Connect Google Sheets to Snowflake

Working with Snowflake data? Yeah, me too. And sometimes you just need to dig in, explore, or share your data in something simple – like a spreadsheet. That’s where Google Sheets comes into play: it’s lightweight, familiar, and lets you do quick, on-the-fly analysis. But getting your Snowflake data into Google Sheets isn’t always straightforward. So let’s break down how to do it, get you set up quickly, and then take a look at what to expect when using Sheets for this purpose. And hey, if you find Google Sheets isn't enough for your needs, I'll also throw in some alternative solutions, including a certain tool you might already know (hint: Gigasheet). Let's dive in.

Benefits of Connecting Google Sheets to Snowflake

• Access to Snowflake Data from a Familiar Interface: For those who prefer working in spreadsheets or need to share reports in Google Sheets format, connecting it to Snowflake makes data access easy.
• Real-Time Data: If set up properly, you can pull the most recent data from Snowflake directly into your Google Sheets, avoiding the need for manual exports.
• Simple Analysis and Sharing: Use the spreadsheet features to perform calculations, visualizations, and easily share insights with colleagues who may not have access to Snowflake.

Limitations of Connecting Google Sheets to Snowflake

• Limited Scalability: Unlike Gigasheet, Google Sheets is not designed to handle large datasets. Performance can slow significantly if you're working with more than a few thousand rows of data.
• Manual Data Refresh: Depending on how you connect the two, data may not refresh automatically, requiring you to manually trigger updates.
• Complexity in Querying: If your data needs are complex, working through Google Sheets' query functions may not be efficient. A dedicated data tool might provide more flexibility.

Now, let's get started with setting up the connection.

Step 1: Set Up a Snowflake Account and Role for Access

First, make sure you have access to a Snowflake account and the appropriate role with permissions to access the data you want to bring into Google Sheets. If you're the account administrator, you'll want to create a dedicated user for the Google Sheets integration with read-only access to ensure data security.

CREATE USER gsheets_user PASSWORD='your_secure_password';
GRANT ROLE gsheets_role TO USER gsheets_user;
GRANT SELECT ON DATABASE your_database TO ROLE gsheets_role;

‍

Replace gsheets_user, your_secure_password, gsheets_role, and your_database with your actual values.

Step 2: Connect Google Sheets to Snowflake

There are a few ways to connect Google Sheets to Snowflake. Two of the most common are via Google Apps Script or using a third-party Google Sheets add-on.

Option 1: Using Google Apps Script

If you want a more hands-on approach and control over the connection, using Google Apps Script can work well. Here’s how you can set this up:

Open Your Google Sheet: Start by opening a new or existing Google Sheet where you want to pull in Snowflake data.

Access Apps Script Editor:

1. Go to Extensions > Apps Script
2. This will open the Apps Script editor in a new tab.
   ‍

Add a JDBC Connection Script:

function getSnowflakeData() {
    var conn = Jdbc.getConnection(
      'jdbc:snowflake://<your_snowflake_account>.snowflakecomputing.com', 
      'gsheets_user', 
      'your_secure_password');
    var stmt = conn.createStatement();
    var results = stmt.executeQuery('SELECT * FROM your_table LIMIT 100');
    
    var sheet = SpreadsheetApp.getActiveSpreadsheet().getActiveSheet();
    sheet.clear(); // Clears existing data
    
    var numCols = results.getMetaData().getColumnCount();
    
    while (results.next()) {
        var row = [];
        for (var col = 0; col < numCols; col++) {
            row.push(results.getString(col + 1));
        }
        sheet.appendRow(row);
    }
    
    results.close();
    stmt.close();
    conn.close();
}

‍
Replace <your_snowflake_account>, gsheets_user, your_secure_password, and your_table with your actual values.
‍

Set Up Snowflake JDBC Driver:

1. You'll need to have the Snowflake JDBC driver available for Google Apps Script to connect to Snowflake. You may need to use a workaround or a third-party connector if the default JDBC options are not available.
2. Run the Script:
   • Save the script, then go back to your sheet and run getSnowflakeData from the script editor.
   • The data will populate your Google Sheet.

Option 2: Using a Google Sheets Add-On

If coding’s not your thing, don’t worry – there are plenty of third-party add-ons to help you bridge the gap between Google Sheets and Snowflake. There are more than a dozen options on the Google Workspace Marketplace, and new ones pop up regularly. Some of the notable ones are Supermetrics, CData Connect, and SeekWell. These add-ons provide a user-friendly way to pull Snowflake data into Google Sheets without needing to dig into scripts or Google Sheets APIs.

These tools are great for setting up quick queries and syncing data back and forth. You get a straightforward way to connect, pull in data, and schedule updates to keep your Sheets fresh without any heavy lifting. Each add-on has its own approach – some are better for visualization, others focus on data management – so you’ve got options to find the right fit for your needs.

But here’s the catch: while these add-ons can make your life easier, they don’t quite give you a real-time connection to Snowflake. What you’re usually setting up is a scheduled data sync – maybe hourly, daily, or at some other interval. And that means you’re working with a snapshot of your Snowflake data, not a live, real-time view. If something changes in Snowflake, there’s a lag before it’s reflected in your Google Sheet, which can create multiple copies of your data floating around and potentially lead to inconsistencies. Essentially, these add-ons don’t maintain a single source of truth – the data in your sheet could be outdated compared to what’s actually in Snowflake.

And when it comes to handling bigger data sets or more complex analysis, you’ll likely hit some limitations with these add-ons. Google Sheets can only handle so much before performance starts to degrade, and data sync times can become cumbersome. That’s where tools built specifically for Snowflake, like Gigasheet, come into play. With Gigasheet, you get a more scalable solution designed to work seamlessly with large datasets and provide a familiar spreadsheet interface for exploring data without any of the limitations you’d face with a Google Sheets add-on. And because Gigasheet allows for direct querying of Snowflake, you’re always working with real-time data – no data silos, no outdated copies, just one source of truth.

Alternatives to Connecting Google Sheets and Snowflake

While connecting Google Sheets to Snowflake directly has its perks, you may find that the scalability and flexibility of this method are limiting for complex data needs. Here are some alternatives to consider:

1. Gigasheet for Snowflake

Gigasheet is built to make working with Snowflake data spreadsheet-easy and scalable. It's designed to handle BIG data seamlessly, bringing the familiar experience of working in a spreadsheet to a tool that’s purpose-built for large datasets. If you're finding Google Sheets isn't cutting it for data size or complexity, Gigasheet steps in to fill that gap, providing an intuitive interface for querying, visualizing, and even pushing data back to Snowflake.

How Gigasheet Stands Out:

• Scalable Data Handling: Google Sheets can handle some basic analysis, but once you go beyond a few thousand rows, things start to break down. Gigasheet is engineered to handle billions of rows of data without performance issues (we process about 20B rows of data every month). That means no matter how large your Snowflake dataset, Gigasheet can process it quickly, allowing you to filter, join, pivot, and analyze without a hitch.
• Spreadsheet Interface with No Code: The Gigasheet UI is like a spreadsheet on steroids, giving you all the tools you're familiar with – sorting, filtering, formulas – but at a scale that typical spreadsheets can’t match. This allows you to explore your Snowflake data without needing to write SQL or scripts, making it accessible for analysts and business users alike.
• Direct Integration and Live Data Access: With Gigasheet, you connect directly to Snowflake and Gigasheet queries the data live generating SQL for you behind the scenes. You can analyze full tables or run custom queries, visualizing the data right in Gigasheet. This direct integration means that when you work on your analysis, you’re pulling the most up-to-date data straight from Snowflake – ensuring that you're always working with a live view of your data and maintaining a single source of truth.
• Write-Back Capabilities for Data Cleaning and Uploads: One of Gigasheet’s standout features is its ability to not only analyze data from Snowflake but also to write back changes. If you’re cleaning or fixing data (say, standardizing names or correcting values), Gigasheet allows you to push those changes back into Snowflake in a governed way, ensuring data integrity and consistency across your team. This process is managed securely, with user access controls to make sure data changes are made responsibly.
• Additionally, if you need to upload new data – like CSVs, Excel files, JSON, or data from other tools – Gigasheet allows you to bring this data into Snowflake for inspection by your data team and incorporated into your data warehouse. This capability makes Gigasheet an excellent tool for staging and enriching your data before it hits your primary Snowflake tables.
• Collaborative Data Exploration: Sharing insights and collaborating across teams is easy. You can invite others to view, comment, or edit Gigasheet spreadsheets without requiring them to access Snowflake directly. This makes it perfect for non-technical stakeholders who need access to data or for teams that need to collaborate on datasets.
• Customizable Data Workflows and Automation: Gigasheet’s REST API enables advanced workflows and automation. Whether you want to enrich your data from external sources, automate data refreshes, or programmatically perform transformations, Gigasheet allows for highly customizable processes. This is particularly useful for regularly updated datasets or when you need to prepare specific views for business users.

2. Looker Studio (formerly Google Data Studio)

If you’re looking for a visual, dashboard-centric approach to your Snowflake data, Looker Studio is a popular option. It allows you to build interactive reports and dashboards, connecting directly to Snowflake to pull in your data for visualization and tracking metrics in real time.

Advantages:

• Build interactive dashboards and custom visualizations that refresh based on your Snowflake data.
• Combine data from multiple sources into one view for comprehensive reporting.
• Share dashboards with stakeholders easily, even if they don't have direct access to Snowflake.

Considerations:

• Complex Setup and Learning Curve: While Looker Studio provides great visualization capabilities, getting started can be complex. You’ll need to understand how to configure the data connections properly, and the process of building dashboards has its own learning curve, especially for users unfamiliar with BI tools.
• Limited Data Manipulation: Looker Studio is primarily a reporting and visualization tool. If your needs go beyond creating charts and visualizations and require in-depth data manipulation, cleansing, or analysis, it falls short. For these kinds of activities, a spreadsheet-like interface (like Gigasheet) or a dedicated data prep tool would be more effective.
• Lag in Real-Time Updates: Although Looker Studio can pull data from Snowflake, it’s not truly real-time like direct querying. Data refreshes may lag based on the intervals set up in the connection, so you won’t always have the most up-to-date view. This can be an issue when you're trying to make timely, data-driven decisions.
• Potential for Performance Issues with Large Datasets: When working with large datasets, performance may degrade. Complex visualizations or blending multiple data sources can slow down the report rendering. Unlike Gigasheet, which is optimized for handling large datasets efficiently, Looker Studio may struggle with performance on large-scale Snowflake tables.
• Cost and Governance Challenges: Depending on your Looker licensing and setup, there may be additional costs associated with the integration, particularly if you need to create a lot of custom dashboards or require advanced functionality. And while sharing dashboards is easy, it may be more difficult to govern who can view or alter underlying data, which can lead to discrepancies in data security and compliance.

Looker Studio shines when you need to create visual reports and dashboards for stakeholders. But if you're aiming for deep data analysis, ad-hoc investigation, data cleaning, or real-time exploration, Gigasheet’s direct integration and write-back capabilities may provide a more comprehensive solution.

3. Other BI Tools (Tableau, Power BI)

If your needs go beyond simple spreadsheet functionalities and you require advanced analytics or data visualization capabilities, BI tools like Tableau and Power BI are solid options. They have native connectors to Snowflake, allowing for complex analysis and dashboard creation.

Advantages:

• Powerful Data Visualization & Analytics: Both tools excel at building comprehensive visual reports and dashboards, enabling deep data exploration and storytelling.
• Real-Time Connections: With connectors to Snowflake, you can refresh your data in near-real-time, providing up-to-date insights.
• Advanced Analytics Features: These platforms support a wide range of analytics features, from simple charts to predictive modeling, allowing for sophisticated data exploration.

Considerations:

• Lack of Write-Back Capabilities: A key limitation of using Tableau or Power BI is the inability to make changes or clean data and push those updates back to Snowflake. If you're looking to fix or transform your data and maintain a governed single source of truth, you'll need to handle those workflows separately, which adds complexity. For this use case, Gigasheet's write-back functionality is a significant advantage, allowing you to not only analyze but also modify and enrich your data directly in Snowflake.
• Complex Setup and Governance: While these BI tools are powerful, they often require a more complex setup. Configuring data sources, managing security, and creating dashboards come with a steep learning curve. This is particularly true for teams not familiar with traditional BI workflows, and maintaining data governance can become a challenge as data gets exported and shared.
• Limited Flexibility for Ad-Hoc Data Exploration: BI tools like Tableau and Power BI are built for visualization, not ad-hoc data exploration or on-the-fly analysis. If you need to manipulate, transform, or analyze your Snowflake data without rigid dashboards or pre-built reports, the spreadsheet-like approach of Gigasheet provides a lot more flexibility and ease of use.
• Performance Issues with Large Datasets: Just like Google Sheets add-ons, both Tableau and Power BI may encounter performance bottlenecks when handling extremely large datasets. While they can handle more data than Google Sheets, the user experience might still slow down when working with massive Snowflake tables, especially during complex calculations or visualizations.
• Cost and Licensing: The licensing model for BI tools can be costly, especially as your user base grows or you require more advanced features. Additionally, these costs often scale as you bring in more data sources and users, which can make them a more significant investment than initially anticipated.

In summary, if your primary need is visualization and dashboarding, BI tools like Tableau and Power BI are great options. But for those who need not only to analyze data but also clean, transform, and push updates back into Snowflake – all in a flexible, scalable environment – Gigasheet provides a more comprehensive and efficient solution.

Conclusion: Finding the Right Fit for Your Snowflake Workflow

When it comes to connecting Snowflake to Google Sheets, Looker Studio, Tableau, Power BI, or any other tool – there's no one-size-fits-all solution. Each has its strengths, but the key is knowing what you need to get done.

If you’re looking for a quick and simple way to pull some Snowflake data into a spreadsheet, and your datasets are small enough, a Google Sheets add-on might be all you need. They're user-friendly, easy to set up, and great for basic use cases. But as soon as you start dealing with larger datasets, need real-time access, or want more advanced capabilities, those add-ons will start to show their limits – not to mention that they're creating multiple copies of your data and not offering any way to push clean data back to Snowflake.

BI tools like Tableau and Power BI are heavy hitters when it comes to data visualization and dashboarding. They’re fantastic for telling stories with your data, combining sources, and diving deep into analytics. But if you need the agility to explore, clean, and manipulate Snowflake data ad hoc, and especially if you need write-back capabilities to maintain a single source of truth, BI tools can be rigid and leave gaps in your workflow.

Looker Studio is an excellent middle ground for those who need more visual reporting than Google Sheets but don’t need all the bells and whistles of enterprise BI tools. It allows for some flexibility in how you visualize and share data from Snowflake. However, it still falls short when it comes to data prep, real-time data syncing, and making changes that are pushed back to your data warehouse. If you're trying to balance flexibility with visual appeal, Looker Studio has its perks, but the gaps become apparent as soon as you need more direct data exploration or governance.

Now, when you look at Gigasheet – it’s different by design. It’s built for those who need to explore, clean, and work with big data at scale, without breaking a sweat. If you need to dig deep into your Snowflake data, run complex queries, or transform data in a way that's easy to share with your team, Gigasheet gives you that spreadsheet-like interface that feels familiar but works on a much bigger level. And it doesn't stop there – Gigasheet’s write-back capabilities allow you to not only analyze but also clean and push updates directly back to Snowflake. This isn’t just about seeing your data; it's about improving it, enriching it, and closing the loop on your data workflows, all while keeping your Snowflake warehouse as your single source of truth.

So here's the takeaway: If you're dealing with simple, quick, and lightweight needs, those add-ons can be a good fit. For serious dashboarding and visual storytelling, Tableau and Power BI shine. But if you're looking to do in-depth, large-scale data analysis and exploration, all while having the power to write back changes, clean up data, or add new data to your Snowflake warehouse, Gigasheet provides a level of control and scalability that the other options just don’t.

Pick the right tool for the job, but make sure it’s one that can grow with your data – and your business.