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.

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

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.

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,
      ...
    }
  ]
}

Google Cloud Platform offers a helpful guide for loading data into BigQuery. You can use the bq command-line tool, and in particular the bq load command, to upload files to your datasets, adding schema and data type information along the way. You can find the syntax in the Quickstart guide for bq. Iterate through this process as many times as it takes to load all of your tables into BigQuery.

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.

BigQuery is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.