use importance and has_attachments to find high-priority or heavy mail.
Use Microsoft Outlook data in your AI and BI tools
Section titled “Use Microsoft Outlook data in your AI and BI tools”Once Microsoft Outlook 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 Outlook 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 Microsoft Outlook connector pricing and plan details.