Optimized Cloud Infrastructure and Data Platform for Scalable Data Processing and Cost Reduction

The client faces significant challenges with high operational costs, suboptimal performance, and limited scalability in their data infrastructure, which processes approximately 30 TB of raw data monthly, with a target query throughput of 2,000 queries per second and a service availability of 99.9%. The current setup struggles with redundancy, cost inefficiencies, and failover capabilities, hindering their ability to process and analyze large-scale data efficiently while maintaining reliability.

A mid-to-large scale fintech company handling extensive transaction and customer data, requiring high-performance data processing infrastructure.

• Reduce monthly cloud infrastructure costs from approximately $75,000 to below $25,000.
• Achieve a minimum of 2,000 queries per second query performance with over 30% performance margin compared to SLA requirements.
• Implement a highly available infrastructure with automated failover across multiple regions to ensure 99.9% service uptime.
• Enhance data architecture for faster query execution and lower latency, facilitating real-time data processing.
• Establish scalable, flexible, and resilient database sharding, along with integrations of caching, messaging, and search systems to support diverse data streams.

• Self-hosted database clusters utilizing sharding to optimize query performance and reduce costs.
• Implementation of messaging systems (e.g., Kafka) for streaming data pipelines.
• Integration of search and caching layers (e.g., Elasticsearch, Redis) to accelerate data retrieval.
• Automated failover strategies with cross-region master/slave replication to ensure high availability.
• Optimization of ETL processes to minimize query load and improve data freshness.
• Monitoring, logging, and alerting tools for system health, performance metrics, and fault detection.

• External ETL pipelines for data ingestion
• Monitoring and alerting systems
• Regional failover infrastructure for redundancy
• APIs for data querying and reporting

• Supports at least 2,000 queries per second with performance margin
• 99.9% service availability with multi-region failover
• Cost efficiency reducing operational expenses by approximately 70%
• System scalability to handle increasing data volume and query load
• Real-time data processing and updates

The new infrastructure aims to dramatically lower operational costs from around $75,000 to below $25,000 per month, while enhancing query performance to support over 2,000 QPS with performance over SLA. Improved redundancy and failover mechanisms will ensure near-continuous uptime of 99.9%, enabling real-time data insights, faster decision-making, and greater scalability to accommodate future growth.