How to Connect Snowflake to Power BI

Connecting your Snowflake data warehouse to Power BI combines one of the most powerful and scalable cloud data platforms with a top-tier visualization tool. This guide will walk you through the entire process step-by-step, showing you exactly how to connect to your data and start building reports.

Why Connect Snowflake to Power BI?

On their own, both Snowflake and Power BI are excellent tools. But when you use them together, you unlock some significant advantages for your data analysis and reporting. The combination allows you to analyze massive datasets and turn them into insightful, interactive dashboards that your entire team can use.

Here’s why it’s such a powerful pairing:

• Scalability and Performance: Snowflake is built to handle huge volumes of data effortlessly. Its unique architecture separates computing power from storage, so your queries run incredibly fast without getting bogged down, even with billions of rows. When you connect it to Power BI, your dashboards and reports load quickly and remain responsive, regardless of the underlying data size.
• A Single Source of Truth: Instead of pulling data from dozens of different spreadsheets or databases, you can centralize all of your business data in Snowflake. This creates a single, reliable source of truth. When your entire team builds Power BI reports from this central hub, everyone works with the same consistent and up-to-date information, eliminating confusion and errors from disparate data sources.
• Real-Time Data with DirectQuery: Power BI’s DirectQuery mode lets you connect live to your Snowflake data. Every time a user interacts with a dashboard, Power BI sends a query directly to Snowflake and retrieves the latest information. This is perfect for scenarios where you need up-to-the-minute data, like monitoring sales performance or tracking website traffic.
• Leverage Power BI’s Strengths: Power BI excels at creating interactive reports and dashboards. Once connected to Snowflake, you can use all of Power BI’s visualization capabilities, powerful DAX functions for custom calculations, and seamless sharing and collaboration features through the Power BI service.

Prerequisites for Connecting

Before you begin, make sure you have the following information and software ready. Getting these details organized first will make the connection process smooth and quick.

• Power BI Desktop: You'll need Power BI Desktop installed on your computer. You can download the latest version for free directly from the Microsoft Store.
• Snowflake Account Credentials: You need a valid Snowflake user account with a username and password.
• Snowflake Account URL: This is the unique URL for your company's Snowflake instance. It usually looks something like this: yourcompany.snowflakecomputing.com
• Snowflake Warehouse, Database, and Schema: Know the specific names of the Warehouse (the computing engine), Database, and Schema (data organization layer) you intend to pull data from.
• Correct Permissions: Ensure your Snowflake user role has the necessary permissions (USAGE on the warehouse, database, schema, and SELECT on the tables) to access the data you want to visualize in Power BI. Check with your Snowflake administrator if you're unsure.

Step-by-Step Guide: Connecting Power BI to Snowflake

With your prerequisites in hand, you're ready to create the connection. Follow these steps carefully.

Step 1: Open Power BI and Select 'Get Data'

Launch Power BI Desktop. On the Home ribbon at the top of the window, click on the Get Data button. This will open a window with a long list of available data sources.

Step 2: Find the Snowflake Connector

In the Get Data window, you can either scroll down to find Snowflake or simply use the search bar at the top left. Type "Snowflake" and select the Snowflake connector from the list, then click Connect.

Step 3: Enter Your Snowflake Server and Warehouse Details

A new dialog box will appear asking for your Snowflake instance details.

• In the Server field, enter your Snowflake account URL. For example: my-account.snowflakecomputing.com.
• In the Warehouse field, enter the name of the Snowflake warehouse you want to use. While this field is optional, it’s highly recommended you specify a warehouse to ensure your queries are routed to the correct compute resources for optimal performance.

After entering these details, you'll move on to a very important choice: the Data Connectivity mode.

Step 4: Choose Your Data Connectivity Mode (Import vs. DirectQuery)

Power BI offers two distinct ways to connect to Snowflake data, and your choice will significantly impact your report's performance and data freshness. It's crucial to understand the difference.

Import Mode

In Import mode, Power BI pulls a full copy of your selected data from Snowflake and loads it into its internal high-performance engine (the VertiPaq engine). All interactions with your visuals in the report are then handled by this cached data.

• Pros: Excellent performance for dashboards and visuals because the data is stored locally within the Power BI file. Supports the full range of Power BI transformations and DAX functions.
• Cons: The data becomes static. To see new information, you must schedule a refresh, which re-imports the data from Snowflake. This mode can also consume significant memory and disk space if you import very large datasets.
• Best for: Smaller to medium datasets (under 1 GB) where performance is a top priority and real-time data is not a necessity.

DirectQuery Mode

In DirectQuery mode, Power BI does not store a copy of the data. Instead, it establishes a live connection to Snowflake. When you interact with a filter or visual in your report, Power BI generates and sends a query directly to Snowflake to fetch the necessary data in real time.

• Pros: The data in your report is always up-to-date, reflecting the current state of your Snowflake warehouse. You can work with massive datasets that would be too large to import.
• Cons: Performance is entirely dependent on the speed of your Snowflake warehouse and network latency. There are also some limitations on the types of transformations and DAX functions you can use, as they must be compatible with Snowflake's SQL dialect.
• Best for: Very large datasets or when real-time reporting is absolutely critical.

Step 5: Authenticate Your Connection

After selecting your connectivity mode and clicking OK, Power BI will ask you to authenticate. You have two main options:

1. Username/Password: This is the most straightforward method. Simply enter the username and password for a Snowflake user account that has the required data access permissions.
2. Microsoft Account (Azure AD): If your organization has configured Single Sign-On (SSO) between Snowflake and Azure Active Directory, you can select this option. Click "Sign in" and use your standard organizational login. This is often the preferred and more secure method in an enterprise environment.

Choose your method, enter your credentials, and click Connect.

Step 6: Navigate and Select Your Data

Once you’ve successfully authenticated, the Power BI Navigator window will appear. Here, you'll see a structured view of your Snowflake environment.

• Expand the database and schema you want to access.
• A list of all the tables and views within that schema will be displayed.
• Check the boxes next to the tables or views you want to include in your report. As you select each one, a preview of its data will show up on the right side of the screen.

At the bottom of the Navigator, you'll have two options:

• Load: Clicking this will load the selected tables directly into your Power BI data model. This is quick, but it's often wise to clean your data first.
• Transform Data: This is the recommended choice in most cases. It opens the Power Query Editor, a powerful tool that lets you clean, reshape, filter, and prepare your data before loading it into your final model.

That's it! Your data from Snowflake is now connected to Power BI, ready for you to start building powerful visualizations and reports.

Best Practices for Snowflake and Power BI

To get the most out of this connection, follow these simple tips for better performance and manageability.

• Filter Your Data Early: Whether you're using Import or DirectQuery, don't bring in more data than you need. Use Power Query to filter out unnecessary rows and columns. For large tables in DirectQuery, this is critical for keeping your reports snappy.
• Create Views in Snowflake: Instead of doing complex joins and logic inside Power BI, consider creating dedicated reporting views directly in Snowflake. Pre-aggregating data or joining large tables in a view offloads the heavy lifting to Snowflake's powerful engine, which simplifies your Power BI model and improves performance.
• Choose the Right Warehouse Size: If you're using DirectQuery and find your reports are slow, the bottleneck might be your Snowflake warehouse. You can easily scale up your warehouse size (e.g., from an X-Small to a Small) in Snowflake to provide more computing power for queries coming from Power BI.
• Leverage Snowflake’s Role-Based Access: Power BI respects the security policies you set in Snowflake. When a user views a Power BI report connected via DirectQuery (and with SSO enabled), Snowflake will only show them the data their specific role is allowed to see. You can manage data security in one central place - Snowflake - instead of trying to replicate it in multiple Power BI reports.

Final Thoughts

By connecting Snowflake to Power BI, you create a robust analytics pipeline that handles massive amounts of data with exceptional performance. You can leverage Snowflake for scalable data storage and processing while using Power BI for what it does best: intuitive visualization and user-friendly reporting.

While direct connections like this are powerful, they often represent just the first step. Building meaningful dashboards and getting timely answers can still pose a steep learning curve for many teams. We built an AI data analyst to solve this very problem. Graphed connects to your data sources like Snowflake, Google Analytics, and Salesforce, and lets you create dashboards and get answers using simple, natural language. It's like having a data analyst on your team who helps you find insights in seconds, not hours.