Google Analytics to Google Data Studio

This page provides you with instructions on how to extract data from Google Analytics and analyze it in Google Data Studio. (If the mechanics of extracting data from Google Analytics seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Google Analytics?

Google Analytics (GA) lets you track the performance of websites and applications and measure advertising ROI. It includes a tag manager, an analytics dashboard, and a tool to optimize websites based on GA data.

Getting data out of Google Analytics

It can be tricky to extract data from Google Analytics because the APIs don't allow us to extract event-level data. It would be great to just extract page_views or visitors, but that option is available only on the paid tier of Google Analytics, which carries a hefty price tag. Therefore, the data we'll be working with is rolled up into an aggregated format.

The gateway to your Google Analytics data is the Google Core Reporting API, which lets you make calls to retrieve data.

Example Google Analytics code

The GA API returns JSON-formatted data. Here's an example of what that response might look like:

{
  "kind": "analytics#gaData",
  "id": string,
  "selfLink": string,
  "containsSampledData": boolean,
  "query": {
    "start-date": string,
    "end-date": string,
    "ids": string,
    "dimensions": [
      string
    ],
    "metrics": [
      string
    ],
    "samplingLevel": string,
    "sort": [
      string
    ],
    "filters": string,
    "segment": string,
    "start-index": integer,
    "max-results": integer
  },
  "itemsPerPage": integer,
  "totalResults": integer,
  "previousLink": string,
  "nextLink": string,
  "profileInfo": {
    "profileId": string,
    "accountId": string,
    "webPropertyId": string,
    "internalWebPropertyId": string,
    "profileName": string,
    "tableId": string
  },
  "columnHeaders": [
    {
      "name": string,
      "columnType": string,
      "dataType": string
    }
  ],
  "rows": [
    [
      string
    ]
  ],
  "sampleSize": string,
  "sampleSpace": string,
  "totalsForAllResults": [
    {
      metricName: string,
      ...
    }
  ]
}

Keeping Google Analytics data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Google Analytics.

And remember, as with any code, once you write it, you have to maintain it. If Google modifies its GA API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

From Google Analytics to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Google Analytics data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Google Analytics to Redshift, Google Analytics to BigQuery, and Google Analytics to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Google Analytics data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.