How to Migrate GA4 Data to Your Data Warehouse?

Are you looking for an easy way to move your data from Google Analytics to a data warehouse? If yes, then you are in the right place. This blog post aims to show you steps that can help you load your data from Google Analytics to a data warehouse. The blog will also highlight any limitations that you may encounter. This will enable you to make an informed decision after evaluating the methods. 

Methods to Move Data From Google Analytics 4 to Data Warehouse

Method 1: Migrate Google Analytics 4 (GA4) to Data Warehouse with Hevo

Using Hevo Data to migrate your GA4 data to your preferred data warehouse is quick, easy, and more streamlined.

Before you start the process, here are two prerequisites for its successful completion:

1. You must have an active GA4 account that has the data you want to transfer to the warehouse. If you do not have access to the account, contact the GA4 account administrator to gain access.
2. Ensure that you are assigned the role of a Pipeline Administrator, Team Collaborator, or Team Administrator to be able to create the Pipeline in Hevo.

Once you meet these prerequisites, follow the steps below to migrate GA4 data:

Step #1: Configure GA4 as a source

Before you start ingesting data from GA4, you need to set it as the Source in Hevo Data.

Follow the steps below to do it:

• Click PIPELINES in the Navigation Bar.
• Now, from the Pipelines List View, click +CREATE PIPELINE.
• Pick Google Analytics 4 on the Select Source Type page.
• Now, you will be taken to the Configure your Google Analytics 4 account page.
• Follow one of the steps below once you are on the page:
  • If you have already configured a Google Account, choose the same and click CONTINUE.
  • If not, click +ADD GOOGLE ANALYTICS 4 ACCOUNT and follow the steps below to configure an account:

1. Choose your linked Google account.
2. Click Allow to let Hevo access your Google Account data.

• On the Configure your Google Account Source Page, specify the following details:

• Pipeline Name: Add a unique name under 255 characters to your Pipeline.

• Authorized User/Service Account: A non-editable field, this is the email address associated with the Google Analytics you chose when connecting Hevo to the Google Analytics 4. This field comes pre-filled. 
• GA4 Account Name: This is the GA4 account from which you want to replicate the data. Remember that there could be several analytics accounts under a single Google account.
• Property Name: This is the app’s website from which you want Hevo to fetch user data. This field appears after choosing the preferred GA4 account from the drop-down.
• Historical Sync Duration: This is the duration for which you want to ingest the existing data from the Source. Default duration: 6 Months.
• In the Report section:
  • Report Name: A unique name for your report, not exceeding 30 characters.
  • Dimensions: These are the attributes for which you want to see the data in your analytics report.
  • Metrics: The numerical measurement of data as per the dimensions selected above.
  • Pivot Report: Hevo can create additional reports by rearranging the data from the above report.
  • Pivot Dimensions: This is the subset of dimensions from the parent report for which you want to rearrange the data.
  • Pivot Metrics: This is the subset of metrics from the parent report for which you want to rearrange the data.
  • Pivot Aggregation Function: This field is the aggregation function that you want to use to reorganize the data.
  • Advanced Options: These are conditions you can employ to filter the data from the above report as you need.

If you want to add more reports, you can click + ADD ANOTHER REPORT and add up to five reports. But this is optional.

Click TEST & CONTINUE.

Proceed to configure the data ingestion and set up the Destination.

Step #2: Configure your Data Warehouse details

Once the first step is complete, follow the steps below:

• In the Navigation Bar, click DESTINATIONS.
• From the Destinations List View, click +CREATE DESTINATION
• Now, choose Google BigQuery as the Destination type on the Add Destination page
• On the Configure your Google BigQuery Destination page, specify the details as shown in the image:

• Destination Name: Pick a unique name for your Destination within 255 characters.
• Account: The type of account for authenticating and connecting to BigQuery.
• Project ID: Select the project ID of your BigQuery instance.
• Dataset: You can either let Hevo create a dataset for you or choose one manually from the list of datasets available under your project ID.
• GCS Bucket: You either let Hevo create a bucket for you or manually choose one from the available buckets under your project ID.
• Advanced Settings:
  • Populate Loaded Timestamp: You can enable this option to add the ___hevo_loaded_at_ column to the Destination table to show the time of loading the event to the Destination.
  • Sanitize Table/Column Names: You can enable this option to replace all non-alphanumeric characters and spaces in between the table and column names with an underscore (_).
• Click TEST CONNECTION.
• Click SAVE & CONTINUE.

With this, you will have configured the Google BigQuery destination to migrate GA4 data to the warehouse.

Method 2:Migrate Google Analytics 4 (GA4) to Data Warehouse Manually with the Google Analytics API

Exporting GA4 data using the Google Analytics API is an efficient way for businesses to programmatically access and ingest data from their chosen GA4 properties to any warehouse of their choice.

Here are the steps to do it:

Step #1: Create and Configure Google Cloud Project

• Go to the Google Cloud Console and create a new project
• Enable the Google Analytics Data API for your project

Step #2: Create API Credentials

• In the Cloud Console, generate credentials, such as an OAuth client ID or API key.
• Download the credentials file.

Step #3: Authenticate Your Application

• Use the credentials to authenticate your script or application via OAuth 2.0 or an API key you generated in the previous step.

Step #4: Write and Run API Request

• Use your preferred programming language, such as Python, to call the API’s runReport method.
• Ensure you specify your property ID, desired metrics, dimensions, and date ranges.

Step #5: Fetch and Export Data

Parse the API response, and save or export the data in your desired format (CSV, JSON, etc.) as needed for your workflow.

Limitations Of Manual Method

Limitations of using the manual method for loading the data from Google Analytics to a data warehouse are as follows:

• Time-Consuming: Manually loading your data from Google Analytics to a data warehouse requires a lot of code to complete simple tasks. This is very problematic in fast-paced organizations where tight deadlines have to be consistently met. 
• Knowledge And Resource-Intensive: The manual method of moving your data from Google Analytics to a data warehouse requires a lot of commitment from your engineering team. This could be particularly taxing on small organizations.
• Real-Time Limitations: Configuring cron jobs is a necessity to even achieve limited real-time functionality under this method.
• Error-Handling: Undiscovered errors could potentially sabotage the ETL process and require more time to isolate/fix the issue.

Introduction To Google Analytics

Google Analytics is a cloud-based web analytics platform provided by Google. Google Analytics enables you to track data on your website through dimensions that allow you to sort through your website’s visitors and also metrics that monitor website activity, among other features. Exporting this data into a data warehouse gives you the opportunity to blend with other sources, thus enabling you to gain more nuanced insights into your organisation.   

Features of Google Analytics

• Google Analytics helps in Website Traffic measurement
• You monitor user activity and website conversion using Google Analytics
• You can know your user better with Audience report
• The Flow Visualisation Report gives you the behaviour pattern of the user
• With Google Analytics, you can get custom reports of the Analytics data

Conclusion

In this blog post, you have learned how to load your data from Google Analytics to a data warehouse. While manually writing ETL scripts can be tiring and time-consuming, Hevo Data provides a plug-and-play platform for your data movement.

Using a data transfer tool like Hevo can help your organization develop a more robust and reliable method for transferring its data from Google Analytics to a Data Warehouse for Free.

FAQ on Google Analytics to Data Warehouse

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