Database Modernization, Cost Optimization, and Performance Planning
Industry
Financial Services
Teams & Services
Cloud Infrastructure, DevOps, Data Engineering, Database Optimization, Analytics Enablement
Tech & Tools
AWS Aurora, MySQL, AWS DMS, Snowflake,
Key Data Points
The Vision
PriceSpider helps leading global brands track and optimize their e-commerce presence. As traffic and data volumes grew, their legacy infrastructure struggled to keep up. We partnered with their team to modernize their backend and unlock new performance potential—without disrupting the customer experience.
The Goal
To eliminate data bottlenecks, reduce query latency, and future-proof the platform with a more scalable, cost-effective data infrastructure—while ensuring seamless analytics access for end users.
The Challenge
With read-intensive workloads maxing out RDS capabilities, slow dashboards, and massive historical datasets, PriceSpider faced growing pain points in performance and cost. A combination of infrastructure constraints, data bloat, and inefficient queries threatened scalability. A strategic transformation was needed to restore velocity and reliability.
The Solution
We executed a three-phase modernization strategy:
Phase 1: Aurora Migration
We transitioned PriceSpider’s primary database to Amazon Aurora MySQL, leveraging its parallel query engine and auto-scaling IOPS to eliminate sustained load issues, improve write throughput, and reduce baseline latency across the platform.
Phase 2: Historical Row Cleanup
Using AWS DMS, we safely removed billions of obsolete records across several large datasets. This not only reduced storage and CPU costs but significantly boosted query performance—especially for time-bound and dashboard queries.
Phase 3: Query Optimization Strategy
We conducted in-depth query analysis to uncover the most impactful slowdowns and delivered a clear optimization roadmap focused on high-frequency and long-running queries, with targeted recommendations across indexing, filtering, joins, and subqueries