In systems where modern applications interact with databases designed many years ago, performance challenges are inevitable. On the application side, we often maintain strict discipline around clean architecture, modularity, and sustainable design. Yet in the database layer, fundamental structural details such as table names, column naming, and data types are frequently overlooked. These seemingly small decisions eventually grow into significant long-term problems.
When starting a new application, my focus is always on laying the right foundation and enabling the team to deliver the most effective solution. In practice, however, short-term delivery pressures tend to push long-term maintainability into the background. Solutions that appear harmless at first gradually evolve into complex systems that are difficult to understand and maintain. Over the years, I have learned that the most resilient systems begin with the simplest designs.
As the application matures and becomes a critical component of the organisation’s ecosystem, discussions around performance and modernisation inevitably rise to the surface. In most cases, the first meaningful improvements always come from the database layer. Understanding how the system behaves under real load and identifying the most critical bottlenecks becomes a key responsibility for architects and senior engineers.
Below are the essential checkpoints I have refined through real-world experience and that I consider essential in any modern database transformation effort.
Stations – Key Checkpoints in Database Modernisation
1. Identifying Long-Running Queries
The first step is identifying queries that consistently run for long periods, usually triggered by real application users. This requires analysing database query logs and making use of Dynamic Management Views, particularly those starting with sys.dm_exec_*. These views are among the most reliable tools for understanding how the system behaves internally.
2. Structural Analysis of Queries
Each identified query should be examined in detail. Unnecessary use of UNION, scalar functions inside SELECT statements, deep nested subqueries, and poorly designed filters often lead to significant performance degradation. Understanding the true intent of each query is crucial.
3. Query Optimisation and Execution Plan Review
Once the problematic queries are identified, the optimisation phase begins. This includes revisiting join strategies, selecting the correct union operations, eliminating redundant filters, and ensuring proper indexing. Reviewing the execution plan is the compass that guides these decisions.
4. Hidden Problems with Data Type Mismatches
One of the most frequently overlooked issues in real projects is a data type mismatch. A column that appears to contain numeric data might actually be defined as VARCHAR, or DATETIME value might be stored as text. These decisions introduce implicit conversions, excessive CPU usage, and hard-to-trace errors. Automatic conversions performed by the database are not always a benefit and often hide the root cause of performance issues.
5. Considering Materialised Views
For frequently accessed datasets, a materialised view can be helpful. However, a properly indexed table often provides better performance. Materialised views should be evaluated carefully, as they are not a universal solution and still require underlying table access.
6. Deep Analysis of Stored Procedures
Even in environments that use ORM tools extensively, batch operations and heavy data manipulation are still handled through stored procedures. These objects must be evaluated with the same optimisation principles used for regular queries. Every stored procedure behaves like a small application inside the database.
7. Triggers and the Risk of Unexpected Behaviour
Triggers are responsible for many of the unexpected behaviours that appear during database modernisation. They are frequently forgotten until they produce surprising results after a schema or data change. Business logic inside triggers creates long-term risks and must be carefully reviewed.
8. High-Load Analysis and Maintenance Operations
It is critical to analyse table usage patterns and ensure that database statistics remain up to date. Regular index rebuilds, index reorganisations, and statistics updates are essential for keeping the system healthy. Neglecting these operations eventually weakens even the most powerful hardware.
9. Evaluating the Need for Partitioning
Partitioning is one of the most effective ways to improve performance and scalability for large datasets. A well-designed partitioning strategy can deliver significant gains, especially in systems with rapidly growing data volume.
10. Reducing Linked Server Usage
One of the biggest performance improvements often comes from reducing or eliminating Linked Server usage. Network latency, dependency on remote systems, and implicit data type conversions make this approach costly and unpredictable in most scenarios.
Conclusion – The Mindset of Continuous Improvement
Database modernisation is not a one-time activity. It is a continuous improvement discipline. A design that looks perfect today may become insufficient as data grows and business needs evolve. Making the right architectural decisions early dramatically reduces long-term costs and operational risks.
Always remember the essence of this work. Database optimisation is the discipline of learning from past mistakes. The earlier the issues are identified, the smaller the cost of fixing them.