Combining the impressive cloud storage capabilities of Snowflake with the robust and versatile processing tools of Microsoft Excel is the type of data wizardry that can deliver incredible results. By leveraging these two powerhouses, developers can tap into unparalleled data analysis, visualization, and reporting, transforming raw data into insightful decisions with ease.

In this guide, we will walk you through how to fuse Snowflake and Microsoft Excel to create an unbeatable data integration and analysis to access valuable and actionable insights. Many businesses fail to use data to its full potential, risking falling behind their competitors and missing out on golden opportunities - an oversight that can have drastic consequences.

5 Key Takeaways:

  • Snowflake and Excel's synergy enhances data handling

  • Efficient resource utilization can lower costs

  • Advanced features facilitate deeper insights

  • Streamlined setup enhances accessibility

  • Proactive troubleshooting ensures continuous efficiency

Table of Contents

Understanding Snowflake and Excel

Snowflake is a fully managed SaaS platform that has been designed to work on the public cloud and enables businesses to create a data warehouse - a large-scale repository that stores and manages historical data. 

In terms of data analysis and reporting, Excel has the functionality to meet a business’s requirements in full. Now, let’s get into the nitty-gritty of both of these platforms before we move on to the integration itself


Snowflake also provides features to create data lakes for the storage of raw and unstructured data, in addition to tools relating to data science, real-time data sharing and consumption, data engineering, and data application development.

Snowflake’s popularity has grown exponentially since its launch in 2012, largely down to its out-of-the-box capabilities which allow users to separate storage and compute (the processing of data), scale compute ‘on-the-fly’, share data, clone data, and integrate third-party tools

Snowflake's architecture allows for the separation of storage and computing, enabling scalable and cost-effective data processing. This scalability is crucial when applying AI and ML models, which require substantial computational resources, especially with large volumes of data. As such, the platform is primed for augmented analytics and their slow, but inevitable takeover of data science as a whole. 

Microsoft Excel

Data teams can Excel to organize, interpret, and evaluate data to a very high level, assisted by a range of tools to streamline the data analysis process

Pivot tables help to make sense of complex and messy datasets, turning them into structured, relevant data. Meanwhile, pivot charts visualize the data in a range of graphical formats and conditional formatting further visualizes data simply. 

Additionally, functions allow users to quickly detect any errors within a dataset, perform advanced calculations, search and retrieve information, and more. With regression analysis, it is also possible to highlight current relationships between variables and predict future ones, helping to identify patterns that could be used to optimize areas of the business. 

Although there are many data manipulation tools on the market, such as an aftermarket PDF to Excel converter or data transformations are still much more efficient and secure through Snowflake. Furthermore, by leveraging Excel for data analysis and manipulation, users can more precisely define the data they need to extract from Snowflake, which can lead to more efficient use of Snowflake's compute resources. 

Snowflake's pricing model is consumption-based, so optimizing query execution can reduce cloud costs and allow organizations to run more efficiently. Together, Snowflake and Excel provide everything a business needs to succeed in this modern, data-driven climate.

Setting Up Snowflake with Excel

Using Excel as an analysis tool for Snowflake provides several advantages such as a familiar user interface that reduces any learning curve, live analysis of data, and versatile and dynamic reporting features for detailed, visualized insights.

In this section, we will provide a step-by-step guide on how to set up Snowflake with Excel… let’s get started.

You will need:

  • A Windows or Mac OS.
  • A Snowflake account with access to its web interface.
  • A compatible version of Microsoft Excel
  • An ODBC (Open Database Connectivity) driver (compatible with your version of Excel).
  1. Set up the Snowflake ODBC Driver

Assuming you already have a Snowflake account, sign in and navigate to the ‘Downloads’ section. Then click the Snowflake ODBC driver that is compatible with your OS and system architecture. Once the download is complete, install the driver by following the installation wizard. 

The next step is to open up the Windows or Mac OS search bar and search for ‘ODBC Data Sources’ then click the one that is compatible with your architecture (32bit or 64bit). 

  1. Configure the Snowflake Data Source Name (DSN)

Stage two is to confirm that Snowflake is listed in the ‘Driver’s tab within the ODBC interface. To do this, navigate to the ‘User DSN’ tab and click the ‘Add’ button to add the Snowflake driver to the DSN. Hit ‘Finish’ to start the process. A dialog box will then appear asking you to provide information relating to your Snowflake account. 

Below is a list of the required fields:

  • Data Source - The name that will be assigned to the DSN
  • User - You Snowflake username
  • Password - The password to your Snowflake account
  • Server - The URL that is specific to your Snowflake account. This can be found in the initial welcome email when you created the account. Example: <account_identifier>
  • Role - The user role you want to create. I.E. ACCOUNT ADMIN 

Once these details have been added, click the ‘Test’ button and check that the data source configuration is correct. The Snowflake driver should then be listed under User DSN.

  1. Connect Excel

To ensure Excel is compatible, you should be using at least Excel 2019, but preferably a newer version. 

This step is relatively straightforward. First, open Excel and click the ‘Data’ tab, then select ‘Get Data’ from the dropdown menu. 

Next, click ‘From Other Sources’ and select ‘From ODBC.’ A dialog box will then appear, prompting you to select the data source. Click the Snowflake data source you created in the ODBC interface. 

Enter your Snowflake username and password in the next dialog box, then click ‘Connect’. Your Snowflake data should then be available to access in Excel. 

  1. Using Excel to Query Snowflake Data

Once the data has been made available to Excel via the ODBC driver, you can select any database or schema you wish to begin querying and manipulating its contents. 

Simply load the data into an Excel spreadsheet by using the built-in file navigator and click ‘Load’. Data can also be transformed into your desired format using the ‘Transform Data’ button. 

That’s it, you can then begin working on the data. 

Data Analysis and Reporting with Snowflake Excel Integration

With your Snowflake data now easily accessible in Excel, you now perform advanced data analysis using the program’s many features and functions. This allows data engineers to create dynamic data visualizations and identify deep data insights. 

PivotTables/ PivotCharts

PivotTables or Charts help to make sense of data visually. This tool allows users to summarize, analyze, and inspect data in the form of presentable charts and graphs. Visualizations make it easy to understand complex data sets, and highlight patterns, trends, and connections to help businesses make better decisions using tangible insights. 

CONCAT Function

The CONCAT function replaced the CONCATENATE function in Excel, allowing users to combine the text from multiple ranges and/ or strings, but without IgnoreEmpty or delimiter arguments. This makes it possible to combine multiple cells into a single cell without the need to use delimiters (specified boundaries) between the combined cell values - resulting in a much quicker process. 


VLOOKUP is used to find things with a table or a range by row, such as an individual in an organization based on their user ID. In a Vlookup, the user is essentially defining what they want to look up and where they want to look for it. This can be used to return approximate or exact matches in a certain range.

Tip - This may sound simplistic but many Excel users slip up by not naming column headers sensibly. As well as choosing a relevant naming system, users should also avoid double rows of headers and merged cells as headers. 

Best Practices for Snowflake Excel Integration

Although integrating Excel with Snowflake facilitates data-driven decision-making, it can certainly be challenging to properly include it in your existing processes. Hence, here are best practices to optimize this integration:

  • Optimize query performance: When importing data into Excel, use specific SQL queries instead of fetching entire tables. This reduces the volume of data transferred, improving performance and minimizing resource consumption. Tailor your queries to retrieve only the necessary data, leveraging Snowflake's computing power to perform heavy lifting before data reaches Excel.

  • Leverage data types effectively: Ensure compatibility between Snowflake and Excel data types. Certain data types in Snowflake, like VARIANT, may not have direct equivalents in Excel. It's crucial to convert these into compatible formats through SQL queries during data import, preventing data loss or corruption.

  • Implement data refresh strategies: Establish a method for regularly updating Excel data from Snowflake. This can be achieved through manual refresh setups or by scripting automated refresh processes using VBA or external scheduling tools. Keeping data current is vital for accurate analysis and reporting.

  • Security and compliance: Securely handle credentials and access permissions. Utilizing encrypted connections and adhering to role-based access control within Snowflake can significantly limit data exposure in Excel, based on user roles. Ensuring compliance with data governance and regulatory standards is crucial, especially for sensitive or regulated data.

Troubleshooting Common Issues

Although Snowflake and Excel integration is very straightforward, sometimes issues can occur that require troubleshooting. Watch out for the following and you’ll have a much easier time setting up Excel to analyze Snowflake data:  

  1. Snowflake input the incorrect username and password into Excel, preventing the ODBC from establishing a connection. Always double-check your authentication details to avoid any hiccups.
  2. The DSN may be configured incorrectly meaning Excel cannot connect. Ensure these settings are correct and test the connection in the ODBC interface. 
  3. You may be using an older or incompatible version of the Snowflake ODBC driver, meaning it may not connect to your version of Excel. Always ensure your ODBC driver matches your Excel version (32bit or 64bit)
  4. Older versions of Excel may not be compatible with Snowflake. You should be using Excel 2019 or later. 
  5. Sometimes the amount of data you are importing from Snowflake into Excel may be too large, resulting in lower performance. Try to break down data sets into smaller pieces to avoid slow speeds and Excel errors.

Conclusion and How Can Help

For familiarity, power performance, and to avoid purchasing new third-party software, Excel is the ideal option to analyze and report on your Snowflake data. Thanks to its advanced functionality and built-in tools, data can be easily manipulated and presented in dynamic visualizations to highlight key insights and trends. Integration is also extremely straightforward, as seen in our concise step-by-step guide. can significantly enhance the integration of Snowflake and Excel by providing a powerful data integration platform that simplifies the extraction, transformation, and loading (ETL) processes. In the context of leveraging both Snowflake's cloud storage capabilities and Excel's versatile processing tools, can automate data flows between these systems, ensuring that data is seamlessly synced and updated. This automation not only reduces the manual effort required to maintain data accuracy but also speeds up data processing, allowing businesses to gain insights faster and more reliably.

Moreover, supports advanced data transformation capabilities that can preprocess data before it's analyzed in Excel, thereby optimizing the data's structure and quality for better analysis and visualization. For businesses looking to utilize data to its fullest potential and avoid the risk of falling behind competitors, offers a robust solution that strengthens the bridge between Snowflake's data warehousing and Excel's analytical power, ultimately leading to more informed decision-making and efficient resource utilization. Try a free 14-day trial of our product today!


How does Snowflake integrate with Excel?

Snowflake seamlessly connects with Excel through a quick and simple integration process using Snowflake’s ODBC drivers. 

By following the simple steps within the ODBC interface and entering your Snowflake username and password, a connection can be made with Excel that makes it possible to import Snowflake data in just a few clicks. 

What are the benefits of using Snowflake with Excel for data analysis?

The benefits of using Snowflake with Excel are numerous. However, the key benefits include; a familiar interface that makes it easy to start transforming data right away; an easy integration process; a wide range of functions and tools for efficient data manipulation; and the ability to effortlessly create dynamic visualizations.

What are the best practices for managing large datasets in Excel from Snowflake?

When managing large data sets, you should try to break them down as much as possible before importing them into Excel to avoid any performance issues. According to Snowflake documentation, you should aim for file sizes between 100 - 250 MB after being compressed.