How to Export Data from Snowflake to Excel

Getting your valuable data from Snowflake into Excel doesn't need to be a complex technical chore. Whether you need a quick data dump for a one-off analysis or want to create a refreshable report, there are straightforward methods available. This tutorial will walk you through the most practical ways to export your Snowflake data to Excel, from the simplest download to setting up a live connection.

The Easiest Method: Downloading Results Directly from Snowsight

For quick one-time data exports, the simplest approach is to run a query in the Snowflake web interface (Snowsight) and download the results directly as a CSV file, which you can then open in Excel.

When to Use This Method:

• You need a static snapshot of data for immediate analysis.
• You're testing a query and want to see the output in a familiar spreadsheet format.
• The dataset is relatively small (under Excel's row limit and not so large that the query takes too long to run).

Step-by-Step Instructions:

1. Log into Snowflake and navigate to a worksheet in Snowsight.
2. Write Your SQL Query: Write the query to select the data you need. A very important tip: Be specific. Avoid using SELECT * from a massive table. Instead, select only the columns you need and use a WHERE clause to filter the rows. If you're just exploring, add a LIMIT clause to pull a smaller, manageable sample.

SELECT
  order_id,
  customer_name,
  order_date,
  order_total
FROM
  your_database.your_schema.orders_table
WHERE
  order_date >= '2023-01-01'
LIMIT
  5000,

1. Run the Query: Execute your query by pressing Ctrl+Enter (or Cmd+Enter on Mac) or clicking the blue "Run" button in the top right.
2. Download the Results: Once the query finishes and your data appears in the Results pane at the bottom, look for the "Download" icon. It’s located in the upper-right corner of the Results pane.
3. Choose Your Format: Click the download icon, and you’ll be given an option to export as a Tab Separated Values (TSV) or Comma Separated Values (CSV) file. For Excel, CSV is the perfect choice. The file will download to your computer.
4. Open in Excel: Navigate to the downloaded CSV file and open it. Excel will automatically parse the comma-separated data into distinct columns, and you're ready to start your analysis.

This method is your go-to for speed and simplicity. But if you need to create reports that you’ll update regularly, the next method is a far better choice.

For Live Data: Connecting Excel to Snowflake with the ODBC Driver

If you're building a report in Excel that needs to be updated weekly or monthly, manually downloading a CSV each time is tedious and prone to error. By using the Snowflake ODBC (Open Database Connectivity) driver, you can create a direct, live connection. This means you can refresh your data in Excel with a single click, pulling the latest information directly from your Snowflake warehouse.

When to Use This Method:

• You are creating recurring reports or dashboards in Excel.
• You want to be able to refresh your data without leaving Excel.
• You're comfortable with a one-time setup process to enable this powerful workflow.

This process has three main parts: installing the driver, configuring the connection, and then pulling the data into Excel.

Part 1: Download and Install the Snowflake ODBC Driver

The ODBC driver is a small piece of software that acts as a translator, allowing Excel to communicate with Snowflake. You'll need to install it on your computer first.

1. Log into your Snowflake account and click on Help (look for the question mark icon) in the top right corner.
2. From the Help menu, select Download... to open the Downloads page.
3. Click on the ODBC Driver tab. Locate and download the driver for your operating system (e.g., Windows).
4. Crucial Step: Be sure to download the version that matches your installation of Microsoft Office, not your operating system. If you have 64-bit Office, get the 64-bit driver. If you have 32-bit Office, get the 32-bit driver. Most modern installations are 64-bit, but it's essential to check.
5. Run the downloaded installer file and follow the on-screen instructions to complete the installation.

Part 2: Configure the ODBC Connection (DSN Setup)

Next, you need to tell the driver how to find and log into your Snowflake account. You do this by setting up a Data Source Name (DSN).

1. Open the Start menu in Windows and type "ODBC Data Sources". Select the application that matches the driver version you installed (e.g., ODBC Data Sources 64-bit).
2. In the ODBC Data Source Administrator window, click on the User DSN tab. This is where you will add your personal configuration for Snowflake.
3. Click the Add... button. A new window will appear. Scroll through the list of drivers and select SnowflakeDSIIDriver, then click Finish.
4. Now, you'll see the Snowflake Configuration dialog box. This is where you input your connection details:
5. You can click the Test... button to ensure your credentials are correct. If it's successful, click OK to save your DSN.

Part 3: Pull the Data into Excel

With the setup complete, you can now connect to Snowflake from inside Excel.

1. Open a new or existing workbook in Excel.
2. Go to the Data tab on the Ribbon.
3. Click Get Data → From Other Sources → From ODBC.
4. From the dropdown menu under Data Source Name (DSN), select the connection you created in the previous step (e.g., "Snowflake Marketing"). Click OK.
5. If prompted, enter your Snowflake username and password again.
6. The Navigator window will appear, showing you all the schemas, tables, and views available through your connection. Browse and select the table you want to import. You’ll see a preview of the data.
7. Click Load. Excel will pull the data from Snowflake and load it into a new worksheet as a formatted table.

The best part? Your data is now live. Whenever you need an update, simply go to the Data tab and click Refresh All. Excel will reconnect to Snowflake, run the query in the background, and update your table with the latest information.

Tips for a Clean and Efficient Export

Regardless of which method you choose, following a few best practices will save you a lot of time and prevent potential headaches.

1. Be Specific About What You SELECT

It's tempting to type SELECT * FROM my_table, but this is very inefficient. It pulls every single column, many of which you may not need, slowing down the query and cluttering your Excel sheet. Get in the habit of explicitly naming the columns you need. Your queries will run faster and your exports will be much cleaner.

2. Always Filter Your Data First

Never export a full table if you only need a portion of the data. Use the WHERE clause in your SQL to filter down the results on Snowflake’s powerful servers before pulling it into Excel. Think of it like this: let Snowflake do the heavy lifting of sorting through millions of rows, and only transfer the much smaller, relevant subset to your local machine.

3. Be Mindful of Excel’s Limitations

Excel is not a big data tool. A standard sheet is limited to 1,048,576 rows. If your query returns more rows than that, the import will fail or only load a partial dataset. This is another reason why using WHERE clauses and LIMIT is so important. If you need to analyze datasets larger than this, you should consider using a BI platform or performing more advanced aggregations within Snowflake itself.

4. Handle Your Data Types Correctly

Sometimes, data types can be misinterpreted during an export. A numeric ID might be converted to scientific notation, or a date might be read as a string of text. If you're running into issues formatting columns properly in Excel, you can use CAST or :: in your Snowflake SQL to specify the type. For example, casting a numeric ID column to a string ensures Excel treats it as text:

SELECT
  order_id::VARCHAR,
  order_date,
  order_total
FROM
  your_database.your_schema.orders_table,

Final Thoughts

You have several effective ways to move data from Snowflake to Excel. The direct download method is perfect for quick, ad-hoc tasks, while setting up an ODBC connection gives you the power of refreshable, live data for your recurring reports. Whichever path you choose, remember to filter and select your data wisely in Snowflake before exporting to ensure a smooth and efficient process.

While these methods are powerful for getting your data into Excel, the process of manually exporting, cleaning, and visualizing info is often where your most valuable time gets spent. Instead of pulling data from sources like Snowflake just to wrangle it in a spreadsheet, we built Graphed to cut out those steps entirely. We offer one-click integrations with your core data platforms so you can use simple, natural language - not SQL - to instantly build real-time dashboards and get answers, creating a constantly updated view of your business performance without the manual reporting drain.