Executive Summary This case study explores the development of DooLittle Health Patient Timeline Explorer, a modern healthcare application that demonstrates enterprise-grade architecture patterns for FHIR-compliant patient data visualization. Built as a proof-of-concept, this project showcases best practices in full-stack development, cloud-native deployment, and healthcare interoperability standards. 🏥 HEALTHCARE INTEROPERABILITY SERIES This article is part of […]
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Serverless Showdown: Cloud Run vs Cloud Functions vs App Engine – Choosing the Right GCP Compute Platform
Serverless Showdown: Cloud Run vs Cloud Functions vs App Engine Choosing the Right GCP Compute Platform for Your Workload I’ve deployed applications to all three GCP serverless platforms—Cloud Run, Cloud Functions, and App Engine. Each has strengths, but choosing wrong costs time and money. I’ve seen teams spend weeks migrating from App Engine to Cloud […]
Read more →The Type Revolution: How Python’s Gradual Typing Transformed My Approach to Building Production Systems
Executive Summary Five years ago, I would have dismissed Python type hints as unnecessary ceremony for a dynamically typed language. Today, I cannot imagine building production systems without them. This shift did not happen overnight—it came from debugging production incidents at 3 AM, onboarding new team members to complex codebases, and watching refactoring efforts spiral […]
Read more →Tips and Tricks – Use dbt for Maintainable Data Transformations
Build modular, tested, documented data transformations with dbt.
Read more →Tips and Tricks – Partition Large Tables for Query Performance
Use table partitioning to dramatically speed up queries on large datasets.
Read more →Orchestrating Chaos: Why AWS Step Functions Became My Secret Weapon for Building Resilient Distributed Systems
Three years ago, I inherited a distributed system that processed insurance claims across twelve microservices. The orchestration logic lived in a tangled web of message queues, retry handlers, and compensating transactions scattered across multiple codebases. When something failed—and in distributed systems, something always fails—debugging meant correlating logs across a dozen services while the business waited […]
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