In this tutorial we'll explain how to get the Google Analytics Extractor integrated with your Meltano project to pull your Google Analytics data and load it into a Postgres analytics database.
For this tutorial, you can use a new or existing Meltano project.
If this is your first time using Google Analytics with Meltano, you will also need to:
Once you save your Google Analytics extractor configuration settings, you should be greeted with the Loaders page. Click to
Install Postgres and set the credentials for your local Postgres.
Information on how to install a Postgres Database on your local machine and configure the Postgres Loader can be found on PostgresQL Database Tutorials.
With our extractor and loader configured, you should now see the following page:
This page allows you to apply transformations to your data. We want to run the default transformations that come pre-bundled with Meltano for data fetched from Google Analytics, so we are going to select
Run and then click
If you'd like to learn more about how transformations work in Meltano, check out our documentation on Meltano Transformations.
You should now be greeted with the Schedules page with a modal to create your first pipeline!
Meltano provides Orchestration using Apache Airflow, which allows you to create scheduled tasks to run pipelines automatically. For example, you may want a recurring task that updates the database at the end of every business day.
In the current form, you will see:
All we need to do is click
Save to start our new pipeline! The pipeline's log opens automatically and you can check the pipeline running and what Meltano does behind the scenes to extract and load the data.
You should see a spinning icon that indicates that the pipeline is not completed. Once it's complete, the indicator will disappear and you should be able to see the final results of the extraction:
Congratulations! Now that you have connected to Google Analytics, configured the Postgres Loader, and run a successful pipeline for the dataset, we are now ready to analyze the data!
Let's start by closing the Run Log for the pipeline and click on the
Model option on the header of the page. This should bring us to the "Analyze: Models" page:
Meltano Models provide a starting point to explore and analyze data for specific use cases. They are similar to templates with only what is relevant for each use case included. As you can see in the right column,
tap-google-analytics already has the required models installed.
Let's move on to the next step by clicking
Analyze in the
Google analytics website overview card to move on to the next step.
The Analyze page contains an interactive user interface to allow you to dynamically build queries and visualize your data.
Now, let's explore and analyze our
tap-google-analytics data by selecting the following attributes in the left column:
And with that, the big moment is upon us, it's time to click
Run to run our query!
You should now see a beautiful data visualization and a table below to see the data in detail!
Let's remove the Day so that we can see aggregate stats per month and order the data by Year and Month ascending:
And, finally, switch the graph to an area chart:
When we find an analysis that we want to reference in the future, we can easily do this by creating a report. This can be accomplished by clicking on the
Save Report dropdown in the Analyze toolbar. This will open a dropdown with a default report name that is dynamically populated, but can be easily changed.
Once we click
Save, we should see the upper left "Untitled Report" change to our new report name.
And with that, our analysis has been saved!
As you acquire more reports, you will probably want to organize them via dashboards. This can be done by clicking on the new
Add to Dashboard dropdown in the toolbar.
Since we have never created a dashboard, click on
New Dashboard, which will trigger a modal that contains a dynamically generated dashboard name that can be customized as desired.
Once we click
Create, we can now verify that the our report has been added to the Dashboard by clicking on the
Add to Dashboard menu. We can also visit the Dashboard directly by clicking on the
Dashboard navigation item in the header, which shows our newly created Dashboard and the associated Report.
And with that, you have now setup a complete end-to-end data solution for Google Analytics with Meltano! 🎉
You can now check the rest of the pre-bundled Models for Device, Location, Traffic Source Stats and more.
Don't forget to save the reports that you find useful and add reports to your dashboards.
This is only a starting point: Google Analytics' API provides access to hundreds of dimensions and metrics. We are going to add an Advanced Tutorial soon with how to setup the Google Analytics Extractor to generate any report you may want, add custom Transforms for the extracted data and Custom Models to analyze the end results.