Business and operations teams depend on SQL-based reports for performance reviews, trend analysis, and cost monitoring. When reporting queries are slow or expensive, insights are delayed, review cycles are disrupted, and decision-making quality suffers.
This project focuses on analyzing reporting inefficiencies and improving the cost and turnaround time of business analytics queries — without changing business metrics or logic.
Stakeholders faced challenges with recurring analytical reports:
- Reports took too long to load, slowing monthly and ad-hoc reviews
- High query costs limited frequent analysis
- Time-based analysis (monthly, yearly trends) was inefficient
- Scalability risks as data volume increased
The issue was not incorrect data, but inefficient data access during analysis.
Enable faster, more cost-efficient business reporting while preserving the accuracy and consistency of existing KPIs.
- Reviewed commonly used reporting queries and execution behavior
- Identified full data scans as the primary driver of slow performance and high cost
- Assessed how reporting patterns aligned with time-based analysis needs
- Validated that performance issues were structural, not metric-related
- Redesigned data access to better support time-based reporting
- Optimized SQL queries to minimize unnecessary data scanning
- Ensured business outputs and KPIs remained unchanged
- Tested reporting performance before and after changes
- ~50% improvement in report response time
- ~99% reduction in data scanned per query
- Significant reduction in analytics cost per report
- Improved usability for recurring business reviews
- Faster access to insights for stakeholders
- Lower cost for recurring and exploratory analysis
- Improved analyst productivity
- Greater confidence in data-driven decision-making
- Real-world datasets contain inconsistencies that can affect reporting reliability
- Data organization directly impacts reporting SLAs and cost
- Small structural improvements can unlock large business value
- Measuring before-and-after performance is critical for decision justification
- SQL (business reporting and analysis)
- AWS Athena (analytics execution)
- Amazon S3 (analytical data storage)
Reporting efficiency is a business problem, not just a technical one. Optimizing how data is accessed enables faster decisions, better cost control, and more effective analytics without changing business logic.
- AWS Athena Documentation
- Parquet Format Guide
- SQL Query Optimization Best Practices
- AWS Athena Partition Pruning
Vibin krishna |(https://www.linkedin.com/in/vibin-krishna-3a713518b)|(vibinkrishna574@gmail.com)