Google Analytics to Snowflake: 2 Easy Methods

Google Analytics is the most popular web analytics service on the market. It gathers crucial information on website events, such as web traffic, purchases, signups, and other aspects of browser/customer behavior. However, the vast amount of data that Analytics provides requires many users to search for ways to analyze the information more deeply within the platform.

Enter Snowflake, a platform designed from the ground up to be a cloud-based data warehouse. You can read more about Snowflake here. For many analytics users, Snowflake is the ideal solution for their data analysis needs. In this article, we will walk you through moving your data from Google Analytics to Snowflake.

Introduction to Google Analytics

Google Analytics is your ultimate tool for understanding how visitors interact with your website. Think of it as a friendly guide that helps you see what’s working, what’s not, and where you can improve. If you’ve ever wondered where your traffic is coming from or which pages are most popular, Google Analytics has the answers.

Features of Google Analytics

• Audience Insights: Learn about your visitors. Discover their demographics, interests, and behavior patterns to tailor your content and marketing efforts.
• Acquisition Reports: Find out how visitors are finding your site. Track performance across organic search, paid ads, social media, and email campaigns.
• Behavior Reports: Monitor visitor behavior on your site. Identify which pages are most popular, how long people stay, and where they leave.

Using Google Analytics feels like having a backstage pass to your website. It provides the insights you need to optimize your site and grow your business.

Introduction to Snowflake

Snowflake is a cloud-based data warehouse that’s super flexible and powerful. It makes it easy to store, manage, and analyze large amounts of data. Snowflake is similar to BigQuery in that it stores data separately from where it does its compute. It stores the actual data of your tables in S3 and then it can provision any number of compute nodes to process that data. 

In contrast, Snowflake offers instant access to unlimited resources (compute and storage) on-demand.

Snowflake Features:

• Scalable Architecture: Snowflake scales up or down automatically. Whether you have a few gigabytes or several petabytes of data, it adjusts to your needs without any hassle.
• Secure Data Sharing: Share data seamlessly and securely with your partners and stakeholders. No more cumbersome data transfers or security worries.
• Zero Management: Snowflake is fully managed, so you don’t need to worry about infrastructure, maintenance, or optimization. Instead, focus on your data analysis.

Using Snowflake feels like having a powerful yet easy-to-use data warehouse at your fingertips. It handles the heavy lifting so you can focus on gaining insights and making data-driven decisions.

Why Do We Need to Connect Google Analytics to Snowflake?

Connecting Google Analytics to Snowflake can supercharge your data analysis capabilities. Here’s why integrating these two powerful tools is a game-changer:

• Advanced-Data Analysis: Google Analytics provides a wealth of data, but analyzing it within the platform can be limiting. You can perform complex queries and deeper analysis by transferring your Google Analytics data to Snowflake. This means you can uncover insights that are impossible with Google Analytics alone.
• Data Centralization: If you’re using multiple data sources (like CRM, sales data, or other marketing platforms), Snowflake allows you to centralize all this information. By consolidating your Google Analytics data with other data sources in Snowflake, you get a holistic view of your business operations. This makes it easier to see the big picture and make informed decisions.
• Enhanced Historical Analysis: Google Analytics has limitations on how long it retains historical data. By syncing your data to Snowflake, you can store it indefinitely. This allows you to analyse long-term trends and track performance over extended periods, helping you understand seasonal trends and long-term growth patterns.
• Machine Learning and Predictive Analytics: By having your Google Analytics data in Snowflake, you can leverage machine learning models for predictive analytics. This can help you forecast future trends, understand customer behavior, and make proactive business decisions.

Connecting Google Analytics to Snowflake transforms your data analysis capabilities, making it easier to gain deeper insights and make data-driven decisions. It’s like upgrading from a basic toolkit to a fully-equipped data powerhouse.

This article provides an overview of both approaches. This will allow you to assess their pros and cons and choose the route that best suits your use case.

Method 1: Using Custom ETL Scripts to Move Data from Google Analytics to Snowflake

Here are the steps you can use to set up a connection from Google Analytics to Snowflake using Custom ETL Scripts:

• Step 1: Accessing Data on Google Analytics
• Step 2: Transforming Google Analytics Data
• Step 3: Transferring Data from Google Analytics to Snowflake
• Step 4: Maintaining Data on Snowflake

Step 1: Accessing Data on Google Analytics

The first step in moving your data is to access it, which can be done using the Google Analytics Reporting API. Using this API, you can create reports and dashboards, both for use in your Analytics account and other applications, such as Snowflake. However, when using the Reporting API, it is important to remember that only those with a paid Analytics 360 subscription will be able to utilize all the features of the API, such as viewing event-level data. At the same time, users of the free version of Analytics can only create reports using less targeted aggregate data.

Step 2: Transforming Google Analytics Data

Before transferring data to Snowflake, the user must define a complete and well-ordered schema for all included data. In some cases, such as with JSON or XML data types, data does not need a schema to be transferred directly to Snowflake. However, many data types cannot be moved quite so readily. If you are dealing with (for example) Microsoft SQL server data, more work is required on the user’s part to ensure that the data is compatible with Snowflake.

Google Analytics reports are conveniently expressed as spreadsheets, which map well to Snowflake’s similarly tabular data structures. However, it is important to remember that these reports are samples of primary data and may, as such, contain different values during separate report instances, even over the same time period sampled.

Because Analytics reports and Snowflake data profiles are so similarly structured, a common technique is to map each key embedded in a Report API endpoint response to a mirrored column on the Snowflake data table, thereby ensuring a proper conversion of necessary data types. Because data conversion is not automatic, it is incumbent on the user to revise data tables to keep up with any changes in primary data types.

Step 3: Transferring Data from Google Analytics to Snowflake

There are three primary ways of transferring your data to Snowflake:

1. COPY INTO – The COPY INTO command is perhaps the most common technique for data transferral, whereby data files (stored either locally or in a storage solution like Amazon S3 buckets) are copied into a data warehouse.
2. PUT – The PUT command may also be used. It allows the user to stage files before the COPY INTO command is executed.
3. Upload – Data files can be uploaded to a service such as the previously mentioned Amazon S3, allowing Snowflake to access them directly.

Step 4: Maintaining Data on Snowflake

Maintaining an accurate database on Snowflake is a never-ending battle; with every update to Google Analytics, older data must be analyzed and updated to ensure the integrity of the overarching data tables. This task is made somewhat easier by creating UPDATE statements in Snowflake, but you must also take care to identify and delete any duplicate records that appear in the database.

Overall, maintaining your newly created Snowflake database can be a time-consuming project, which is all the more reason to look for time-saving solutions such as Hevo.

Limitations of Using Custom ETL Scripts to Connect Google Analytics to Snowflake

Although there are other methods of integrating data from Google Analytics to Snowflake, those not using Hevo must be prepared to deal with a number of limitations:

1. Heavy Engineering Bandwidth: Building, testing, deploying, and maintaining the infrastructure necessary for proper data transfer requires a great deal of effort on the end user’s part.
2. Not Automatic: Each time a change is made in Google Analytics, time must be taken to alter the code manually to ensure data integrity.
3. Not Real-time: The steps set out in this article must be performed every single time data is moved from Analytics to Snowflake. For most users who move data regularly, following these steps every time will be a cumbersome, time-consuming ordeal.
4. Possibility of Irretrievable Data Loss: If at any point during this process, an error occurs, say, something changes in Google Analytics API or on Snowflake, serious data corruption and loss can result.

Method 2: Using Hevo Data to Move Data from Google Analytics to Snowflake

Step 1: Configure Google Analytics as Source

Connect Hevo with Google Analytics by simply logging in with your credentials.

Step 2: Connect Snowflake as Destination

Provide the details for configuring the Snowflake destination, such as the Destination Name, Account Name, Account Region, Database User, Database Password, Database Schema, and Database Name.

Conclusion

For users who seek a more in-depth understanding of their web traffic, moving data from Google Analytics to their Snowflake data warehouse becomes an important feat.

However, sifting through this can be an arduous and time-intensive process, a process that a tool like Hevo can streamline immensely, with no effort needed from the user’s end. Furthermore, Hevo is compatible with 150+ data sources, including 60+ Free Sources like Google Analytics, allowing users to interface with databases, cloud storage solutions, and more.

Still not sure that Hevo is right for you?

Try our risk-free, expense-free 14-day trial and experience for yourself the ease and efficiency provided by the Hevo Data Integration Platform. You can also look at the unbeatable pricing, which will help you choose the right plan for your business needs.

FAQs