Getting Started With Google Analytics 360
Getting started with BigQuery/GA360 integration
When you start working with your Analytics data in BigQuery,
there are a few things to keep in mind.
First, the Google Analytics data schema in BigQuery
is a bit different than what’s in Analytics 360.
You’ll get hit level data, but not all dimensions
and metrics are available in BiqQuery.
Examples include landing page and time on page.
You’ll need to calculate these in BigQuery.
Also, metrics brought into Google Analytics through ads integrations,
like clicks and impressions, are not available in BigQuery.
The Analytics schema for BigQuery is constantly
changing and improving, so we recommend that
you refer to the BigQuery Export schema often.
You can find the link at the end of this lesson.
You’ll also want to learn about the key differences
between BigQuery and traditional databases.
For example, in BigQuery, a field can be "repeated."
In a repeated field, you can have a collection of values,
rather than a single value, stored inside one field.
In BigQuery’s Google Analytics schema,
that’s how custom dimensions are stored.
They’re stored as repeated records
inside the main record which represents a session.
Being able to query over repeated fields
is one of BigQuery’s most powerful features,
but can involve using special clauses and commands
for handling repeated fields, such as
FLATTEN and WITHIN (with Legacy SQL)
and UNNEST (in Standard SQL).
Learn more using the links at the end of this lesson.
In addition to working with your organization’s Analytics data in BigQuery,
working with the BigQuery Cookbook and public datasets
provided by the Google Cloud Platform team
can really help your BigQuery skills and knowledge.
You can find these links at the end of the lesson.
Setting up the Google Analytics 360 export is straightforward,
but there are a few things to keep in mind as you prepare.
First, you’ll want to work with your team
to identify which view to export,
because you can only export one view per Google Analytics property.
If you have more than one view used for reporting,
you’ll want to choose a view that includes
as much of the data as possible in your Analytics property.
That said, sometimes an unfiltered view is not the best choice because
some view filters cannot be rewritten as query conditions in BigQuery.
The integration will automatically export historical data.
The amount of data exported will be the smaller of 13 months of data or 10 billion hits,
and this backfill will be completed within four weeks of integration setup.
However, if you subsequently choose another view in the same property to export,
there will be no backfill of its historical data.
There will be a batch export of daily data from the prior day,
and for current day data,
you’ll choose between three batches or real-time export.
Depending on how your team is using the data,
real-time export may make more sense,
but it’s more resource intensive.
Be sure to enter a backup payment method in Google Cloud Platform.
If your payment method fails, your Analytics data
won’t be exported and missing days
won't be backfilled after resolving the payment issue.
BigQuery is one component of the Google Cloud Platform ecosystem,
so when you set up the BigQuery export, you will also be using
Google Cloud Platform, another Google product.
Setting up this integration requires that you create
a Google Cloud Platform project for your Analytics data,
enable billing for that project,
and enable the BigQuery API for your project.
If your organization is already using Google Cloud Platform - or GCP -
you might want to use a GCP project that your company already has.
This is particularly helpful if you plan to combine your Analytics data
with other data sources in BigQuery.
Once you’ve set up a GCP project,
you’ll want to link that project to the view you’ve chosen
at the property level in the Analytics admin panel.
Be sure to add your email address in the “Select contacts” step
so that you’ll get immediate notification if a daily export fails.
Once the linking between Analytics and BigQuery is set up,
your organization will get charged for how much
storage and querying you use in BigQuery.
You can use the Google Cloud Platform Pricing Calculator,
linked at the end of this lesson,
to help you estimate your monthly charges.
Analytics 360 customers receive a monthly BigQuery credit
that can be applied to these charges.
You can find detailed instructions for setting up the Analytics link to BigQuery
in the online help article, “BigQuery Export for Analytics.”
You should now be familiar with
the Analytics 360 integration with BigQuery.