Achieving a balance between data integrity & performance in data platform architecture is challenging but it depends on what the use case is. The key is to balance both approaches in making trade-offs based on the use cases. If we are building critical transactional systems, we need to prioritize data integrity with strong consistency models. At the same time, we try improving performance by implementing techniques like data caching, data sharding or partitioning, MV etc.. If we are building analytical data platform we have to focus on optimizing performance negotiating integrity, but same time we usually implement fallback mechanisms to ensure data consistency and data quality, which is a key for successful implementation of EDW platform

In a high-stakes project, my team encountered a database with rapidly increasing load and performance issues that risked data integrity. - We had to make quick decisions, balancing immediate fixes with long-term stability. - Rather than compromising data integrity for a quick boost, I implemented a phased optimization strategy. - We first indexed critical tables and offloaded heavy queries to read replicas. - Next, we applied row-level locking, ensuring consistency without affecting overall throughput. -This approach allowed us to maintain data integrity and improve performance, proving that a structured, step-by-step method can yield effective results even under pressure.

When we say, capacity challenge, it can CPU, Memory, Network and storage. If we can move to Cloud where we can easily scale out or scale in as and when required, its nothing better than. But while using cloud, we all have to understand every scale out is proportionate to cost. If the capacity is getting utilized randomly, we should work with application team to look for the reasons for abnormal growth of data and/or user population. If its abnormal growth of data, we should advise for purging, compression , partitioning , etc . Also, we can move the less frequently used data to low cost storage.

For critical systems where data integrity cannot be compromised, balancing the system performance is an art that comes with a proper assessment. Most of the relational databases satisfy ACID by default, and the letter "D" in ACID means durability. What it means is that a transaction committed is successful only when the data is written to a persistent storage (WAL or REDO or Transaction log). There may be ways to disable durability and many other important parameters for achieving a better performance. But, this comes with a cost for the business. Balancing the performance requires database engineers to understand the short-term, mid-term and long-term goals in finding approaches to eventually optimize with temporary hardware upgrades.

?? Evaluate Impact: Assess the immediate need for integrity vs. performance. For critical operations, prioritize data integrity as compromised data can cause lasting issues. ?? Prioritize Tasks: Focus on essential integrity checks, while applying faster, temporary solutions—like read-only access or caching for frequently accessed data. ?? Real-Time Monitoring: Spot bottlenecks and areas where integrity and speed can be balanced. ??? Optimize Timing: Schedule performance-intensive checks during off-peak times to reduce system impact. ?? Clear Communication: Keep stakeholders informed about performance decisions that protect both data and reliability.