How to connect Google Analytics to SSIS

Introduction

You can connect to your Google Analytics data in SSIS using the high-performance SSIS Google Analytics Connectors. We'll walk you through the entire setup.

Let's not waste time and get started!

Video tutorial

Watch this quick video to see the integration in action. It walks you through the end-to-end setup, including:

• Installing the SSIS PowerPack
• Configuring a secure connection to Google Analytics
• Working with Google Analytics data directly inside SSIS
• Exploring advanced Google Analytics Source features

Ready to dive in? Download the product to jump right in, or follow the step-by-step guide below to see how it works.

Prerequisites

Before we begin, make sure the following prerequisites are met:

1. SQL Server Data Tools (SSDT) designer installed for Visual Studio.
2. SQL Server Integration Services Projects 2022+ Visual Studio extension installed.
3. SSIS PowerPack is installed.

Read data from Google Analytics in SSIS using Google Analytics Source

This section provides a practical guide on how to extract data from Google Analytics using the Google Analytics Source component within SSIS (SQL Server Integration Services) via the ZappySys PowerPack.

1. Open Visual Studio and click Create a new project.

2. Select Integration Services Project. Enter a name and location for your project, then click OK.

3. From the SSIS Toolbox, drag and drop a Data Flow Task onto the Control Flow surface, and double-click it:

   

4. Make sure you are in the Data Flow Task designer:

   

5. From the SSIS toolbox drag and drop Google Analytics Source on the dataflow designer surface

   

6. Double click on Google Analytics Source component to configure it.

7. Next, set up the Google Analytics Source Connection. For detailed instructions, visit Google Data Connection documentation.

   

8. Configuring the Google Analytics Source:

   • Select View(s) or Profile(s):
     • Select the created Google Analytics Source Connection.
     • Choose the specific View(s) or Profile(s) from which you want to extract data.

       

   • Query Mode: Set the Query Mode to Automatic - Build your own URL. This mode allows for granular control over the data retrieval using URL query parameters.
   • Date Range:
     • Set the Date range type to Custom date range.
     • Specify the start and end dates for your report. You can use variables for dynamic date ranges, which is very useful for automation.
       Example: You can use SSIS variables named ReportStartDate and ReportEndDate.

       

   • Dimensions: Select the dimensions you want to include in your report (e.g., “date”, “pagePath”, “country”). You can select up to 7 dimensions.

     

   • Metrics: Select the metrics you want to retrieve (e.g., “sessions”, “pageviews”, “users”). You can select up to 7 metrics.

     

   • Filters: Optionally, apply filters to narrow down the data based on specific criteria (e.g., filtering for traffic from a particular country).

     

   • Sort By: Optionally, specify how the data should be sorted (e.g., sorting by “sessions” in descending order).

     

   • Segments: Optionally, apply segments to analyze specific subsets of your data.

     

9. Data Preview and Saving:
   • Click the Preview button to view a sample of the data that will be extracted. This allows you to verify your configuration.
   • Click the OK button to save the configuration settings for the Google Analytics Source.

     

10. That's it. Now you can now connect the Google Analytics Source to other data flow components, such as a Destination component, to load the extracted data into a database, file, or other destination.

Load Google Analytics data into SQL Server using Upsert Destination (Insert or Update)

Once you configured the data source, you can load Google Analytics data into SQL Server using Upsert Destination.

Upsert Destination can merge or synchronize source data with the target table. It supports Microsoft SQL Server, PostgreSQL, and Redshift databases as targets. Upsert Destination also supports very fast bulk upsert operation along with bulk delete.

1. From the SSIS Toolbox drag-and-drop Upsert Destination component onto the Data Flow designer background.

2. Connect your SSIS source component to Upsert Destination.

3. Double-click on Upsert Destination component to open configuration window.

4. Start by selecting the Action from the list.

5. Next, select the desired target connection or create one by clicking <New [provider] Connection> menu item from the Target Connection dropdown.

6. Then select a table from the Target Table list or click New button to create a new table based on the source columns.

7. Continue by checking Insert and Update options according to your scenario (e.g. if Update option is unchecked, no updates will be made).

8. Finally, click Map All button to map all columns and then select the Key columns to match the columns on:

   

9. Click OK to save the configuration.

10. Run the package and Google Analytics data will be merged with the target table in SQL Server, PostgreSQL, or Redshift:

    

11. Done!

Deploy and schedule SSIS package

After you are done creating SSIS package, most likely, you want to deploy it to SQL Server Catalog and run it periodically. Just follow the instructions in this article:

• How to design, debug, deploy, schedule SSIS Package (In SQL Agent and Catalog)

Running SSIS package in Azure Data Factory (ADF)

To use SSIS PowerPack in ADF, you must first prepare Azure-SSIS Integration Runtime. Follow this link for detailed instructions:

• Preparing ADF instance to use SSIS PowerPack