Google Analytics to Redshift

This page provides you with instructions on how to extract data from Google Analytics and load it into Redshift. (If this manual process sounds onerous, 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": [
    "metrics": [
    "samplingLevel": string,
    "sort": [
    "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": [
  "sampleSize": string,
  "sampleSpace": string,
  "totalsForAllResults": [
      metricName: string,

Loading data into Redshift

Once you've identified all the columns you want to insert, you can use the CREATE TABLE statement in Reshift to set up a table to receive your data.

With the table built, you might think that the easiest way to migrate your data (especially if there isn't much of it) would be to build INSERT statements to add data to your Redshift table row by row. Think again! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, we suggest moving the data into Amazon S3 and then using the COPY command to load it into Redshift.

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.

Other data warehouse options

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To BigQuery, To Postgres, To Snowflake, and To Panoply.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, 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 your Redshift data warehouse.