Outlook connector

Set up the Outlook connector in Kaivo: authentication, configuration, the 5 BigQuery tables it syncs, and answers to common questions.

Written By Lauri Raivio

Last updated 36 minutes ago

Kaivo is a fully managed data platform that syncs your 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 Outlook connector

Sync your Outlook email metadata into BigQuery with Kaivo to analyse volume, response activity, and unread backlog across mailboxes.

CategorySurveys & Email
StatusGenerally available
AuthenticationAPI key
SetupSelf-service

Getting started with the Outlook connector

  1. Sign up for Kaivo and create a workspace.
  2. Connect your Outlook account.
  3. Choose which tables to sync.
  4. Wait for the initial sync to finish.
  5. Query your data in BigQuery or your favourite AI or BI tool.

Authenticating Outlook

Authenticate with your Outlook credentials. You provide:

FieldDescription
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:

  1. 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).
  2. Under Certificates & secrets, create a Client Secret and copy its value.
  3. 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 Outlook

Kaivo syncs 5 tables from Outlook into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.

How the Outlook sync works

After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where 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

How long does the initial sync take for Outlook?

It depends on how much history is in your 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?

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 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 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?

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 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 Outlook connector in Kaivo and all of its synced data is deleted with it.

Common use cases for Outlook data

Volume and senders

Use messages to track email volume by sender and date over time.

Unread backlog

Use is_read across messages and mailboxes to see where unread mail piles up.

Conversation activity

Group messages by conversation_id to measure thread length and back-and-forth.

Priority and attachments

Use importance and has_attachments to find high-priority or heavy mail.

Use Outlook data in your AI and BI tools

Once 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 Outlook 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 Outlook connector pricing and plan details.