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Software Development
Amazon RDS, MySQL, Amazon CloudWatch, AWS IAM
Intelligent Data Lifecycle Management and MySQL Partitioning Framework for high-volume transactional databases on Amazon RDS.
Abright Lab is a software company with multidisciplinary digital product experts focused on user experience, design, and development. They extend the design and development departments of the most innovative companies. Abright Labs uses digital product design and development expertise to achieve quantifiable business goals, build a strong development framework early on and empower their customers to continue maintaining consistent product.
Query Performance Improvement
Maintenance Window Reduction
Scalability
The client’s primary database environment struggled with severe scalability issues driven by unchecked growth of massive transactional and notification tables past hundreds of millions of records. Query execution slowed significantly, data retention management became a major operational burden with traditional deletions causing transaction overhead and replication delays, uncontrolled storage growth bloated costs, and inadequate operational visibility into partition growth and retention compliance created significant governance challenges.
• Date-based monthly RANGE partitioning on high-volume tables using transaction timestamps, enabling partition pruning to restrict scans to relevant partitions and reduce query overhead.
• A scheduled framework to auto-create future partitions and retire expired ones via partition drop operations, reducing maintenance from hours to seconds.
• Reporting and audit queries optimized with partition-aware filtering, reducing I/O and improving response times without application changes.
• Amazon CloudWatch monitoring and custom dashboards tracking partition growth, pruning effectiveness, and retention compliance with automated anomaly alerts.
• Automated partition expiration enforcing retention policies while maintaining audit readiness and regulatory compliance.
• Logical data segmentation and automated retention controls reducing storage consumption and delaying infrastructure expansion needs.
Achieved 60-90% query performance improvement, reduced maintenance windows from hours to seconds, and enabled scalable data growth beyond hundreds of millions of records without infrastructure changes through intelligent partitioning and automated lifecycle management.
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