# aws

Cloud Data Infrastructure: 5 Strategies for Cost Optimization

Cloud Data Infrastructure: Cutting the Bill Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. Building high-performance data infrastructure on AWS, Azure, or GCP is ea…

Apr 20, 20267 min read

#aws#databases#messaging

Cloud-Native Databases: Why the Log is the Database

Cloud-Native Databases: The Log is the Database Mental Model > The source of truth where data persistence, consistency, and retrieval speed must be balanced. Traditional databases (PostgreSQL, MySQL) were designed to run…

System DesignIntermediate

Consistent Hashing: The Secret Sauce of Distributed Scalability

Consistent Hashing: Scaling Without the Chaos Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. In a distributed system, you need a way to map data keys to specific…

Deep DiveDistributed Systems Fundamentals

Deep DiveMANG Interview Problem Bank

DSAEasy

The 'Small Files' Problem in Data Lakes: Why Your Kafka Sink is Slow

The 'Small Files' Problem: The Data Lake Killer Mental Model > Breaking down a complex problem into its most efficient algorithmic primitive. Streaming data from Kafka into a Data Lake (like Amazon S3 or Azure Blob Stora…

Apr 20, 20267 min read

#aws#data-engineering#dsa

DynamoDB Pitfalls: Throttling, Hot Partitions, and the 400KB Limit

DynamoDB Pitfalls: Mastering the Serverless Scale Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. DynamoDB is a powerful, fully managed NoSQL database, but its se…

Deep DiveDatabase Engineering Mastery

#aws#databases#java

DynamoDB Single Table Design: Advanced Modeling Patterns

DynamoDB Single Table Design: The Senior Engineer's Guide Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. Single Table Design is often considered the "Holy Grail"…

PlaybookDatabase Engineering Mastery

Multi-Region DR: Warm Standby vs Active-Active

Multi-Region Disaster Recovery (DR) Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. If a complete AWS region goes down, your system must keep running. Designing f…

Case StudyReliability Engineering Mastery

#aws#system-design

S3 Express One Zone: When to Use it for Stateful Workloads

S3 Express One Zone Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. Amazon S3 Express One Zone stores data in a single AZ, reducing network hops and latency. It's no…

Apr 20, 20265 min read

#ai-ml#aws#data-engineering

System DesignIntermediate

Kubernetes Networking: What Happens Between the Load Balancer and Your Pod?

Kubernetes Networking for Backend Developers Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. As a backend engineer, you usually stop thinking about a request once…

Apr 20, 20266 min read

#aws#devops#system-design

S3 Express One Zone: When to use it

S3 Express One Zone Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. For stateful data processing (like Spark shuffle files), standard S3 latency is too high. S3 Expr…

Apr 20, 20265 min read

#ai-ml#aws#java

System Design: Designing Google Drive (Distributed File Storage)

Case Study: Design a File Storage System (Google Drive) Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. Designing a file storage system like Google Drive or Dropb…

Apr 20, 20265 min read

System Design: Designing an Object Store (Amazon S3 Internals)

System Design: Designing an Object Store (Amazon S3) Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. An Object Store is a distributed storage system designed to s…

Case StudySystem Design Interview Mastery

Terraform for Backend Engineers: Managing Your Own Infra

Terraform for Backend Engineers Mental Model > Connecting isolated components into a resilient, scalable, and observable distributed web. In modern engineering teams, the boundary between "Code" and "Infra" is blurring.…

Apr 20, 20266 min read

#aws#devops#system-design

AI Infrastructure on AWS: SageMaker, EKS GPU Scheduling, and Cost-Efficient Inference

Running AI workloads in production is a different discipline from running web services. The hardware is expensive, the failure modes are opaque, and the cost curve compounds fast. After operating inference infrastructure…

Apr 8, 202615 min read

#ai-ml#aws#performance

Kubernetes for AI Inference: GPUs, Autoscaling, Queues, and Cost Control

Kubernetes can run AI inference workloads well, but only if you treat GPU capacity as a scarce production resource instead of "just another Deployment." GPU nodes are expensive, images are large, cold starts are painful,…

Apr 8, 202612 min read

#ai-ml#aws#devops

LLM Inference Optimization: Quantization, KV Cache, and High-Throughput Serving

The Real Cost of Running LLMs Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. A 70B-parameter model in fp16 requires roughly 140 GB of GPU VRAM just for weights. Tha…

Apr 8, 202614 min read

#ai-ml#aws

System Design: Building a File Upload Platform

File upload looks easy until production traffic arrives. Users upload huge files, mobile networks drop halfway through, browsers retry, malicious files appear, thumbnails fail, metadata gets out of sync, signed URLs expi…

Apr 8, 202611 min read

#ai-ml#aws#security

Multi-Region Architecture: Active-Active, Active-Passive, and Consistency Trade-Offs

Multi-region architecture is expensive insurance. It can improve availability and latency, but it also makes data consistency, deployment, observability, and operations much harder. Before designing multi-region, ask two…

Apr 7, 20267 min read

#aws#system-design

System Design: Building a Feature Store for Real-Time Machine Learning

Production machine learning systems fail when the model sees different data in training than it sees in production. A feature store helps solve that problem. It provides a consistent way to define, compute, store, and se…

Apr 7, 20265 min read

AWS Lambda in Production: Cold Starts, Concurrency, and Cost Optimization

Lambda's value proposition is compelling: run code without managing servers, pay per invocation, scale from zero to 10,000 concurrent executions without configuration. The reality is a set of execution model nuances that…

Jun 28, 20258 min read

#aws#java#performance

Deep DiveDatabase Engineering Mastery

DynamoDB Advanced Patterns: Single-Table Design and Beyond

DynamoDB is a fully managed key-value and document database that delivers single-digit millisecond performance at any scale. It achieves this with a fundamental constraint: you must define your access patterns before you…

Jun 13, 202511 min read

PlaybookStaff Engineer Playbooks

Kubernetes in Production: Patterns Every Backend Engineer Must Know

Running a container in Kubernetes and running a production workload in Kubernetes are different disciplines. The gap between kubectl apply -f deployment.yaml and a service that survives node failures, deployment rollouts…

Jun 8, 20257 min read

#ai-ml#aws#devops

Terraform Infrastructure as Code: Production Patterns and Pitfalls

Terraform is the industry-standard tool for Infrastructure as Code (IaC) — defining cloud infrastructure as declarative HCL configuration that can be version-controlled, reviewed, and applied reproducibly. The value prop…

May 14, 20258 min read

#aws#devops

Cloud Cost Optimization: Engineering Practices That Cut AWS Bills by 50%

Cloud bills scale with usage — but they also scale with inattention. Most teams that haven't deliberately optimized their AWS spend are 30-50% over what they need to pay for the same workload. The savings come from a pre…

Apr 29, 20257 min read

Deep DiveMessaging & Event-Driven Mastery

#aws#performance

MessagingIntermediate

SQS vs Kafka vs EventBridge: Choosing the Right Messaging System on AWS

Every AWS backend team eventually faces the same decision: you need asynchronous messaging. SQS is right there in the console. Your architect says you need Kafka. Someone from DevOps mentions EventBridge. Each option has…

Apr 2, 202512 min read

#aws#messaging

ComparisonStaff Engineer Playbooks

AWS ECS vs EKS: Choosing the Right Container Orchestration

Every team containerizing their workloads on AWS faces the same choice: ECS or EKS? ECS is simpler and tightly integrated with AWS. EKS is Kubernetes — portable, powerful, and complex. Getting this choice wrong means yea…

Mar 25, 202510 min read

#ai-ml#aws#devops

System Design: Video Streaming Platform at Netflix Scale

Netflix accounts for 15% of global internet traffic. At peak, it serves 250 million concurrent streams — each stream adapting in real-time to network conditions, each segment served from servers 20ms away from the viewer…

Mar 19, 202513 min read

#aws#messaging#system-design

AWS Lambda: Cold Starts, Memory Tuning, and Cost Optimization

Lambda functions are the easiest compute to get started with and the hardest to tune well. A 2-second cold start is a dealbreaker for a payment API. A 512MB function that runs in 1 second might be cheaper than a 128MB fu…

Mar 7, 202513 min read

#aws#java#performance

AWS Architecture Patterns for High-Traffic Applications

Architecting on AWS is not about using every service in the catalog — it's about choosing the right services for your scale, stitching them together correctly, and understanding the failure modes of each. This article wa…

Jan 19, 202515 min read