Data Platform Optimization

Performance improvements and scalability enhancements for Cohesity's data management platform

Overview

As a Senior Software Engineer on the Data Platform team at Cohesity, I’ve been instrumental in optimizing the performance and scalability of our distributed data management platform. This work involves handling petabytes of data across thousands of nodes while maintaining high availability and consistency.

Key Contributions

Performance Optimization

  • Database Query Optimization: Improved query performance by 40% through index optimization and query restructuring
  • Memory Management: Reduced memory footprint by 25% through efficient data structures and caching strategies
  • I/O Optimization: Enhanced disk I/O performance for large-scale data operations

Scalability Improvements

  • Distributed Processing: Implemented distributed algorithms for parallel data processing across cluster nodes
  • Load Balancing: Designed and implemented intelligent load balancing for data distribution
  • Resource Management: Optimized resource allocation algorithms for better cluster utilization

System Reliability

  • Fault Tolerance: Enhanced system resilience through improved error handling and recovery mechanisms
  • Monitoring & Alerting: Implemented comprehensive monitoring solutions for proactive issue detection
  • Data Consistency: Ensured data integrity across distributed operations

Technologies Used

  • Languages: C++, Python, Go
  • Distributed Systems: Apache Kafka, Redis, Kubernetes
  • Databases: PostgreSQL, Cassandra
  • Monitoring: Prometheus, Grafana
  • Cloud Platforms: AWS, GCP

Impact

  • Improved overall system performance by 35%
  • Reduced operational costs through better resource utilization
  • Enhanced system reliability with 99.9% uptime
  • Enabled the platform to scale to handle larger enterprise workloads