In today’s fast-paced business environment, effective supply chain analytics is crucial for success across industries. By integrating Power BI for reporting with Snowflake as a backend data platform, organizations can transform their approach to supply chain data. This integration enables real-time insights, facilitating smarter decision-making and responsiveness to market demands, optimizing operations and enhancing efficiency.
The issue
A leading U.S. retailer with over 300 stores faced challenges in managing its supply chain data and reporting systems. Despite a strong legacy of quality, customer satisfaction, and sustainability, their reliance on MySQL for data management and Qlik Sense for reporting was becoming increasingly inefficient. MySQL’s limited scalability led to performance bottlenecks as data volumes grew, while complex queries slowed processing times, making it difficult to manage large datasets effectively. To address these issues, the company decided to migrate to Snowflake, a scalable, cloud-based solution that supports real-time analytics and seamless integration with various data sources.
On the reporting side, the client faced difficulties with Qlik Sense, such as high costs, maintenance, and performance problems with large datasets. Additional expenses for certain add-ons further complicated their reporting infrastructure. By transitioning to Microsoft Power BI, they will gain a more cost-effective and user-friendly solution. This migration, combined with Snowflake’s integration, will improve data connectivity and streamline their reporting processes.
Our solution
Migrating from Qlik Sense to Microsoft Power BI provided the client with cost savings and a more user-friendly interface, simplifying report creation and sharing across the organization. Power BI’s seamless integration with Snowflake enables real-time analytics and advanced data visualizations, enhancing decision-making in supply chain operations. To further improve scalability and performance, the client transitioned from MySQL to Snowflake, addressing MySQL’s limitations with faster data processing, efficient handling of large datasets, and high concurrency support through its multi-cluster architecture and caching capabilities.
The migration followed a structured, multi-step process to ensure a seamless transition:
· Data transformation: Transformed raw data in Snowflake using a dedicated layer to process and generate Power BI-ready views.
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Data loading: Configured a unified semantic model, defining relationships in Power BI.
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Report building: Developed intuitive Power BI reports covering all necessary data points.
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Power Automate: Automated data updates by detecting changes in raw tables.
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GitHub integration: Enabled version control and collaborative development across Snowflake and Power BI.
| Figure 1: Power BI reporting powered by Snowflake |
Furthermore, we implemented best practices in Power BI with Snowflake as the data source to optimize performance.
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Efficient reports: Limited visuals in Power BI to enhance performance with large datasets. Snowflake’s query optimization retrieves only necessary data, reducing query complexity and compute costs.
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Optimal connection: The data gateway keeps data in Snowflake via standard mode, minimizing duplication and processing.
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Seamless data querying: Power BI’s DirectQuery mode enables real-time interaction with Snowflake, leveraging its capacity for large, concurrent queries.
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Elastic scaling: Snowflake’s multi-cluster warehouses and auto-scaling compute model adjust resources based on workload demands, ensuring smooth Power BI queries even at peak times.
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Data model design: Star schemas simplify the model and accelerate queries, while denormalizing frequently used metrics improves efficiency.
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Row-Level Security (RLS): Implemented RLS using Snowflake’s native features to ensure users see only relevant data, with complex calculations managed at the source for greater efficiency.
Business outcomes
Within four months, we migrated eight critical reports to Power BI, delivering a near-real-time analytics solution. This transition reduced operational costs, improved ROI, and provided a unified, automated system. The client can now seamlessly access insights, enabling confident, data-driven decisions.
Key improvements include:
· Faster report performance: Reports now load in near-real-time, eliminating data latency issues.
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Cost savings: Optimized data retrieval in Snowflake and Power BI’s affordable licensing reduced expenses.
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High user adoption: Over 80% of end users transitioned to Power BI, driving a data-centric culture.
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Enhanced accuracy: The Power BI semantic model simplified maintenance and improved reporting precision.
Conclusion
By integrating Power BI with Snowflake, we transformed the client’s reporting capabilities, boosting efficiency, reducing costs, and strengthening decision-making. These advancements position them for continued success in a data-driven landscape.
Contact CustomerSuccess@MAQSoftware.com to discover how Microsoft Fabric and Snowflake can optimize your business, enhance customer satisfaction, and unlock significant cost savings.