Many companies are moving their data systems to the cloud to gain scalability, flexibility, and lower costs. Still, there’s a hidden danger behind this development: unoptimized queries can harm how a database performs. Many companies migrate legacy systems without revisiting how their queries are structured. This oversight is often the root cause of poor performance in the cloud. The result? Performance lags, inflated cloud bills, and frustrated stakeholders often stem from outdated query logic, not from the cloud itself.
Santosh Vinnakota, a skilled data engineer, has become a key figure in redefining the boundaries of cloud performance. He has led large-scale optimization projects and tackled some of the most complex cloud data warehouse migrations. From healthcare to digital media, he has achieved impressive things: he reduced the time it takes to execute tasks from 45 minutes to under 5 minutes, consumed less than 35% of the costs for important queries and received praise for his advances in real-time analytics. This performance leap allowed real-time dashboards to replace delayed batch reports, fundamentally changing how decisions are made across departments. It’s no surprise that Santosh now leads query optimization on enterprise-scale projects.
Santosh’s work covers more than just refactoring code. He emphasizes that optimizing queries should be integrated into a data engineer’s role rather than viewed as something only done when the urgent need arises. Using prepared plans for monitoring and performance assessment, he has enabled teams to both see inefficient queries and measure the lasting effect they have on budgets and how users interact with the database. Because of his data models, teams can predict future financial results in real time, see their logistics in action and find out how people are interacting with their media.
Figures provide the clearest evidence of what happened. Queries in systems guided by Santosh now finish at least 40% faster, so these environments can update in real time without having to depend on batches. There have been major increases in the accuracy of monetization models by double digits and overall system performance is now steadier and more predictable, leading to more precise revenue forecasting and improved advertiser targeting. These changes aren’t simple fixes; they have fundamentally changed the boundaries of cloud analytics.
His point is that moving to the cloud without checking your queries is like buying a sports car but never passing first gear. He has dealt with many of the most difficult problems such as working on complex SQL, overly elaborate schema designs and databases without good indexes. His approaches such as choosing what to materialize, tuning Delta Lake storage formats and implementing proactive error-checking for queries, have made the analytics platform perform better while keeping it easy to maintain and extend.
Santosh believes that in the future, people will handle query optimization as routinely and automatically as testing code. He suggests changes that include SQL reviews during development, service-level agreements for performance set up on dashboards and tools that spot trouble early. For this reason, he believes that the next important step for cloud data engineering is to develop intelligent systems that spot and address issues on their own, so teams can concentrate more on finding useful insights rather than solving urgent issues. As every second and data access is valuable now, Santosh Vinnakota’s work encourages us to rethink how we query, optimize, and extract value from cloud data, because simply migrating isn’t enough anymore.
