This page provides you with instructions on how to extract data from Google Analytics 360 and load it into Azure Synapse. (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 360?
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.
What is Azure Synapse?
Azure Synapse (formerly Azure SQL Data Warehouse) is a cloud-based petabyte-scale columnar database service with controls to manage compute and storage resources independently. It offers encryption of data at rest and dynamic data masking to mask sensitive data on the fly, and it integrates with Azure Active Directory. It can replicate to read-only databases in different geographic regions for load balancing and fault tolerance.
Getting data out of Google Analytics 360
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.
Preparing Google Analytics 360 data
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.
Loading data into Azure Synapse
Azure Synapse provides a multi-step process for loading data. After extracting the data from its source, you can move it to Azure Blob storage or Azure Data Lake Store. You can then use one of three utilities to load the data:
- AZCopy uses the public internet.
- Azure ExpressRoute routes the data through a dedicated private connection to Azure, bypassing the public internet by using a VPN or point-to-point Ethernet network.
- The Azure Data Factory (ADF) cloud service has a gateway that you can install on your local server, then use to create a pipeline to move data to Azure Storage.
From Azure Storage you can load the data into Azure Synapse staging tables by using Microsoft's PolyBase technology. You can run any transformations you need while the data is in staging, then insert it into production tables. Microsoft offers documentation for the whole process.
Keeping Google Analytics 360 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.
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
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.
Other data warehouse options
Azure Synapse 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 Panoply, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3. 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 Panoply, and To S3.
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 move data from Google Analytics 360 to Azure Synapse automatically. With just a few clicks, Stitch starts extracting your Google Analytics 360 data, structuring it in a way that's optimized for analysis, and inserting that data into your Azure Synapse data warehouse.