Peeush Agarwal > Engineer. Learner. Builder.

I am a Machine Learning Engineer passionate about creating practical AI solutions using Machine Learning, NLP, Computer Vision, and Azure technologies. This space is where I document my projects, experiments, and insights as I grow in the world of data science.

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Core Architecture Principles

This document outlines the fundamental principles for designing robust system architectures. Adhering to these principles helps ensure that systems are scalable, maintainable, and resilient. Key principles covered include:

  1. Vertical and Horizontal Scaling: Understanding the differences between vertical and horizontal scaling, and when to apply each strategy.
  2. CAP Theorem: Exploring the trade-offs between consistency, availability, and partition tolerance in distributed systems.
  3. PACELC Theorem: An extension of the CAP theorem that considers latency and consistency during normal operation.
  4. ACID vs BASE: Comparing the ACID properties of traditional databases with the BASE properties of NoSQL databases.
  5. Throughput vs Latency: Balancing throughput and latency to meet application performance requirements.
  6. Amdahl’s Law: Understanding the limits of parallelization and its impact on system performance.
  7. Strong vs Eventual Consistency: Examining the differences between strong and eventual consistency models in distributed systems.
  8. Stateful vs Stateless Architectures: Analyzing the benefits and challenges of stateful and stateless system designs.
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