Connecting Google Analytics to SQL Server: A Comprehensive Guide

Are you looking to enhance your data analysis capabilities by connecting Google Analytics to SQL Server? You’re in the right place. This guide will walk you through three proven methods to integrate these powerful platforms, helping you choose the best solution for your needs.

Why Connect Google Analytics to SQL Server?

Before we dive into the how-to, let’s explore the benefits of this integration:

1. Enhanced Data Analysis: Combine web analytics data with other business metrics in SQL Server to create comprehensive business intelligence dashboards and reports.
2. Custom Report Creation: Build sophisticated custom reports by querying Google Analytics data directly using SQL.
3. Historical Data Storage: Maintain complete control over your historical analytics data by storing it in your own SQL Server database.

Top 3 Methods to Connect Google Analytics to SQL Server

Solution	Best For
Coefficient	Non-technical teams needing real-time Google Analytics data in SQL Server through a familiar spreadsheet interface
Stitch	Technical teams requiring automated ETL pipelines for large-scale Google Analytics data integration
CData SQL Gateway	Organizations needing direct SQL Server linked server setup for Google Analytics

1. Coefficient: No-Code Spreadsheet Automation

Coefficient offers a no-code solution to sync Google Analytics data to SQL Server using spreadsheets as an intermediate layer. This method allows for data transformation and cleaning before pushing to SQL Server.

Step-by-step guide:

Step 1. Install Coefficient

For Google Sheets

• Open a new or existing Google Sheet, navigate to the Extensions tab, and select Add-ons > Get add-ons.
• In the Google Workspace Marketplace, search for “Coefficient.”
• Follow the prompts to grant necessary permissions.
• Launch Coefficient from Extensions > Coefficient > Launch.
• Coefficient will open on the right-hand side of your spreadsheet.

For Microsoft Excel

• Open Excel from your desktop or in Office Online. Click ‘File’ > ‘Get Add-ins’ > ‘More Add-Ins.’
• Type “Coefficient” in the search bar and click ‘Add.’
• Follow the prompts in the pop-up to complete the installation.
• Once finished, you will see a “Coefficient” tab in the top navigation bar. Click ‘Open Sidebar’ to launch Coefficient.

Step 2. Connect and Import Data from Google Analytics

Click ‘Import from’

Click ‘Google Analytics 4

Select ‘Start from Scratch’

Select the appropriate data and click ‘Import.’

Step 3. Export Data from Your Spreadsheet to MS SQL

Before starting, make sure you’ve connected to MS SQL.

Then, navigate to Coefficient’s menu >Click “Export to…”

Select MS SQL.

Choose the tab in your workbook that contains the data you want to export and specify the header row that contains the database field headers.

Specify the table in your database where you want to insert the data and choose the appropriate action (Insert, Update, Delete).

Complete the field mappings for the export. Then, confirm your settings and click “Export” to proceed.

Then, highlight the specific rows in your sheet that you want to export, or choose to export all rows.

Review your settings and follow the prompts to push your data back to MS SQL.

Pros:

• No coding required
• Familiar spreadsheet interface
• Real-time data sync
• Data transformation capabilities

Cons:

• Requires spreadsheet as intermediate step
• May not be ideal for very large datasets

Learn more about connecting Microsoft SQL Server to Google Sheets

2. Stitch: Automated ETL Service

Stitch is a cloud-based ETL service that automates the process of extracting Google Analytics data and loading it into SQL Server.

Step-by-step guide:

Step 1: Create Stitch Account

The initial setup requires configuring several key account parameters:

Account Configuration:

  Plan Selection: Enterprise (recommended for GA data)

  User Authentication: Two-factor authentication

  API Access: Enabled

  Data Retention: 30 days (configurable)

Step 2: Configure Google Analytics as Source

Connection setup requires proper OAuth credentials and data selection:

Source Configuration:

  Integration Type: Google Analytics 4

  OAuth Credentials:

    Client ID: [from Google Cloud Console]

    Client Secret: [from Google Cloud Console]

    Redirect URI: [Stitch-provided URI]

  Data Selection:

    Properties: [Selected GA4 property]

    Metrics:

      – activeUsers

      – screenPageViews

      – sessions

      – bounceRate

      – averageSessionDuration

    Dimensions:

      – date

      – deviceCategory

      – country

      – source

      – medium

Step 3: Set Up SQL Server Destination

Database preparation requires creating appropriate schemas and tables:

— Create destination schema

CREATE SCHEMA analytics;

— Create tables for GA data

CREATE TABLE analytics.daily_metrics (

    date_id DATE PRIMARY KEY,

    active_users INT,

    page_views INT,

    sessions INT,

    bounce_rate DECIMAL(5,2),

    avg_session_duration DECIMAL(8,2),

    updated_at DATETIME2 DEFAULT GETDATE()

);

CREATE TABLE analytics.traffic_sources (

    source_id INT IDENTITY(1,1) PRIMARY KEY,

    date_id DATE,

    source VARCHAR(100),

    medium VARCHAR(100),

    sessions INT,

    users INT,

    FOREIGN KEY (date_id) REFERENCES analytics.daily_metrics(date_id)

);

Step 4: Configure Data Selection and Mapping

Define replication settings and establish field mappings:

Replication Settings:

  Frequency: Every 6 hours

  Historical Sync: 90 days

  Update Method: Merge

Field Mapping:

  daily_metrics:

    date: date_id

    active_users: active_users

    screenPageViews: page_views

    sessions: sessions

    bounceRate: bounce_rate

    averageSessionDuration: avg_session_duration

  traffic_sources:

    source: source

    medium: medium

    sessions: sessions

    activeUsers: users

Step 5: Initialize First Sync

Monitor the initial data load through the Stitch dashboard, ensuring proper data flow and transformation.

Pros:

• Automated ETL process
• Handles large data volumes
• Comprehensive data replication

Cons:

• Requires technical setup
• Premium features need paid subscription
• Limited transformation capabilities

3. CData SQL Gateway: Direct SQL Server Connection

CData SQL Gateway creates a direct connection between Google Analytics and SQL Server using a linked server setup.

Step-by-step guide:

Step 1: Install CData SQL Gateway

The installation process requires specific system configurations:

Installation Requirements:

  Operating System: Windows Server 2019/2022

  .NET Framework: 4.7.2 or higher

  Memory: 4GB minimum

  Disk Space: 500MB

Configuration Settings:

  Port: 1433 (configurable)

  Authentication: Windows Authentication

  Service Account: NetworkService

  TLS/SSL: Enabled

Step 2: Configure Google Analytics Credentials

Authentication setup requires proper OAuth configuration:

GA4 Authentication:

  Auth Type: OAuth 2.0

  Project ID: [from Google Cloud Console]

  View ID: [from GA4 property]

Connection Properties:

  InitiateOAuth: GETANDREFRESH

  OAuthClientId: [your_client_id]

  OAuthClientSecret: [your_client_secret]

  CallbackURL: http://localhost:33333

Schema Generation:

  UseCustomSchema: true

  CustomMetrics: enabled

  CustomDimensions: enabled

Step 3: Set Up Linked Server

Create the necessary database connections:

— Create the linked server

EXEC sp_addlinkedserver

    @server = ‘GASERVER’,

    @srvproduct = ‘Google Analytics’,

    @provider = ‘MSDASQL’,

    @datasrc = ‘GoogleAnalytics’;

— Configure security

EXEC sp_addlinkedsrvlogin

    @rmtsrvname = ‘GASERVER’,

    @useself = ‘False’,

    @rmtuser = ‘your_username’,

    @rmtpassword = ‘your_password’;

Step 4: Implement Query

Structure Set up views and stored procedures for data access:

— Create a view for common GA metrics

CREATE VIEW analytics.vw_ga_metrics AS

SELECT 

    date,

    activeUsers,

    screenPageViews,

    sessions,

    bounceRate,

    CAST(averageSessionDuration AS DECIMAL(10,2)) as avg_session_duration

FROM 

    OPENQUERY(GASERVER,

    ‘SELECT

        date,

        activeUsers,

        screenPageViews,

        sessions,

        bounceRate,

        averageSessionDuration

     FROM ga:sessions

     WHERE date >= DATEADD(day, -30, GETDATE())’);

— Create stored procedure for data refresh

CREATE PROCEDURE analytics.sp_refresh_ga_data

AS

BEGIN

    BEGIN TRY

        BEGIN TRANSACTION;

            TRUNCATE TABLE analytics.daily_metrics;

            INSERT INTO analytics.daily_metrics

            SELECT * FROM analytics.vw_ga_metrics;

        COMMIT TRANSACTION;

    END TRY

    BEGIN CATCH

        ROLLBACK TRANSACTION;

        THROW;

    END CATCH;

END;

Step 5: Test and Validate

Execute test queries and validate data accuracy through comparison with Google Analytics interface.

Pros:

• Direct SQL Server integration
• Native T-SQL querying
• No intermediate storage required

Cons:

• Complex initial setup
• Requires technical expertise
• Higher latency for real-time data

Start Connecting Google Analytics to SQL Server Today

Whether you need real-time data sync, automated ETL, or direct SQL querying, there’s a solution that fits your needs. For teams looking for an easy-to-use solution with powerful capabilities, Coefficient provides the perfect balance of simplicity and functionality.

Ready to enhance your data analysis capabilities? Try Coefficient’s free plan today and start connecting your Google Analytics data to SQL Server.

Further Reading

• How to Connect SQL Server to Excel
• How to Connect Microsoft SQL Server to Google Sheets

Frequently Asked Questions

Does Google Analytics teach SQL? 

While Google Analytics itself doesn’t teach SQL, Coefficient allows you to work with GA data using familiar spreadsheet functions, making it accessible even without SQL knowledge.

Can I pull data from Google Analytics? 

Yes, you can easily pull Google Analytics data using Coefficient’s no-code interface, which removes the 5000-row limitation and enables automated data refresh.

How to connect Google Data Studio to SQL Server?

While Data Studio offers direct SQL Server connection, using Coefficient provides more flexibility by allowing data transformation and cleaning before visualization.