About Client
The client is one of the largest home leasing companies in the U.S., operating at a national scale with a diverse portfolio of residential rental properties. Their business relies heavily on data to manage property performance, tenant information, financial reporting, maintenance operations, and executive-level analytics. Accuracy, speed, and reliability of data are critical to daily operations as well as long-term strategic planning.
Background
Home leasing organizations generate and consume data from multiple systems—property management platforms, CRM tools, ERP systems, financial applications, and operational databases. Historically, the client relied on a SQL Server–based enterprise data warehouse to consolidate and analyze this information.
While SQL Server had served as a dependable foundation for years, increasing data volumes and a growing need for near real-time analytics exposed several limitations. Scaling compute resources was complex and expensive, performance degraded during peak usage, and the infrastructure required continuous maintenance. As a result, leadership began evaluating data warehouse migration to Snowflake as a long-term solution that could offer elasticity, performance isolation, and predictable cost control.
Challenges
The decision to pursue a Snowflake migration from SQL Server was driven by multiple operational and technical challenges:
- Complex maintenance overhead: The SQL Server enterprise data warehouse depended on a legacy Microsoft stack, including SSIS (SQL Server Integration Services). Managing ETL workflows, tuning performance, and maintaining infrastructure required significant manual effort and specialized expertise.
- Performance and scalability constraints: Compute resources were shared across analysts, BI teams, and data engineers. During peak query times, performance degraded, leading to slower insights and delayed reporting for business users.
- High operational costs: Licensing, infrastructure upgrades, and ongoing maintenance pushed annual costs to approximately $250,000, making the existing platform increasingly unsustainable.
- Limited agility for analytics growth: Adding new data sources or scaling workloads required extensive planning, slowing down innovation and experimentation across analytics teams.
