EP196: Cloud Load Balancer Cheat Sheet

2025-12-15 · Source: ByteByteGo Newsletter · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

This content provides a multi-faceted overview of essential cloud-native technologies and architectural patterns, including a cheat sheet for cloud load balancers, an explanation of CQRS (Command Query Responsibility Segregation), a breakdown of Docker's architecture, and six practical AWS Lambda application patterns. It details how Docker clients, hosts, and registries interact to build and run containers, and how containerization ensures portability and isolation. The CQRS explanation outlines a six-step process for separating read and write operations to enhance scalability. Additionally, it presents specific use cases for AWS Lambda, such as on-demand media transformation, real-time data processing with Kinesis, and automated stored procedures, emphasizing serverless benefits.

Key takeaway

For MLOps Engineers or Software Engineers designing scalable, resilient cloud applications, understanding these core patterns is crucial. You should evaluate CQRS for systems requiring distinct read/write scaling, leverage Docker for consistent deployment across environments, and explore AWS Lambda patterns for event-driven, cost-efficient processing. Prioritize selecting the correct load balancer type based on traffic, scalability, and security to optimize application performance and availability.

Key insights

Cloud-native architectures enhance scalability and efficiency through specialized components like load balancers, CQRS, Docker, and serverless functions.

Principles

• Separate read/write operations for scalability.
• Containers ensure application portability.
• Serverless functions abstract infrastructure.

Method

CQRS involves client commands, handler validation, write database storage, asynchronous event emission, read model projection, and query handler data retrieval from precomputed projections in a read database.

In practice

• Use AWS Lambda for on-demand media transformation.
• Implement CQRS for high-scale applications.
• Utilize Docker for consistent application deployment.

Topics

• Cloud Load Balancing
• CQRS
• Docker Containerization
• AWS Lambda
• Serverless Architecture

Best for: Software Engineer, MLOps Engineer, Data Engineer

Related on AIssential

• Presentation: Evolution of a Backend for a Streaming Application — Serverless architectures and strategic patterns enable scalable, resilient, and cost-effective multi-region streaming applications.
• Docker Kanvas Challenges Helm and Kustomize for Kubernetes Dominance — Docker Kanvas simplifies cloud-native deployments by bridging Docker Compose to Kubernetes and IaC.
• How Key value Stores Work (Redis, DynamoDB, Memcached)? — Distributed key-value stores balance scalability, fault tolerance, and data consistency through techniques like consistent hashing and eventual consistency.
• How OpenAI Scaled to 800 Million Users With Postgres — PostgreSQL can scale to massive read-heavy workloads with rigorous optimization, challenging conventional sharding wisdom.
• EP212: Data Warehouse vs Data Lake vs Data Mesh — Effective system design balances data storage, API architecture, server communication, and quality assurance for optimal performance.
• How the lakebase architecture stays resilient to cloud failures — Lakebase achieves high availability for agentic workloads through stateless compute, zone-redundant storage, and a refactored control plane.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by ByteByteGo Newsletter.