Microsoft SQL Server connector

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

Written By Lauri Raivio

Last updated About 3 hours ago

Kaivo is a fully managed data platform that syncs your Microsoft SQL Server 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 data from Microsoft SQL Server instead of moving it.

What is the Microsoft SQL Server connector

Sync your Microsoft SQL Server tables into BigQuery with Kaivo to report on your application data alongside the rest of your business.

CategoryFiles & Databases, Tech
StatusGenerally available
AuthenticationUsername and password
SetupSelf-service

Getting started with the Microsoft SQL Server connector

  1. Sign up for Kaivo and create a workspace.
  2. Connect your Microsoft SQL Server 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 Microsoft SQL Server

Connect with your Microsoft SQL Server login. You provide:

FieldDescription
Username

Username to access the database.

Password

Password associated with the username.

Configuring the Microsoft SQL Server connector

When you set up the connector, you provide:

FieldDescription
Host

Hostname of the database, for example "mssqlserver.example.com", "dw.azure.example.com" or "172.217.22.14". Only public IP addresses are allowed.

Port

Port of the database.

Database

Name of the database.

Tables and columns synced from Microsoft SQL Server

The available streams and columns mirror the tables and fields in your own database, so they are determined when the connection runs rather than listed here.

How the Microsoft SQL Server sync works

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

It depends on how much history is in your Microsoft SQL Server 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 SQL Server'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 SQL Server, 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 SQL Server 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 SQL Server connector in Kaivo and all of its synced data is deleted with it.

Common use cases for Microsoft SQL Server data

Operational reporting

Bring the tables behind your application into BigQuery for reporting without straining your SQL Server database.

Combine with SaaS data

Join your database with CRM, billing, or marketing data in BigQuery for a full picture.

Historical analysis

Keep a synced history of your tables to analyse how records change over time.

One warehouse

Centralise SQL Server data with the rest of your sources for company-wide reporting.

Use Microsoft SQL Server data in your AI and BI tools

Once Microsoft SQL Server 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 SQL Server 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 SQL Server connector pricing and plan details.