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Executive Summary Cellular architecture represents a paradigm for designing and operating distributed systems to achieve extreme levels of resilience and virtually limitless scalability. Inspired by the bulkhead design in shipbuilding, Read More …
Part 1: The Paradigm of Federated Graphs Section 1.1: Introduction to Distributed GraphQL: Beyond the Monolith The adoption of GraphQL has marked a significant evolution in API design, offering clients Read More …
The Imperative for Observability in Distributed Systems The modern software landscape is defined by a paradigm shift away from monolithic application architectures toward distributed systems, most notably those composed of Read More …
The API Gateway as a Cornerstone of Microservice Architecture The architectural shift from monolithic applications to distributed microservice ecosystems has fundamentally altered how modern software is designed, deployed, and managed. Read More …
Section 1: An Introduction to High-Performance RPC 1.1. Defining the Modern RPC Framework: gRPC gRPC (General Remote Procedure Call) is a modern, open-source, high-performance Remote Procedure Call (RPC) framework that Read More …
The Imperative for Dynamic Discovery in Microservice Architectures The transition from monolithic to microservice architectures represents a fundamental paradigm shift in how applications are designed, deployed, and managed. This shift Read More …
Section 1: Redefining the Architectural Spectrum: Beyond the Binary The contemporary discourse on software architecture has long been dominated by a perceived dichotomy: the legacy Monolith versus the modern Microservice. Read More …
Executive Summary This report provides a definitive analysis of distributed GraphQL architectures, guiding platform decisions by deconstructing the evolution from monolithic API servers to sophisticated, distributed graphs. The analysis addresses Read More …
Executive Summary This report provides a comprehensive analysis of multi-model serving (MMS), a critical paradigm for efficiently deploying large numbers of machine learning models on shared infrastructure. We deconstruct the Read More …
Part 1: Foundations of the Modern ML Deployment Stack The transition of a machine learning model from a development environment, such as a Jupyter notebook, to a production system that Read More …