How to Connect Snowflake to Power BI: Complete Integration Guide for 2026

Snowflake has become one of the most widely adopted cloud data platforms in the enterprise, running analytical workloads for thousands of organizations across financial services, healthcare, retail, technology, and government. Power BI is the dominant business intelligence platform in the Microsoft ecosystem. Connecting the two is a high-value integration that unlocks interactive analytics, executive dashboards, embedded reporting, and AI-powered insights on top of Snowflake's scalable compute and storage architecture. Our <a href="/services/data-analytics">data analytics consulting services</a> have designed and deployed Snowflake-to-Power BI integrations for organizations ranging from mid-market companies with 50 GB datasets to Fortune 500 enterprises managing petabyte-scale Snowflake environments.

<h2>Why Connect Snowflake to Power BI</h2>

Snowflake excels at scalable storage, elastic compute, near-zero-maintenance operations, and seamless data sharing. Power BI excels at interactive visualization, natural language Q&A, mobile dashboards, row-level security, and enterprise distribution through the Power BI Service. Combining the two creates an analytics architecture where Snowflake handles the heavy lifting of data storage, transformation, and warehousing while Power BI delivers the last mile of insight consumption.

<strong>Key reasons organizations connect Snowflake to Power BI:</strong>

<ul> <li><strong>Interactive dashboards on warehouse-scale data</strong>: Snowflake tables with billions of rows become explorable through Power BI visuals, slicers, and drill-throughs without requiring end users to write SQL.</li> <li><strong>Unified semantic layer</strong>: Power BI datasets (semantic models) add business logic, calculated measures, KPIs, and hierarchies on top of raw Snowflake tables, creating a governed single source of truth.</li> <li><strong>Enterprise distribution</strong>: Power BI Service provides workspaces, apps, subscriptions, embedding, and mobile access—capabilities that Snowflake's native Snowsight dashboards do not match for enterprise BI distribution.</li> <li><strong>Row-level security</strong>: Power BI RLS restricts data visibility per user role, enabling a single report to serve multiple business units or customers with appropriate data isolation.</li> <li><strong>Microsoft 365 integration</strong>: Power BI reports embed natively in Teams, SharePoint, and PowerPoint, meeting users where they already work.</li> <li><strong>AI and Copilot</strong>: Power BI Copilot and smart narratives generate natural language insights from Snowflake data, accelerating time-to-insight for business users.</li> </ul>

Our <a href="/services/power-bi-architecture">Power BI architecture services</a> help organizations design the optimal connection pattern between Snowflake and Power BI based on data volume, freshness requirements, and licensing constraints.

<h2>The Snowflake Connector in Power BI Desktop</h2>

Power BI Desktop includes a certified Snowflake connector that supports both Import and DirectQuery modes. This connector uses the Snowflake ODBC driver under the hood but abstracts configuration into a simple dialog.

<strong>Step-by-step connection setup:</strong>

<ol> <li>Open Power BI Desktop and click <strong>Get Data</strong> from the Home ribbon.</li> <li>Search for <strong>Snowflake</strong> in the connector list and select it.</li> <li>Enter your Snowflake <strong>Server</strong> URL in the format `account_identifier.snowflakecomputing.com` (for example, `xy12345.us-east-1.snowflakecomputing.com`). Do not include `https://` or a trailing path.</li> <li>Enter the <strong>Warehouse</strong> name. This is the Snowflake virtual warehouse that will execute queries. Choose a warehouse sized appropriately for your Power BI workload (more on sizing below).</li> <li>Expand <strong>Advanced options</strong> to optionally specify a Database, Schema, Role, and a native SQL statement. Specifying the database and schema narrows the Navigator tree and improves load time.</li> <li>Choose <strong>Import</strong> or <strong>DirectQuery</strong> as the data connectivity mode.</li> <li>Click <strong>OK</strong> and authenticate using your chosen method (Snowflake credentials, Azure AD SSO, or key pair).</li> <li>Use the Navigator to select tables and views, or click <strong>Transform Data</strong> to open Power Query Editor for pre-load transformations.</li> </ol>

<h2>Import vs. DirectQuery Mode for Snowflake</h2>

The choice between Import and DirectQuery fundamentally affects performance, cost, data freshness, and DAX capability. Understanding the trade-offs is critical for a successful Snowflake-to-Power BI deployment.

<h3>Import Mode</h3>

Import mode extracts data from Snowflake and loads it into Power BI's in-memory VertiPaq engine. Once imported, all dashboard interactions query the local compressed dataset—Snowflake is not involved until the next scheduled refresh.

<strong>Advantages of Import mode:</strong> <ul> <li>Sub-second query performance regardless of data volume (VertiPaq is optimized for analytical queries)</li> <li>Full DAX capability including calculated columns, time intelligence, semi-additive measures, and complex iterators</li> <li>No Snowflake compute cost during dashboard interactions (warehouse only runs during refresh)</li> <li>Works offline in Power BI Desktop after initial load</li> <li>Supports all Power BI visual types and features without restriction</li> </ul>

<strong>Limitations:</strong> <ul> <li>Dataset size limited to 1 GB (Pro), 10 GB (Premium Per User), or 400 GB (Premium/Fabric capacity)</li> <li>Data freshness depends on refresh schedule (up to 8 refreshes/day on Pro, 48 on Premium)</li> <li>Large datasets require longer refresh times, consuming Snowflake credits during extraction</li> </ul>

<h3>DirectQuery Mode</h3>

DirectQuery sends live SQL queries to Snowflake for every visual interaction. No data is stored in Power BI—every slicer change, filter, and drill-through generates a query against Snowflake.

<strong>Advantages of DirectQuery:</strong> <ul> <li>Always-current data (no refresh lag)</li> <li>No dataset size limits (Power BI queries Snowflake on demand)</li> <li>Data never leaves Snowflake (important for compliance scenarios)</li> </ul>

<strong>Limitations:</strong> <ul> <li>Dashboard performance depends entirely on Snowflake query execution time and warehouse size</li> <li>Each visual fires a separate SQL query—a page with 20 visuals generates 20 concurrent queries per user interaction</li> <li>Restricted DAX functionality (no calculated columns, limited iterator functions)</li> <li>Snowflake warehouse must remain running during dashboard usage, consuming credits continuously</li> <li>Complex DAX measures may generate inefficient SQL, leading to slow visuals or timeouts</li> </ul>

<strong>Recommendation:</strong> Import mode is the correct default for the majority of Snowflake-to-Power BI integrations. Use DirectQuery only when data freshness must be sub-minute, dataset exceeds Premium capacity limits, or compliance mandates that data cannot leave Snowflake. For many scenarios, a <strong>composite model</strong> (Import for dimension tables, DirectQuery for large fact tables) provides the best balance. Our <a href="/services/enterprise-deployment">enterprise deployment services</a> evaluate these trade-offs during architecture design.

<h2>Authentication Options</h2>

The Snowflake connector in Power BI supports three authentication methods, each suited to different security postures and organizational requirements.

<h3>1. Snowflake Username and Password</h3>

The simplest method. Enter a Snowflake username and password directly in the Power BI connection dialog. Suitable for development and proof-of-concept work, but not recommended for production because credentials must be stored in the Power BI Service dataset settings and managed manually. If using this method for scheduled refresh, create a dedicated service account with the minimum required role and monitor it with Snowflake's ACCESS_HISTORY view.

<h3>2. Azure Active Directory Single Sign-On (Azure AD SSO)</h3>

For organizations using Microsoft Entra ID (Azure AD), the Snowflake connector supports SSO through the Azure AD OAuth2 flow. This is the recommended method for organizations in the Microsoft ecosystem.

<strong>Setup requirements:</strong> <ul> <li>Configure Snowflake as an Azure AD enterprise application (SAML or OAuth)</li> <li>Map Azure AD users to Snowflake users (matching by email or UPN)</li> <li>Enable external OAuth in Snowflake: `CREATE SECURITY INTEGRATION` with type = EXTERNAL_OAUTH and provider = AZURE</li> <li>In Power BI Desktop, select "Microsoft account" authentication when connecting to Snowflake</li> </ul>

<strong>Benefits:</strong> SSO passes the signed-in user's identity through to Snowflake, enabling Snowflake-side row-level security (data masking policies, row access policies) to apply per user. This bridges identity between Power BI and Snowflake seamlessly.

<h3>3. Key Pair Authentication</h3>

Key pair authentication uses RSA public/private key pairs instead of passwords. This is the most secure option for service accounts used in scheduled refresh and automated pipelines.

<strong>Setup:</strong> <ul> <li>Generate an RSA key pair using OpenSSL</li> <li>Assign the public key to the Snowflake user: `ALTER USER svc_powerbi SET RSA_PUBLIC_KEY = '...'`</li> <li>Store the private key securely (Azure Key Vault recommended)</li> <li>Configure the Power BI gateway or connection to use key pair auth</li> </ul>

Key pair authentication eliminates password rotation challenges and is preferred for production scheduled refresh scenarios.

<h2>Query Folding and Performance Optimization</h2>

Query folding is the mechanism by which Power Query transformations are translated into native SQL and pushed down to Snowflake for execution. When query folding works correctly, Snowflake handles filtering, aggregation, joins, and column selection before data is transferred to Power BI—dramatically reducing data transfer volume and import time.

<strong>Transformations that fold to Snowflake SQL:</strong> <ul> <li>Column selection (Remove Columns)</li> <li>Row filtering (Filter Rows with simple conditions)</li> <li>Sorting (Sort by column)</li> <li>Grouping and aggregation (Group By)</li> <li>Column renaming</li> <li>Data type changes</li> <li>Table joins (Merge Queries when both sources are Snowflake)</li> </ul>

<strong>Transformations that break query folding:</strong> <ul> <li>Adding custom columns with complex M expressions</li> <li>Pivot / Unpivot operations (in some cases)</li> <li>Merging with non-Snowflake sources</li> <li>Using `Table.Buffer` or `Table.Distinct` with complex logic</li> </ul>

<strong>Verification:</strong> In Power Query Editor, right-click any step and check whether "View Native Query" is available. If it is, that step folds. If greyed out, folding has broken at or before that step.

<strong>Performance best practices:</strong> <ul> <li>Place all folding-compatible steps before any non-folding steps</li> <li>Filter early and aggressively—reduce row count at the Snowflake level before any local processing</li> <li>Use Snowflake views to pre-join and pre-aggregate complex logic, then import the view rather than raw tables</li> <li>Avoid `SELECT *`—explicitly select only the columns your model requires</li> <li>Set the Snowflake warehouse `STATEMENT_TIMEOUT_IN_SECONDS` to prevent runaway queries from consuming excessive credits</li> </ul>

<h2>Warehouse Sizing for Power BI Workloads</h2>

Snowflake virtual warehouse sizing directly impacts Power BI refresh speed and DirectQuery response time. Under-sized warehouses cause query timeouts and slow refreshes; over-sized warehouses waste credits.

<strong>Sizing guidelines by scenario:</strong>

<table> <tr><th>Scenario</th><th>Recommended Size</th><th>Rationale</th></tr> <tr><td>Development / proof of concept</td><td>X-Small (1 credit/hr)</td><td>Small data volumes, single developer</td></tr> <tr><td>Import refresh, dataset under 10 GB</td><td>Small (2 credits/hr)</td><td>Balanced cost/performance for moderate data</td></tr> <tr><td>Import refresh, dataset 10-100 GB</td><td>Medium (4 credits/hr)</td><td>Parallelism needed for larger table scans</td></tr> <tr><td>Import refresh, dataset 100+ GB</td><td>Large to X-Large (8-16 credits/hr)</td><td>Heavy extraction, consider partitioned refresh</td></tr> <tr><td>DirectQuery, low concurrency (1-10 users)</td><td>Small to Medium</td><td>Each visual generates a query; warehouse must handle concurrent load</td></tr> <tr><td>DirectQuery, high concurrency (50+ users)</td><td>Medium to Large + multi-cluster auto-scaling</td><td>Auto-scaling prevents queuing under concurrent load</td></tr> </table>

<strong>Critical configuration:</strong> Set `AUTO_SUSPEND = 60` (seconds) on the Power BI warehouse so it shuts down one minute after the last query. Set `AUTO_RESUME = TRUE` so it starts automatically when Power BI initiates a refresh or DirectQuery. This prevents idle credit burn. For scheduled Import refreshes, the warehouse runs only during the refresh window—typically 2-15 minutes depending on data volume.

<h2>Data Gateway Considerations</h2>

The gateway requirement depends on your Power BI licensing and Snowflake connectivity scenario:

<ul> <li><strong>Power BI Service (cloud) to Snowflake (cloud)</strong>: No gateway required for Import or DirectQuery when connecting to a Snowflake account accessible over the public internet. The Power BI Service connects directly to Snowflake.</li> <li><strong>Snowflake with Private Link / private connectivity</strong>: If your Snowflake account restricts access to private endpoints (AWS PrivateLink, Azure Private Link, Google Cloud Private Service Connect), you need an On-premises Data Gateway installed on a VM within the same private network. The gateway acts as a bridge between the Power BI Service and the private Snowflake endpoint.</li> <li><strong>VNet Data Gateway (Fabric)</strong>: Microsoft Fabric offers a VNet Data Gateway that runs as a managed service within your Azure VNet, eliminating the need to manage gateway VMs. This is the recommended approach for Fabric customers with private Snowflake connectivity.</li> </ul>

<strong>Gateway cluster best practices:</strong> Install at least two gateway nodes in a cluster for high availability. Schedule Power BI refreshes during off-peak hours to minimize gateway load. Monitor gateway performance through the Power BI admin portal and set up alerts for refresh failures.

<h2>Snowflake Views and Materialized Views for Power BI</h2>

Creating dedicated Snowflake views for Power BI consumption is a best practice that separates the raw data layer from the analytics layer.

<h3>Standard Views</h3>

Create views that pre-join dimension and fact tables, apply business logic, rename columns to business-friendly names, and filter out irrelevant records. Power BI then imports or queries these views instead of raw tables.

<strong>Benefits:</strong> <ul> <li>Simplifies the Power BI data model (fewer tables, fewer relationships to configure)</li> <li>Pushes complex SQL joins and transformations to Snowflake where they execute efficiently</li> <li>Provides a stable interface—underlying table structures can change without breaking the Power BI model as long as the view definition is maintained</li> <li>Enables Snowflake-side data governance (column masking policies applied at the view level)</li> </ul>

<h3>Materialized Views</h3>

Materialized views pre-compute and store query results, refreshing automatically when underlying data changes. They are particularly valuable for DirectQuery scenarios where Power BI repeatedly queries the same aggregated data.

<strong>When to use materialized views:</strong> <ul> <li>DirectQuery reports that query large fact tables with aggregation (SUM, COUNT, AVG)</li> <li>Dashboards where the same complex join pattern is queried by multiple visuals</li> <li>Scenarios where query latency must be minimized and slightly stale data is acceptable</li> </ul>

<strong>Cost consideration:</strong> Materialized views consume Snowflake storage and require background maintenance compute. The cost is justified when the alternative is a large warehouse running expensive queries for every DirectQuery interaction. Monitor materialized view maintenance cost using the `MATERIALIZED_VIEW_REFRESH_HISTORY` function.

<h2>Cost Management: Snowflake Credit Consumption</h2>

Snowflake's consumption-based pricing means that every query Power BI sends to Snowflake costs money. Unmanaged integrations can lead to bill shock, especially with DirectQuery or frequent Import refreshes against large datasets.

<strong>Cost optimization strategies:</strong>

<ul> <li><strong>Use Import mode over DirectQuery</strong>: Import mode runs queries only during scheduled refresh (minutes per day). DirectQuery runs queries for every user interaction (potentially hours per day). For a 20-user team using DirectQuery dashboards for 8 hours daily on a Medium warehouse, the cost is 4 credits/hr x 8 hrs = 32 credits/day = ~960 credits/month. The same workload in Import mode with 4 daily refreshes averaging 5 minutes each costs 4 credits/hr x (20 min / 60) = ~1.3 credits/day = ~40 credits/month. That is a 24x cost difference.</li> <li><strong>Right-size the warehouse</strong>: Do not default to Large. Start with X-Small and scale up only if refresh times exceed your SLA. Snowflake's query profiler (`QUERY_HISTORY` view) shows execution times to guide sizing.</li> <li><strong>Set AUTO_SUSPEND aggressively</strong>: 60 seconds is appropriate for Power BI workloads. The default 600 seconds wastes 9 minutes of credits after each refresh.</li> <li><strong>Use resource monitors</strong>: Set a Snowflake resource monitor on the Power BI warehouse with daily and monthly credit limits. Configure alerts at 75% and 100% thresholds. This prevents runaway costs from unexpected usage patterns.</li> <li><strong>Consolidate datasets</strong>: Multiple Power BI datasets querying the same Snowflake tables multiply credit consumption. Consolidate into shared datasets or use Fabric Dataflows Gen2 to extract once and serve many reports.</li> <li><strong>Schedule refreshes during off-peak</strong>: If your Snowflake contract includes discounted off-peak pricing, schedule Power BI refreshes during those windows.</li> </ul>

<h2>Incremental Refresh with Snowflake</h2>

Incremental refresh is essential for large Snowflake datasets. Instead of reimporting the entire table on each refresh, Power BI queries only the rows that changed within a defined window.

<strong>Setup steps:</strong> <ol> <li>In Power Query, create two parameters: `RangeStart` (type DateTime) and `RangeEnd` (type DateTime).</li> <li>Filter your Snowflake table on a date column (e.g., `UPDATED_AT` or `LOADED_AT`) between `RangeStart` and `RangeEnd`. Ensure this filter folds to Snowflake SQL.</li> <li>In Power BI Desktop, right-click the table and select <strong>Incremental refresh</strong>.</li> <li>Configure the policy: for example, store 3 years of data and refresh data from the last 7 days.</li> <li>Publish to the Power BI Service. On first refresh, Power BI creates date-partitioned segments and loads the full history. Subsequent refreshes query only the recent window.</li> </ol>

<strong>Snowflake-specific considerations:</strong> <ul> <li>Ensure the filter column (`UPDATED_AT`) is included in the Snowflake table's clustering key for optimal pruning. Clustering on the date column means Snowflake scans only the relevant micro-partitions, reducing both execution time and credits.</li> <li>Use `CHANGE_TRACKING = TRUE` on the table if you need to detect deletes (Power BI incremental refresh with "detect data changes" uses polling, but Snowflake Streams provide a more efficient mechanism for custom pipelines).</li> <li>For tables exceeding 100 GB, incremental refresh can reduce refresh time from hours to minutes and cut credit consumption by 90% or more.</li> </ul>

<h2>Snowflake and Microsoft Fabric Integration</h2>

Microsoft Fabric introduces new integration patterns with Snowflake that go beyond the traditional Power BI connector.

<h3>Snowflake Shortcut in OneLake (Preview)</h3>

Fabric OneLake supports shortcuts to external data sources, including Snowflake. A Snowflake shortcut creates a virtual reference to a Snowflake table within OneLake—no data movement required. All Fabric workloads (Spark notebooks, SQL analytics endpoint, Direct Lake Power BI models) can read from the shortcut as if the data were stored natively in OneLake.

<strong>Benefits:</strong> <ul> <li>Zero-copy access to Snowflake data from all Fabric workloads</li> <li>Direct Lake Power BI models can reference Snowflake data without Import refresh</li> <li>Data remains in Snowflake, satisfying data residency and governance requirements</li> </ul>

<h3>Dataflows Gen2 to OneLake</h3>

For organizations that want Snowflake data physically copied into OneLake (for performance or to eliminate Snowflake compute dependency during dashboard usage), Dataflows Gen2 extracts from Snowflake and lands data as Delta tables in a Fabric Lakehouse.

<strong>Architecture flow:</strong> Snowflake (source) -> Dataflows Gen2 (extract + transform) -> OneLake Lakehouse (Delta tables) -> Direct Lake Power BI model (zero-import, reads Delta directly)

This approach decouples Power BI from Snowflake compute entirely. Dashboard interactions read from OneLake at memory speed without incurring Snowflake credits. The dataflow runs on a schedule (hourly, daily) using Fabric capacity compute.

<h3>Fabric Mirroring for Snowflake</h3>

Fabric Mirroring provides near-real-time replication of Snowflake data into OneLake. Unlike Dataflows Gen2 which run on a schedule, mirroring uses change data capture to continuously replicate changes with minimal latency. This is the ideal pattern for organizations that need fresh data in Power BI without the cost of DirectQuery against Snowflake.

<h2>Row-Level Security Bridging</h2>

Securing data at the row level across Snowflake and Power BI requires a coordinated approach. There are two primary patterns:

<h3>Pattern 1: Power BI-Side RLS (Import Mode)</h3>

Define RLS roles in Power BI Desktop using DAX filter expressions. For example: `[Region] = USERPRINCIPALNAME()` or table-based dynamic RLS where a security mapping table links user emails to authorized data segments. This approach is simple, well-documented, and works regardless of the data source. The limitation is that all data is imported into Power BI—RLS filters the in-memory model, but the full dataset exists in the Power BI workspace.

<h3>Pattern 2: Snowflake-Side RLS with SSO Pass-Through (DirectQuery)</h3>

When using DirectQuery with Azure AD SSO, Power BI passes the authenticated user's identity through to Snowflake. Snowflake then applies its own row access policies and data masking policies based on that identity. This provides defense-in-depth: data is filtered at the source, and only authorized rows ever leave Snowflake.

<strong>Setup:</strong> <ul> <li>Configure Azure AD SSO for the Snowflake connector (as described in the Authentication section)</li> <li>Create Snowflake row access policies that reference `CURRENT_USER()` or `CURRENT_ROLE()`</li> <li>Apply row access policies to the relevant tables</li> <li>In Power BI DirectQuery, each user's queries execute with their Snowflake identity, enforcing row-level filtering at the database level</li> </ul>

<strong>Recommendation:</strong> For most organizations, Power BI-side RLS with Import mode provides the best balance of performance, simplicity, and security. Use Snowflake-side RLS pass-through when regulatory requirements mandate source-level filtering or when the dataset is too large to import. Our <a href="/services/enterprise-deployment">enterprise deployment team</a> designs hybrid RLS architectures that meet compliance requirements for <a href="/services/data-analytics">regulated industries</a>.

<h2>Common Pitfalls and Troubleshooting</h2>

<strong>1. Query timeout during Import refresh</strong>: Snowflake defaults `STATEMENT_TIMEOUT_IN_SECONDS` to 172,800 (48 hours) at the account level, but Power BI has its own timeout (typically 2 hours for scheduled refresh). If large table extracts exceed the Power BI timeout, the refresh fails. Solution: pre-aggregate in Snowflake views, use incremental refresh, or increase the Power BI timeout in gateway settings.

<strong>2. Snowflake warehouse suspended during DirectQuery</strong>: If `AUTO_RESUME` is not enabled and the warehouse is suspended, DirectQuery fails immediately. Always set `AUTO_RESUME = TRUE` on warehouses used by Power BI.

<strong>3. Data type mismatches</strong>: Snowflake VARIANT, ARRAY, and OBJECT columns are not natively supported by the Power BI connector. Flatten these using `LATERAL FLATTEN` in a Snowflake view before importing. Snowflake TIMESTAMP_TZ columns should be cast to TIMESTAMP_NTZ to avoid time zone confusion in Power BI.

<strong>4. Case sensitivity</strong>: Snowflake identifiers are case-sensitive when quoted. If table or column names were created with double quotes in Snowflake (e.g., `"MyTable"`), Power BI must reference them with exact casing. Use unquoted (uppercase) identifiers in Snowflake to avoid this issue.

<strong>5. Excessive credit consumption</strong>: Monitor the `WAREHOUSE_METERING_HISTORY` view to track credits consumed by the Power BI warehouse. Set resource monitors with email alerts to catch cost overruns early.

<h2>Getting Started with Snowflake and Power BI</h2>

Connecting Snowflake to Power BI is a foundational step in building a modern analytics architecture. The integration delivers the best of both platforms: Snowflake's elastic, cost-efficient compute and storage with Power BI's rich visualization, governance, and distribution capabilities.

Start with a focused proof of concept: connect one Snowflake schema to Power BI using Import mode, build a dashboard on a well-defined use case (financial reporting, sales analytics, or operational metrics), and demonstrate the value within two weeks. From there, expand to incremental refresh for large tables, Fabric integration for advanced scenarios, and DirectQuery or composite models where real-time data is required.

Our <a href="/services/power-bi-architecture">Power BI architecture team</a> has deployed Snowflake-to-Power BI solutions for organizations across healthcare, financial services, and government—sectors where security, compliance, and performance are non-negotiable. We handle connection architecture, data modeling, DAX optimization, warehouse sizing, cost management, and ongoing support.

For Snowflake integrations with other platforms, see our guide on <a href="/blog/connect-salesforce-power-bi-integration-guide-2026">connecting Salesforce to Power BI</a>, which covers CRM analytics patterns that complement Snowflake warehouse data.

<strong>Ready to connect Snowflake to Power BI?</strong> <a href="/contact">Contact EPC Group</a> for a free consultation to discuss your Snowflake analytics requirements.