You are going about your day setting up and operating your organization’s data infrastructure and preparing it for further analysis. Suddenly, you get a request from one of your team members to replicate data from Google Analytics 4 to Snowflake.
We are here to help you out with this problem. You can replicate data from Google Analytics 4 to Snowflake using custom ETL scripts or pick an automated tool to do the heavy lifting for you. This article provides a step-by-step guide for both of them.
How to connect Google Analytics 4 to Snowflake?
Two methods are explained below to replicate data from Google Analytics 4 to Snowflake.
Export Google Analytics 4 to Snowflake using BigQuery
Step 1: Export Google Analytics 4 to BigQuery
Google Analytics 4 exports event data at the user level to BigQuery for free. So the first step is to export from Google Analytics 4 to BigQuery.
Step 2: Export from BigQuery to GCS
To manually export, create a GCS bucket to receive the exported tables. Exporting in Parquet format with snappy compression will work:
bq extract --destination_format=PARQUET --compression=SNAPPY bigquery-public-data:ga4_obfuscated_sample_ecommerce.events_20201202 gs://your-bucket/yourprefix/ga4sample-20201202-*
Step 3: Now prepare Snowflake to read from GCS
Simply follow the steps in this link to configure Snowflake to read from Google Cloud Storage securely.
Step 4: Read the exported Parquet files in Snowflake
Create a table in Snowflake to read the exported files:
list @fh_gcp_stage; -- check files exist
create or replace table ga4_variant(v variant);
copy into ga4_variant
file_format = (type='PARQUET');
Your data is now ready to query in Snowflake.
Using CSV files and SQL queries is a great way to replicate data from Google Analytics 4 to Snowflake effectively. It is ideal in the following situations:
- One-Time Data Replication: When your business teams require these GA4 files only quarterly, annually, or for a single occasion, manual effort and time are justified.
- No Transformation of Data Required: This strategy offers limited data transformation options. Therefore, it is ideal if the data in your spreadsheets is accurate, standardized, and presented in a suitable format for analysis.
- Lesser Number of Files: Downloading and composing SQL queries to upload multiple CSV files is a time-consuming task. It can be particularly time-consuming if you need to generate a 360-degree view of the business and merge spreadsheets containing data from multiple departments across the organization.
When your business teams require fresh data from multiple reports every few hours, you face a challenge. In order for them to make sense of this data in various formats, it must be cleaned and standardized. This eventually causes you to devote a substantial amount of engineering bandwidth to the creation of new data connectors.
To ensure a replication with zero data loss, you must also monitor any changes to these connectors and fix data pipelines on an ad hoc basis. These additional tasks consume forty to fifty percent of the time you could have spent on your primary engineering objectives.
So, is there a simpler yet effective alternative to this? You can use the method mentioned below.
Going all the way to write custom scripts for every new data connector request is not the most efficient and economical solution. Frequent breakages, pipeline errors, and lack of data flow monitoring make scaling such a system a nightmare.
You can streamline the Google Analytics 4 to Snowflake data integration process by opting for an automated tool. To name a few benefits, you can check out the following:
- It allows you to focus on core engineering objectives while your business teams can jump on to reporting without any delays or data dependency on you.
- Your marketers can effortlessly enrich, filter, aggregate, and segment raw Firebase Analytics data with just a few clicks.
- The beginner-friendly UI saves the engineering team hours of productive time lost due to tedious data preparation tasks.
- Without coding knowledge, your analysts can seamlessly standardize timezones or aggregate campaign data from multiple sources for faster analysis.
- Your business teams get to work with near-real-time data with no compromise on the accuracy & consistency of the analysis.
As a hands-on example, you can check out how Hevo, a cloud-based No-code ETL/ELT Tool, makes the Google Analytics 4 to Snowflake data replication effortless in just 2 simple steps:
Step 1: Configure Google Analytics 4 as your Source
Configure Google Analytics 4 as the source.
Note: You can select from the “Historical Sync Duration” according to your requirements, where the default duration is 6 months. You can enable the “Pivot Report” option if you want to create an aggregated report based on the dimensions and metrics selected.
Step 2: Configure Snowflake as your Destination
You can also visit the official documentation of Hevo for Google Analytics 4 as a source and Snowflake as a destination to have in-depth knowledge about the process.
In a matter of minutes, you can complete this No-Code & automated approach of connecting Google Analytics 4 to Snowflake using Hevo and start analyzing your data.
Hevo’s fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.
Hevo’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work. By employing Hevo to simplify your data integration needs, you get to leverage its salient features:
Get started for Free with Hevo!
- Fully Managed: You don’t need to dedicate any time to building your pipelines. With Hevo’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
- Data Transformation: Hevo provides a simple interface to cleanse, modify, and transform your data through drag-and-drop features and Python scripts. It can accommodate multiple use cases with its pre-load and post-load transformation capabilities.
- Faster Insight Generation: Hevo offers near real-time data replication, so you have access to real-time insight generation and faster decision-making.
- Schema Management: With Hevo’s auto schema mapping feature, all your mappings will be automatically detected and managed to the destination schema.
- Scalable Infrastructure: With the increase in the number of sources and volume of data, Hevo can automatically scale horizontally, handling millions of records per minute with minimal latency.
- Transparent pricing: You can select your pricing plan based on your requirements. Different plans are clearly put together on its website and all the features it supports. You can adjust your credit limits and spend notifications for any increased data flow.
- Live Support: The support team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
What Can You Achieve by Migrating Data from Google Analytics 4 to Snowflake?
Here’s a little something for the data analyst on your team. We’ve mentioned a few core insights you could get by replicating data from Google Analytics 4 to Snowflake. Does your use case make the list?
- Which Demographic contributes to the highest fraction of users of a particular Product Feature?
- How do Paid Sessions and Goal Conversion Rates vary with Marketing Spend and Cash flow?
- How to identify your most valuable customer segments?
Summing It Up
These data requests from your marketing and product teams can be effectively fulfilled by replicating data from Google Analytics 4 to Snowflake using BigQuery. If data replication must occur every few hours, you will have to switch to a custom data pipeline. This is crucial for marketers, as they require continuous updates on the ROI of their marketing campaigns and channels. Instead of spending months developing and maintaining such data integrations, you can enjoy a smooth ride with Hevo’s 150+ plug-and-play integrations (including 40+ free sources such as Google Analytics 4).
Visit our Website to Explore Hevo
Saving countless hours of manual data cleaning & standardizing, Hevo’s pre-load data transformations get it done in minutes via a simple drag-and-drop interface or your custom Python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
Want to take Hevo for a ride? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.
Share your experience of replicating data from Google Analytics 4 to Snowflake! Let us know in the comments section below!