Access your data on Google Analytics

The first step in loading data from Google Analytics to any kind of data warehouse solution is to access your data and start extracting it.

The Google Analytics Reporting API is the most advanced programmatic method to access report data in Google Analytics. The API also allows you to programmatically interact with the Google Analytics account, creating reports and dashboards that can be viewed from within your GA account but also embed them into other applications.

Data from Google Analytics is always coming in the form of a report, which means that you have to construct a report and request it from Google Analytics for a specific time period.

Google Analytics is accessed in the same way that every other Google API is, you need to leverage the Google API console to manage applications and access to the various APIs, including Google Analytics.

In addition to the above, the things that you have to keep in mind when dealing with the Google Analytics API, are:

1. Rate limits. Every API has some rate limits that you have to respect.
2. Authentication. You authenticate on Google Analytics using an OAuth.
3. Paging and dealing with a big amount of data. Platforms like Google Analytics that are dealing with clickstream data tend to generate a lot of data, like events on your web properties.

Transform and prepare your Google Analytics Data

After you have accessed your data on Google Analytics, you will have to transform it based on two main factors,

1. The limitations of the database that the data will be loaded onto
2. The type of analysis that you plan to perform

Each system has specific limitations on the data types and data structures that it supports. If for example, you want to push data into Google BigQuery, then you can send nested data like JSON directly, but keep in mind that every data you get from Google Analytics are in the form of a tabular report closer to what a CSV or a spreadsheet looks like.

Of course, when you are dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data, just as in the case of JSON, before loading it into the database.

Also, you have to choose the right data types. Again, depending on the system that you will send the data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database. Google Analytics has a very limited set of available data types which means that your work to do these mappings is much easier and straightforward, but nonetheless equally important with any other case of a data source.

In order to understand and model correctly the data by Google Analytics, you will need to understand that data coming out of it are in the form of a report. The report is like a spreadsheet and it can be naturally mapped into a table. So more or less you will end up with a one-to-one mapping between a report and a table on your database.

You also need to keep in mind that because of the report nature of data, you will not find any primary keys that can be used for deduplication and reference. This is something that you have to construct by understanding the nature of your report’s data.

Also, as Google analytics is sampling data to generate the report, you might see slightly different values if you pull the same report, for the same period, more than once.

Transform and prepare Google Analytics data for Amazon Redshift

Amazon Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, in order to load your data into it, you will have to follow its data model which is a typical relational database model. Data you extract from your data source should be mapped into tables and columns. Where you can consider the table as a map to the resource you want to store and columns the attributes of that resource.

Also, each attribute should adhere to the data types that are supported by Redshift.

As your data is probably coming in a representation like JSON that supports a much smaller range of data types you have to be really careful about what data you feed into Redshift and make sure that you have mapped your types into one of the data types that are supported by Redshift.

Designing a Schema for Redshift and mapping the data from your data source to it is a process that you should take seriously as it can both affect the performance of your cluster and the questions that you can answer. It’s always a good idea to have in your mind the best practices that Amazon has published regarding the design of a Redshift database. When you have concluded on the design of your database you need to load your data on one of the data sources that are supported as input by Redshift, these are the following:

1. Amazon S3
2. Amazon DynamoDB
3. Amazon Kinesis Firehose

Load your Google Analytics data into Amazon Redshift

To upload your data to Amazon S3 you will have to use the AWS REST API, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse. The first task that you have to perform is to create a bucket, you do that by executing an HTTP PUT on the Amazon AWS REST API endpoints for S3.

You can do this by using a tool like CURL or Postman. Or use the libraries provided by Amazon for your favorite language. You can find more information by reading the API reference for the Bucket operations on Amazon AWS documentation.

After you have created your bucket you can start sending your data to Amazon S3, using again the same AWS REST API but by using the endpoints for Object operations. As in the Bucket case you can either access the HTTP endpoints directly or use the library of your preference.

Amazon Redshift supports two methods for loading data into it. The first one is by invoking an INSERT command. You can connect to your Redshift instance with your client, using either a JDBC or ODBC connection and then you perform an INSERT command for your data.

The way you invoke the INSERT command is the same as you would do with any other SQL database, for more information you can check the INSERT examples page on the Redshift documentation.

Redshift is not designed for INSERT like operations, on the contrary, the most efficient way of loading data into it is by doing bulk uploads using a COPY command.

You can perform a COPY command for data that lives as flat files on S3 or from an Amazon DynamoDB table. When you perform COPY commands, Redshift is able to read multiple files simultaneously and it automatically distributes the workload to the cluster nodes and performs the load in parallel.

The best way to load data from Google Analytics to Amazon Redshift

So far we just scraped the surface of what you can do with Redshift and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources.

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