Connecting Google Analytics 4 with Snowflake allows teams to analyze user engagement data alongside the rest of their business data, enabling deeper insights and more informed decision-making. While Google Analytics 4 provides rich reporting on user behavior across web and mobile applications, exporting that data into Snowflake makes it easier to run advanced queries, join with other datasets, and support downstream analytics.
Snowflake serves as a centralized, scalable data platform for aggregating data from multiple sources. A GA4-to-Snowflake integration enables cross-channel analysis, historical reporting, and near real-time analytics that go beyond what’s possible in the GA4 interface alone.
This guide explains Google Analytics 4 and Snowflake at a high level and walks through the most common ways to load GA4 data into Snowflake.
Key Takeaways
Integrating Google Analytics 4 with Snowflake enables deeper analysis by combining user engagement data with other business datasets.
CSV exports and GA4–BigQuery integrations are suitable for one-time analysis or batch workflows but do not scale well for ongoing analytics.
Continuous GA4 ingestion into Snowflake is better suited for production use cases that require fresh and reliable data.
Estuary provides a managed approach for continuously loading GA4 data into Snowflake and supports Snowpipe Streaming for low-latency updates.
Choosing the right integration method depends on data freshness requirements, operational complexity, and long-term scalability.
Google Analytics 4: An Overview
Google Analytics 4 (GA4) is an analytics service that allows businesses to measure traffic and engagement across their apps and websites. It is built upon the foundation of its predecessor, Universal Analytics, and includes several enhancements that cater to evolving business needs.
While page views were the most important metric in Universal Analytics, the focus of GA4 is to track events. This includes specific user interactions, such as clicks, views, form submissions, etc. Instead of seeing generalized data, you now gain a deeper understanding of user engagement and how they interact with your app and website. By combining data tracking information for both app and web interactions, you can obtain a unified view of user behavior across multiple digital platforms.
Yet another impressive feature of GA4 is its Life cycle collection feature, which allows you to track the entire user journey from acquisition to retention. Understanding the complete lifecycle of your users can help you optimize strategies for improved customer experiences.
Snowflake: An Overview
Snowflake is a cloud-based data warehouse built on top of the Microsoft Azure, Amazon Web Services, and Google Cloud infrastructure. It offers a fully managed and scalable solution for data warehousing, data lakes, data sharing, and data engineering.
Snowflake is a popular data warehouse choice because it separates storage from compute, enabling on-demand scaling and preventing resource contention. Similar to a shared disk architecture, Snowflake uses a central data repository for persisting data that is accessible from all compute nodes of the platform. And to process queries, Snowflake uses Massively Parallel Processing (MPP) compute clusters. Multiple clusters can access the same data simultaneously, and each node in the cluster stores a portion of the entire data set locally.
If you want to access historical data (modified or deleted data) at any point within a defined period, you can use Snowflake’s Time Travel feature. It helps:
- Restore data-related objects, such as tables, schemas, and databases, that might have been accidentally or intentionally deleted.
- Duplicate and back up data from key points in the past.
- Analyze data usage/manipulation over specified time periods.
Methods to Move Data from Google Analytics 4 to Snowflake
There are three commonly used ways to integrate Google Analytics 4 with Snowflake. The right approach depends on whether you need a one-time export, periodic batch loads, or a continuous, production-ready data pipeline.
Method #1: Export Google Analytics 4 Data as CSV Files
Google Analytics 4 allows you to export reports as CSV files directly from the GA4 interface and manually load them into Snowflake.
This method works for:
- Small datasets
- Ad-hoc analysis
- One-time or infrequent reporting needs
However, CSV exports are limited in row count per report, require manual effort, and do not support automation or real-time updates. This approach does not scale for ongoing analytics or production use cases.
Method #2: Use the GA4–BigQuery Integration, Then Load Data into Snowflake
Google Analytics 4 can export data automatically to BigQuery, where raw or aggregated analytics data is stored in partitioned tables. From there, data can be transferred into Snowflake using cloud storage (such as Google Cloud Storage) and Snowflake’s COPY operations.
This method is useful when:
- You already rely on BigQuery for GA4 exports
- You need access to raw GA4 event data
- Batch-based analytics are sufficient
However, this approach introduces additional infrastructure, increases complexity, and typically results in delayed data availability in Snowflake. It is best suited for batch analytics rather than near real-time use cases.
Method #3: Use Estuary for Continuous Google Analytics 4 to Snowflake Ingestion
Estuary provides a managed way to continuously ingest Google Analytics 4 data into Snowflake without relying on CSV exports or BigQuery as an intermediary.
Estuary connects directly to Google Analytics 4 via the Google Analytics Data API and streams report data into Snowflake using native Snowflake ingestion mechanisms. This approach supports near real-time or scheduled batch delivery, depending on your requirements.
This method is best suited for:
- Continuous GA4 data ingestion into Snowflake
- Analytics teams that need fresh data with low latency
- Production pipelines that must be reliable and maintainable
Estuary supports Snowpipe Streaming for delta updates, enabling low-latency ingestion directly into Snowflake tables without file-based staging or scheduled COPY jobs. This makes it easier to power up-to-date dashboards and downstream analytics while keeping operational complexity low.
Summary: Choosing the Right GA4 to Snowflake Integration Method
- CSV exports are best for one-time analysis and small datasets
- GA4 to BigQuery works for batch analytics and raw event access
- Estuary is the most suitable option for continuous, low-latency GA4 to Snowflake pipelines
Each method differs in setup complexity, data freshness, and long-term maintainability. The sections below walk through each approach in detail, so you can choose the option that best fits your analytics and operational requirements.
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Method #1: Using CSV Files Export/Import to Move Data from Google Analytics 4 to Snowflake
You can export a Google Analytics 4 report as a CSV file and then load it into a Snowflake database. Here are the steps involved in this process:
Step 1: Export Data from Google Analytics 4 as CSV Files
- On the Google Analytics dashboard, select the Reports icon from the left menu.
- Click on the share icon on the top right of a report.
- Click on Download file and select Download CSV. The CSV file will be available in your downloads folder.
Step 2: Load the CSV Files to Snowflake
To load into a Snowflake database using the Classic Console, follow these steps:
- Select Databases and then choose a specific database and schema.
- Click on the Tables tab and locate the table into which you want to load data.
- Either select a table row and click on Load Data or select a table name and click on Load Table.
- In the Load Data wizard, select a warehouse to load data into the table, and then click on Next.
- Navigate through Load files from your computer → Select Files, and browse the files you want to load.
- After selecting the CSV files of your Google Analytics 4 reports, click on Open and select Next.
- From the dropdown list, select the CSV file format and click on Load. Finally, click on OK to close the Load Data wizard.
Method #2: Using GA4-BigQuery Integration to Move GA4 Data to Snowflake
You can export all of your raw events from Google Analytics 4 properties to BigQuery for free and then transfer the data to Snowflake. Here are the steps to follow:
- Set up a BigQuery Export by following the steps for a GA4-BigQuery Export integration.
- Next, create a GCS bucket to receive the exported tables from BigQuery, since Snowflake can read files directly from GCS. Use Parquet format with snappy compression and execute the following command using BigQuery’s command-line tool:
plaintextbq extract --destination_format=PARQUET --compression=SNAPPY bigquery-public-data:ga4_obfuscated_sample_ecommerce.events_20221202 gs://your-bucket/yourprefix/ga4sample-20221202-*bigquery-public-data:ga4_obfuscated_sample_ecommerce.events_20221202 must be replaced with the full name of the source BigQuery table from which data will be extracted.
gs://your-bucket/yourprefix/ga4sample-20221202-* must be replaced with the GCS location where the extracted data will be saved.
- Configure Snowflake to read from GCS securely by following these steps.
- Create a table in Snowflake to read the exported files with:
plaintextlist @fh_gcp_stage; -- check files exist
create or replace table ga4_variant(v variant);
copy into ga4_variant from @fh_gcp_stage/yourprefix/
pattern='yourprefix/ga4sample-.*'
file_format = (type='PARQUET');This script will load all files in the specified folder while skipping the ones it has already loaded into the specified table.
Upon completing these steps, your GA4 data is now ready to query in Snowflake.
Method #3: Use Estuary for Continuous Google Analytics 4 to Snowflake Ingestion
Estuary provides a managed way to continuously move data from Google Analytics 4 into Snowflake, without building and maintaining custom ingestion pipelines. Estuary is the Right-Time Data Platform that lets teams move data when they choose (sub-second, near real-time, or batch), making it well suited for keeping analytics data fresh and query-ready in Snowflake.
To set up a Google Analytics 4 to Snowflake pipeline with Estuary, you’ll need an Estuary account. If you don’t already have one, register for a free account. Then sign in and follow these steps.
Step 1: Configure Google Analytics 4 as the Data Source
Prerequisites to use the Google Analytics 4 connector:
- Your Google Analytics 4 property ID.
- The Google Analytics Data API enabled on the Google project associated with your Analytics property
From the Estuary dashboard, click Sources, then select + NEW CAPTURE. Use the Search connectors box to find Google Analytics 4, and click Capture.
Image Source: Estuary
On the connector setup page, provide the required details, such as a Name for the capture, Property ID, and Start Date. You can authenticate using OAuth through the Estuary web application or by supplying a service account JSON key. Once complete, click NEXT, then SAVE AND PUBLISH.
Image Source: Estuary
The connector captures data from Google Analytics 4 into Estuary collections via the Google Analytics Data API. Supported data resources include:
- Daily active users
- Devices
- Four-weekly active users
- Locations
- Pages
- Traffic sources
- Website overview
- Weekly active users
Each resource is fetched as a report and mapped to a separate Estuary collection. Custom reports can also be captured when additional dimensions or metrics are required.
Step 2: Configure Snowflake as the Data Destination
Before configuring the Snowflake connector, ensure your Snowflake environment is prepared with a target database, schema, warehouse, and a user or role with the appropriate permissions.
After the capture is published, click MATERIALIZE COLLECTIONS from the capture confirmation screen, or navigate to Destinations → + NEW MATERIALIZATION in the Estuary dashboard.
Search for Snowflake and select the Snowflake connector. Enter the required configuration details, including a Name for the materialization, Host URL, Account, Database, and Schema.
Image Source: Estuary
Important update: Snowflake deprecated password-based authentication in 2025. Estuary’s Snowflake connector uses key-pair (JWT) authentication, which is required for production deployments and for enabling advanced ingestion features.
Once configured, click NEXT → SAVE AND PUBLISH. Estuary will materialize your Google Analytics 4 collections into tables in Snowflake.
Image Source: Estuary
Snowpipe Streaming support: When using delta updates, Estuary can ingest data using Snowpipe Streaming, Snowflake’s lowest-latency ingestion mechanism. This allows data to be written directly into Snowflake tables as it is processed, reducing latency and eliminating the need for file-based staging or scheduled batch loads.
For more details on connector configuration and advanced options, see the Estuary documentation:
Conclusion
Integrating Google Analytics 4 with Snowflake allows teams to analyze user engagement data alongside the rest of their business data, enabling more advanced analytics and better decision-making. While GA4 offers powerful reporting within its own interface, moving that data into Snowflake unlocks greater flexibility for querying, historical analysis, and cross-domain insights.
There are multiple ways to connect GA4 to Snowflake. CSV exports and GA4–BigQuery integrations can work for one-time analysis or batch-based workflows, but they involve manual steps, added infrastructure, or delayed data availability. These approaches are generally best suited for limited or non-operational use cases.
For teams that need a reliable, ongoing GA4 to Snowflake pipeline, Estuary provides a more scalable option. By ingesting GA4 data continuously and supporting Snowpipe Streaming for low-latency updates, Estuary helps keep Snowflake analytics current without relying on manual exports or complex intermediary systems.
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