How Distillery transformed an underperforming data warehouse migration into a successful outcome.
One of Distillery’s recent data engineering engagements involved helping a global payments network optimize its enterprise reporting environment by migrating four business-critical reports from a transactional database to a SQL Server data warehouse.
The objective was straightforward: improve report performance while maintaining complete result accuracy.
The migration involved designing and building the supporting tables and ETL processes required to load transactional data into the data warehouse. Once complete, the team began benchmarking report performance against the existing transactional database.
The initial results revealed an unexpected outcome.
Nearly all four reports performed worse in the data warehouse than they had in the transactional environment.
Rather than considering the project complete, our data engineering team investigated the underlying cause, identified optimization strategies designed specifically for analytical workloads, and ultimately delivered a 64% overall performance improvement compared to the original transactional database while maintaining complete data accuracy.
The Challenge
The project involved migrating four DataSuite reports from a transactional database to an enterprise data warehouse.
To support the migration, our team designed the necessary tables and ETL processes to ensure transactional data was accurately loaded into the warehouse. Success would be measured against two objectives:
- Improve report performance compared to the transactional database
- Maintain identical reporting results after migration
Although the migration itself was completed successfully, the first round of performance testing showed that nearly every report executed more slowly in the data warehouse than in the transactional environment.
Rather than accepting those results, the team paused implementation to understand why the new environment was underperforming.
The Investigation
The findings reinforced an important principle of data engineering: migrating reporting workloads into a data warehouse does not automatically improve performance.
After several days of research and performance analysis, the team identified two optimization strategies specifically designed for analytical workloads.
Database Partitioning
Database partitioning was implemented across the enterprise data warehouse to improve data access patterns and query efficiency.
By reducing the amount of data processed during query execution, partitioning helped improve performance across reporting workloads.
Columnstore Indexes
The team also implemented columnstore indexes on the warehouse’s largest tables.
Unlike traditional row-based indexes, columnstore indexes use columnar storage and compression while enabling batch execution mode, making them particularly effective for analytical and reporting queries.
Together, these optimizations significantly improved how SQL Server executed the migrated reports.
The Results
After implementing both optimizations, the team reran performance testing.
The final results exceeded the project’s original performance objectives while maintaining complete report accuracy.
Overall improvements included:
- 64% overall performance improvement compared to the transactional database
- 16.3× performance gain based on total execution time
- 49 minutes and 21 seconds of total execution time saved across the four reports
- Complete result accuracy maintained throughout the migration
Performance improvements by report included:
| Report | Performance Improvement |
| Atcan Settlements | 98% |
| Atcan Volume | 80% |
| Advanced Transactions Search | 43% |
| Transactions Search | 35% |
The final benchmarking demonstrated significant improvements across all four migrated reports while preserving data integrity throughout the migration.
“This achievement reflects the best of what the DBA Team stands for: technical excellence, resilience under pressure, and an uncompromising commitment to delivering quality. What started as a setback became a showcase of the team’s depth and determination.”
– Marcelo Javier Acevedo Picco, Senior DBA Microsoft SQL Server at Distillery
Key Takeaways
This project demonstrates that migrating workloads into a data warehouse is only part of the process.
While enterprise data warehouses are designed for analytical workloads, realizing their full performance potential requires optimization strategies tailored to those environments.
In this engagement, implementing database partitioning and columnstore indexes transformed an underperforming migration into one that exceeded its original performance targets without compromising data accuracy.
Looking Ahead
As organizations continue to modernize their data platforms, performance optimization remains a critical part of successful data warehouse initiatives.
At Distillery, our data engineering teams help organizations design, modernize, and optimize data platforms that support scalable analytics, efficient reporting, and long-term performance.
Whether you’re modernizing your data platform or optimizing an existing environment, our data experts are here to help. Contact us to schedule a complimentary 60-minute consultation.