Google Analytics to Snowflake

This page provides you with instructions on how to extract data from Google Analytics and load it into Snowflake. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

About Snowflake

Snowflake is a data warehouse solution that is entirely cloud based. It's a managed service. If you don't want to deal with hardware, software, or upkeep for a data warehouse you're going to love Snowflake. It runs on the wicked fast Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be flexible and easy to work with where other relational databases are not. One example of this is the query execution. Snowflake creates virtual warehouses where query processing takes place. These virtual warehouses run on separate compute clusters, so querying one of these virtual warehouses doesn't slow down the others. If you have ever had to wait for a query to complete, you know the value of speed and efficiency for query processing.

Getting data out of Google Analytics

This can be tricky with Google Analytics because the API's we're working with here don't allow us to extract event level data. It would be great to just extract page_views or visitors, but that option is only available on the paid tier of Google Analytics, which carries a big 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. By following the process in the API documentation, you'll make calls to it in order to retrieve your data.

Example Google Analytics code

You can call the GA API programmatically, it returns JSON formatted data. This is 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,
      ...
    }
  ]
}

Preparing data for Snowflake

Depending on the structure that you data is in, you may need to prepare it for loading. Take a look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them. If you have a lot of data, you should compress it. Gzip, bzip2, Brotli, Zstandard v0.8 and deflate/raw deflate compression types are all supported.

One important thing to note here is that you don't need to define a schema in advance when loading JSON data into Snowflake. Onward to loading!

Loading data into Snowflake

There is a good reference for this step in the Data Loading Overview section of the Snowflake documentation. If there isn’t much data that you’re trying to load, then you might be able to use the data loading wizard in the Snowflake web UI. Chances are, the limitations on that tool will make it a non-starter as a reliable ETL solution. There two main steps to getting data into Snowflake:

  • Use the PUT command to stage files
  • Use the COPY INTO table command to load prepared data into the awaiting table from the prior step.

For the COPY step, you’ll have the option of copying from your local drive, or from Amazon S3. One of Snowflakes’ slick features lets you to make a virtual warehouse that will power the insertion process.

Keeping Google Analytics data up to date

Awesome work. Your script will extract data from Google Analytics and load it into your data warehouse for Analysis. All done right? Not just yet. What about when there are new and updated records?

One option here would be to load all of your data over again. This will work, but it's most assuredly the slowest and the most painful option. It won't be a good solution if latency is a priority for you.

The only sustainable way to keep your data up to date is to build your script in such a way as to identify new and updated records. You can do this by building in logic around some primary keys that auto-increment like updated_at or created_at. When you've built in this functionality, you can set up your script as a cron job or continuous loop to grab new data as it appears in Google Analytics.

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 Snowflake data warehouse.