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How to Connect Excel to Snowflake: A Step-by-Step Guide

  • Writer: Spreadsheet Hacker
    Spreadsheet Hacker
  • Oct 7, 2024
  • 7 min read

Updated: Feb 25

With the power of Snowflake’s cloud data platform and Excel’s familiar spreadsheet interface, you can quickly analyze data at scale. This guide will walk you through connecting Excel to Snowflake, enabling you to run queries and analyze Snowflake data directly in Excel.

How To Connect Snowflake & Excel

Considerations: Benefits and Limitations

When connecting Excel to Snowflake, it's essential to weigh the benefits and limitations of this approach to determine if it fits your specific use case.

Benefits

  1. Familiar Interface for Data Analysis: Leveraging Excel allows users to interact with data in a familiar environment, reducing the learning curve for non-technical users who may not be proficient in SQL or Snowflake’s UI.

  2. Ad-Hoc Data Exploration: Once connected, Excel makes it easy to perform ad-hoc analysis on Snowflake data, from quick aggregations to more complex functions and pivot tables.

  3. Live Data Access: Since data is pulled directly from Snowflake, users can access up-to-date information without having to manually export and import files. Refreshing the data is as simple as clicking "Refresh," ensuring that analysis remains relevant.

  4. Data Blending and Transformation: Power Query in Excel allows you to blend data from multiple sources, transform data before analysis, and even automate repetitive data preparation tasks, all using Snowflake as a primary source.

  5. Scalability of Snowflake: Since Snowflake can handle massive datasets, you can perform operations on large tables without being limited by the size constraints of Excel files. This makes Snowflake a robust back-end for storing and processing data.

Limitations

  1. Performance with Large Data Sets: While Snowflake is built to handle large datasets, importing massive amounts of data into Excel may lead to performance issues. Excel’s memory limitations can result in slow performance, lag, or even crashes if you attempt to import more data than Excel can handle. This can especially become a problem if you're trying to create pivot tables on Snowflake data at scale.

  2. Read-Only Data Access: The connection from Excel to Snowflake is generally read-only. If you need to write data back to Snowflake or perform data transformations directly on the Snowflake side, this workflow may not be sufficient, requiring additional tools or processes.

  3. Limited Query Complexity: Running complex SQL queries directly in Excel can be cumbersome, especially if you're working with multiple joins or more advanced Snowflake features. For complex data retrieval, using Snowflake's native interface or a dedicated BI tool might be more effective.

  4. Dependence on ODBC Driver and Connectivity: The ODBC driver serves as the bridge between Excel and Snowflake. If the driver is not configured properly or becomes outdated, it can lead to connection issues. Moreover, a stable internet connection is required for live data access; interruptions can affect data retrieval.

  5. Manual Data Refreshing: While it's easy to refresh data within Excel, it remains a manual step unless you automate the process. For scenarios requiring real-time data or continuous updating, other ETL or BI tools with scheduled refresh capabilities may be more appropriate.

  6. Security and Access Management: Connecting Excel to Snowflake via ODBC means managing security credentials locally. It’s crucial to secure and manage these credentials properly, especially when sharing Excel files across different users or teams. Additionally, access permissions should be carefully managed within Snowflake to ensure data security.


Don't let these shortcomings hold you back. We'll explore alternatives later in the article!


Connecting Excel & Snowflake Step-by-Step


Step 1: Prerequisites

Before connecting Excel to Snowflake, make sure you have:

  1. An active Snowflake account with credentials (account name, username, password).

  2. Microsoft Excel (2016 or later for best compatibility) installed on your computer.

  3. ODBC Driver for Snowflake installed. Download the appropriate driver for your operating system here.

Step 2: Installing the Snowflake ODBC Driver

  1. Download the ODBC driver from the Snowflake documentation link above.

  2. Follow the installation instructions based on your operating system:

    • On Windows, run the installer .msi file and follow the prompts.

    • On Mac, download the .dmg file, mount it, and copy the driver files to the appropriate directory.

  3. Once installed, the ODBC driver will be used to bridge Excel and Snowflake.

Step 3: Configuring the ODBC Data Source

  1. Open the ODBC Data Source Administrator:

    • On Windows, search for “ODBC Data Source” in the Start Menu and select ODBC Data Source (64-bit).

    • On Mac, use the iODBC Data Source Administrator.

  2. Add a new Data Source:

    • Click Add to create a new DSN (Data Source Name).

    • Select SnowflakeDSIIDriver from the list and click Finish.

  3. Configure the DSN Settings:

    • Data Source Name (DSN): Give your DSN a name that is easy to remember.

    • Server: Enter your Snowflake account's URL (e.g., youraccount.snowflakecomputing.com).

    • Database: (Optional) Specify the default database you want to connect to.

    • Schema: (Optional) Specify the default schema.

    • Warehouse: (Optional) Set the warehouse you intend to use for queries.

    • Role: (Optional) Define the role to be used when accessing Snowflake.

  4. Click OK to save your DSN settings.

Step 4: Connecting Excel to Snowflake Using ODBC

  1. Open Microsoft Excel and navigate to the Data tab.

  2. Click on Get Data (or From Other Sources in older versions) and select From ODBC.

  3. In the ODBC Data Source dialog box:

    • Select the DSN name you created in Step 3.

    • Click OK to proceed.

  4. A Snowflake sign-in dialog box will appear:

    • Enter your Snowflake username and password.

    • Click Connect to authenticate and establish the connection.

Step 5: Selecting and Importing Data from Snowflake

  1. Once connected, you’ll see a Navigator pane in Excel that displays the databases, schemas, and tables available in your Snowflake account.

  2. Browse the data structure to locate the table or view you wish to import.

  3. Select the table or view, and you'll see a preview of the data.

  4. Choose Load to import the data directly into Excel or Transform Data to open the Power Query Editor for further data shaping and filtering.

  5. If you select Load, Excel will import the data into a new sheet, making it available for analysis.

Step 6: Running Queries on Snowflake from Excel

If you need more control over the data you retrieve, you can run SQL queries against your Snowflake database from Excel:

  1. In the Navigator pane, instead of selecting a table, choose Advanced Options.

  2. Enter your SQL query in the box provided.

  3. Click OK to execute the query and load the results into Excel.

Step 7: Refreshing Data

After importing data, you can refresh it periodically to pull the latest information from Snowflake:

  1. Go to the Data tab in Excel.

  2. Click on Refresh All to update all connections, or right-click the imported data and select Refresh to update a single connection.


Alternatives to Connecting Excel to Snowflake

While connecting Excel directly to Snowflake is a viable option for many, it’s worth considering other alternatives that provide enhanced capabilities, better scalability, and more efficient data workflows. Here's a comparison of several alternatives and why Gigasheet stands out as the best choice.

1. Business Intelligence Tools (e.g., Tableau, Power BI)

BI tools like Tableau and Power BI offer powerful data visualization capabilities and seamless integration with Snowflake. They are great for creating dashboards and sharing insights across teams. However, these tools often require a steeper learning curve, additional licensing costs, and complex setups. Also, users who want to conduct ad-hoc data exploration or require spreadsheet-like functionality may find these tools less intuitive than Excel.

2. Python and SQL Clients

For technical users, Python libraries (like Pandas) and SQL clients (like DBeaver or SQL Workbench) provide flexible options for querying Snowflake, analyzing data, and performing complex transformations. Even the Snowflake UI provides a limited table view. These tools are powerful but come with a steep learning curve and require coding skills, making them less accessible to business users who prefer the simplicity of a spreadsheet interface.

3. Cloud-Based Spreadsheet Solutions (e.g., Google Sheets, Airtable)

Some teams use cloud-based spreadsheet solutions like Airtable or Google Sheets to connect to Snowflake. While these tools provide a familiar environment and ease of collaboration, they face significant performance issues when handling large datasets, often resulting in slow processing or an inability to manage billions of rows. They also lack advanced data preparation features necessary for deep analysis.

4. Gigasheet

Gigasheet for Snowflake brings together the best of all worlds. It combines the user-friendly, no-code spreadsheet experience with the ability to work seamlessly with Snowflake data at massive scale—handling billions of rows without performance issues. Here’s why Gigasheet is the optimal choice:

  • Massive Data Handling: Unlike Excel or Google Sheets, which struggle with large datasets, Gigasheet is built to work with billions of rows and hundreds of columns without compromising on speed or usability.

  • No Coding Required: With Gigasheet, you don’t need SQL, Python, or other technical skills to explore and manipulate data. The spreadsheet-like interface is intuitive for business users and powerful enough for analysts.

  • Direct Integration & Automation: Gigasheet’s direct integration with Snowflake allows you to easily import, analyze, and export data. Additionally, you can automate data refreshes and workflows without worrying about manual updates or maintaining complex ODBC connections.

  • Advanced Data Exploration & Cleaning: Gigasheet offers powerful data manipulation features like filtering, sorting, pivoting, and data cleaning—all within the browser. This empowers users to clean and transform data before running deeper analysis or exporting to other tools.

  • Security and Access Control: With role-based access controls and secure connections to Snowflake, Gigasheet ensures your data remains protected and accessible only to authorized users.


Conclusion: Choose the Best Approach for Your Needs

Connecting Excel to Snowflake opens up a world of possibilities for business users and analysts alike, making it easy to explore and analyze large datasets using a familiar interface. While the process is straightforward, it does come with its own set of limitations—especially when working with large volumes of data or requiring real-time updates.

For many users, alternatives like BI tools, SQL clients, or cloud spreadsheets may be suitable but come with trade-offs, such as additional cost, steep learning curves, or performance issues. That’s where Gigasheet shines. By offering the scalability of Snowflake with the usability of a spreadsheet interface, Gigasheet enables you to manipulate and explore massive datasets efficiently and without the need for coding skills.

If you're looking for a solution that combines the ease of Excel with the power of Snowflake at scale, Gigasheet is the best choice to enhance your data workflow and boost your productivity.

So, whether you're a business analyst diving into data exploration, a technical user running advanced transformations, or simply someone tired of Excel’s data limits, Gigasheet can bridge the gap between data warehouses and everyday analysis—without compromise.


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