Case Studies

Real outcomes from companies that faced similar challenges. See how technical improvements translated to business results.

Enterprise Technology

Cut Infrastructure Costs by Optimizing IoT Platform

Dell Technologies

6 weeks

Reduced memory usage by 40% across IoT services, lowering infrastructure costs and improving system reliability.

EdgeX Foundry microservices were experiencing high memory consumption leading to frequent garbage collection pauses, degraded performance, and increased infrastructure costs across distributed IoT deployments.

How it was solved
  • Conducted comprehensive heap dump analysis and profiling of Go and Java services
  • Identified memory leaks and inefficient object allocation patterns
  • Implemented object pooling for frequently created sensor data objects
  • Tuned JVM garbage collection parameters for IoT workload characteristics
  • Refactored data structures to reduce memory footprint

Technologies Used

Go Java Redis Docker EdgeX Foundry IoT

Key Results

Before & After
Before
High consumption
→
After
40% reduction
40% reduction
Memory Usage
Across all EdgeX microservices
60% reduction
GC Pause Time
P99 latency improved significantly
Significant savings
Infrastructure Cost
Through reduced instance requirements
Healthcare Technology

From Overnight Reports to Real-Time Analytics

Prognos Health

10 weeks

Transformed multi-terabyte data processing from hours to minutes, enabling same-day healthcare analytics.

Critical data processing pipeline was taking hours to complete multi-terabyte HIPAA-compliant datasets, delaying insights and limiting business scalability. Sequential processing and inefficient data structures were the primary bottlenecks.

How it was solved
  • Redesigned architecture from sequential to parallel processing
  • Implemented serverless event-driven pipeline (Lambda, SNS, SQS, S3)
  • Built Go-based microservices for high-throughput stages
  • Integrated Apache Spark for distributed data processing
  • Optimized Parquet file structures and added strategic caching

Technologies Used

Go Scala Apache Spark AWS Lambda SNS SQS S3 Terraform

Key Results

Before & After
Before
Hours
→
After
Minutes
Hours → Minutes
Processing Time
Multi-terabyte datasets processed in minutes vs hours
10x increase
Throughput
Processing capacity scaled linearly with resources
Near real-time
Data Freshness
Enabled same-day analytics vs next-day
Retail & E-commerce

From Monthly Deploys to Weekly Releases

Shipt (Target, Walgreens)

Multi-year engagement

Enabled 5x faster deployments and scaled team from 1 to 6 engineers through successful monolith decomposition.

Legacy monolithic application serving major retailers (Target, Walgreens) was limiting deployment velocity, making it difficult to scale specific features, and creating a single point of failure for the entire system.

How it was solved
  • Applied strangler fig pattern for gradual migration
  • Designed domain-driven bounded contexts for retail operations
  • Implemented event-driven architecture using Kafka for service communication
  • Created comprehensive testing strategy for parallel run validation
  • Established CI/CD pipeline for microservices deployment
  • Grew team from 1 to 6 engineers during migration

Technologies Used

Go Kafka Kubernetes PostgreSQL Redis Microservices

Key Results

Before & After
Before
Monthly
→
After
Weekly
5x increase
Deployment Frequency
From monthly to weekly deployments
1 → 6 engineers
Team Growth
Parallel team development enabled
Zero downtime
System Reliability
Eliminated single point of failure during migration

Ready for Similar Results?

Let's discuss how these proven approaches can solve your specific challenges.

Start a Conversation