Aventude's data architecture redesign delivers sub-minute reporting for a future-ready platform

When an enterprise expands, so does the complexity and volume of data driving its day-to-day operations. Legacy systems such as SQL Server Management Studio (SSMS) and Reporting Services (SSRS) struggle to keep pace with increasing demands as businesses scale rapidly. This was the challenge faced by The Princeton Review.

The Princeton Review (TPR), headquartered in New York, is a leading education services provider in the United States. For over four decades, it has helped millions of students prepare for high-stakes standardised exams through expert tutoring, test preparation, and admissions resources. Student enrolments, instructor schedules, revenue metrics, and customer insights all depended on timely reporting.

However, TPR’s legacy infrastructure lacked the agility to meet evolving demands. Reliance on SSMS and SSRS led to report response time exceeding 20–30 minutes on average, which delayed decision-making and negatively impacted user satisfaction. That’s when they turned to Aventude.

Aventude Data team along with TPR stakeholders rebuilt the data architecture end-to-end, adopting Snowflakes, and shifting reporting to Power BI. Along the way, the team implemented automated data quality checks, Gen AI driven data validation capabiltiies built dynamic pipelines, real time auto healing with step up workfows.and visualized platformusage analytics to help TPR optimize costs.

The result? A futureready platform that is faster, smarter, and measurably more efficient.

Unlocking predictive analytics based on machine learning

By migrating to Snowflake team also able to avail itself to the new functionalities that Snowflake offers as a data platform such as building predictive models to forecast revenue, new students signups, course demands, and course utilisation per student.

Beyond core operational data, we integrated additional sources such as Dialpad callcentre interactions via APIs. Combining these with enrolment and performance data gives TPR a more holistic view of the student journey, from first contact to course selection, course attendance, learning management, mock exams, and final outcomes. This enables smarter marketing, better resource planning, and improved student support interventions.

To accelerate iteration and scalability, Aventude built dynamic, metadata driven procedures so new tables can be onboarded with minimal effort: provide schema, table name, and an identifier, and the pipeline generates the destination structures, loads data, and runs integrity checks automatically. This flexibility of the design minimises manual overhead, speed up the delivery, and keeps the platform agile as the organisation’s needs evolve.

Efficiency and cost optimisation through smart monitoring

By using both Snowflake and Power BI, TPR is also able to view platform usage analytics, such as computing demand, refresh frequency, dataset sizes, and user activity.

By extracting admin and workspace statistics, The Princeton Review team can see which Power BI dashboards are most frequently used, how often datasets refresh, and where premium capacity is being consumed. These analytics helps team prioritize optimization efforts and govern platform growth more effectively.

Similarly, by leveraging Snowflake’s native usage data, the team was able to monitor warehouse utilization, credit consumption by workload, and the cost footprint of specific pipelines or procedures. This level of detail helped identify areas where resources were being underused and fine-tune resource allocation.​

This allows TPR to optimize its computing resource usage, scale with confidence, and maximize their efficiency.

​ Aventude data team helped transform a major performance challenge into a strategic advantage. By moving to Snowflake, implementing Power BI, and introducing quality, cost controls, and platform observability, the team reduced report load times to under a minute, unlocked predictive analytics, and gained tighter cost oversight.​