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.

Panoply provides a managed data warehouse platform that lets users quickly set up a new Amazon Redshift instance. It uses machine learning algorithms to handle complex tasks like schema building, data mining, modeling, scaling, performance tuning, security, and backup. Panoply can import data with no schema, no modeling, and no configuration, and you can work with the analysis, SQL, and visualization tools you already know on data in Panoply just as you would if you were creating a Redshift data warehouse manually.

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,
      ...
    }
  ]
}

Once you've identified the columns you want to insert, you can use Redshift's CREATE TABLE statement to define a table to receive all of the data.

With a table built, you might be tempted to migrate your data (especially if there isn't much of it) by using INSERT statements to add data to your Redshift table row by row. Not so fast! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you should load the data into Amazon S3 and use the COPY command to load it into Redshift.

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.

Panoply 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, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. 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 BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, To S3, and To Delta Lake.