Google Analytics 360 is an enterprise-level marketing analytics tool for large companies. It's one of a suite of six Google products designed to help marketers get a holistic view of their online marketing efforts. The software was formerly called Google Analytics Premium.

Google Data Studio is a simple dashboard and reporting tool. It's free and easy to use, but it lacks the sophisticated features of higher-end reporting software. Many of the connectors it supports are for Google products, but third parties have written partner connectors to a wide variety of data sources. Its drag-and-drop report editor lets users create about 15 types of charts.

Google Analytics 360 stores data in a Google BigQuery data warehouse. If your analytics stack is also based on BigQuery, integrating Google Analytics 360 data with the rest of your data is a matter of writing SQL queries. If you use a different data warehouse, however, you need to export the data. That means setting an account up with the required permissions, then figuring out what datasets and columns you want to export. You can't export to a local destination — the export file must be stored in Google Cloud Storage. In addition, Google imposes certain export limitations you have to be aware of.

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Google Data Studio uses what it calls "connectors" to gain access to data. Data Studio comes bundled with 17 connectors, mostly to pull in data from other Google products. It also supports connectors to MySQL and PostgreSQL databases, and offers 200 connectors to other data sources built and supported by partners.

Google Data Studio provides a graphical canvas onto which users drag and drop datasets. Users can set dimensions and metrics, specify sorting and filtering, and tailor the way reports and charts are displayed.

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.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, most data sources include fields like id or created_at that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've taken new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.