Google Analytics is the most widely used analytics platform in the world, with millions of digital properties being tracked by the tool. We all know that data is the new gold, but time is equally important.

An analyst’s job is to dive deep into the data and find valuable, transformational insights, that can move your business forward. Their time shouldn’t be wasted with trivial and repetitive tasks, which can drastically speed up with the right tools.

Data Streams is such a tool. You can easily import your GA data into your warehouse of choice in just a few minutes by linking our cloud-based connector to your GA and data warehouse accounts.

Let’s take for example Redshift as the warehouse of choice.

Check out our tutorial below to see how to import your Google Analytics data into Amazon Redshift.

Setting up the Data Extract Source

You can choose an Existing Data Source or a New Data Source. We’re going to connect to a New Data Source.
From the Data Connector List, select Google Analytics

Note that you would need to link your Google Analytics account before getting started. Once you do that, you’ll be able to see your Google Analytics credentials into the credentials list.

Then, you can name your Data Source and click Submit.

Setting up the Data Destination

From the Data Connector List, select your data warehouse of choice, in this case, Amazon Redshift. First, you will have to link your Redshift account.

The alias can be any name that helps you identify this credential in the Data Streams app.

The server address is the address where your server is found. It can be a fully qualified name or IP address.

The Port is automatically populated based on the Data Warehouse you chose.

Username and Password – the Data Warehouse credentials.

Schedule and Data Range

You can set your stream to be One-time or Recurring.

You have a lot of predefined sections under Date Range.

You can also choose to set your own Start Date and End Date.
In terms of Granularity, our product offers: Daily, Weekly, Monthly.

We also offer three types of calendars: Standard, Retail, and Custom.

If you want your stream to be recurring, you can also select the Frequency, Time of day, and Timezone. You can also make use of the fields “Import Data since” and “Run this report until.”

We’re gonna go with One-time only stream with a specific period of time, daily granularity, and the standard calendar.

Next Step would be to add Filters and Segments.

This step is optional but we’ll show you how you can choose a metric or dimension to filter specific data, or how to Segment your data.

In the Filters section, you can filter by any metrics and dimensions you have set up.

Simply use the drop-down tab and select the data you would like to segment.

In Segments, you can select any segments you have set up.

For the demo purpose, we’re going to select a segment we’ve set up in Google Analytics  for this demo called ‘Connector Demo.’

Metrics and Dimensions has 3 tabs:

The first tab is Source. Here you will set your metric (mandatory) or dimension in the drop-down. Once the Source is set, you can select your Destination. This will be the column in the table.
In Data Type you will need to select the appropriate description.
To have more than one metric or dimension, simply click the green button below that says Add Metric or Dimension.
You can add as many metrics and dimensions you want for your data (*any limitations in this regard are from the cloud platform). We’re going to go for the following Metrics: Revenue, Orders, Units of products sold, and we’re also going to add couple Dimensions: product and campaign name.

Before you save your stream, you can preview your data. Click Preview Data. Note: You can preview all the data you selected, not just a sample.

This is what your data would look like, see the columns: Date/Revenue/Orders, etc.

Now you can name your stream and save it.

Once it’s saved, the stream will show in the stream list, right on top of the list. It’s in STARTED status, and in a couple of minutes it will pass in SUCCESS status.

Once it’s successful, you’ll know you were able to successfully transfer Google Analytics data into your Amazon Redshift data warehouse.

About the author

Sebastian Stan

Sebastian is a journalist and digital strategist with years of experience in the news industry, social media, content creation and management, and digital analytics.

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