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AWS Guide to Cloud Architecture Diagrams (Sponsored)
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Enhance visibility into your cloud architecture with expert insights from AWS + Datadog. In this ebook, AWS Solutions Architects Jason Mimick and James Wenzel guide you through best practices for creating professional and impactful diagrams.
This week’s system design refresher:
Rate Limiter System Design: Token Bucket, Leaky Bucket, Scaling (Youtube video)
Docker vs Kubernetes
Batch vs Stream Processing
What are Modular Monoliths?
What is the difference between Process and Thread?
How AI Agents Chain Tools, Memory, and Reasoning?
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Rate Limiter System Design: Token Bucket, Leaky Bucket, Scaling
Docker vs Kubernetes
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Docker is a container platform that lets you package applications with their dependencies and run them on a single machine. Here’s how it works:
Starts with the app code and dependencies written into a Dockerfile.
The build image step creates a portable container image.
A container runtime runs the image directly on the host machine.
Networking connects containers and external services and produces the final running app.
Kubernetes is a container orchestration platform that manages containerized applications across a cluster of machines for scalability and resilience. Here’s how it works:
Starts with the app code and dependencies in a Dockerfile.
The build image is passed to a container runtime supported by Kubernetes.
A master node runs the API server, etcd (key-value store), controller manager, and scheduler to coordinate the cluster.
Worker nodes run the actual containers inside the Pods, managed by Kubelet and kube-proxy for networking.
Produces a running app that is distributed, scalable, and self-healing in nature.
Over to you: Have you used Docker or Kubernetes in your projects?
Batch vs Stream Processing
Data never stops flowing, but how we process it makes all the difference. When building data systems, two main approaches emerge: batch processing and stream processing. Both have their place, depending on whether you need accuracy over time or immediate insights.
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Batch Processing: Collects data in chunks and processes it at scheduled intervals, great for reports and historical analysis.
Stream Processing: Processes events continuously in real-time, powering dashboards, alerts, and recommendations.
Batch = high-volume, historical accuracy.
Stream = low-latency, real-time action.
Over to you: Which do you find harder to scale, batch pipelines or real-time streams?
What are Modular Monoliths?
A modular monolith is an architectural pattern that divides an application into independent modules or components. Each of these modules has well-defined boundaries that group related functionalities. Doing so results in better cohesion.