Staff Engineer Playbooks is a guided sequence designed to build your understanding step by step.

Staff Engineer Playbooks

A practical incident response playbook for backend engineers: how to triage production latency, error-rate spikes, database issues, queue backlogs, and rollback decisions without guessing under pressure.

Production Incident Playbooks: Debugging Latency, Errors, and Traffic Spikes

Learn how to apply Chaos Engineering to your Kafka, Cassandra, and Redis clusters. Simulate partitions, disk failures, and clock drifts to ensure true reliability.

Chaos Engineering for Data Infrastructure: Testing Distributed Resilience

Implement distributed tracing across microservices using OpenTelemetry, Jaeger, and Spring Boot. Learn trace context propagation, span correlation, and production observability patterns.

Distributed Tracing with OpenTelemetry: End-to-End Observability

Why your tests aren't enough. Prove your distributed algorithm is free from deadlocks and race conditions before you write a single line of code.

TLA+ for Backend Devs: Formally Verifying Distributed Systems

How to capture the state of an entire distributed system without stopping traffic. Deep dive into Chandy-Lamport.

Distributed Snapshots: Chandy-Lamport Algorithm

How does Redis track unique visitors in 12KB? The mathematics behind HyperLogLog and its use in real-time streaming analytics.

HyperLogLog at Scale: Billion-Cardinality Estimation

Stop your system from crashing under load. Learn how to propagate backpressure signals from the database through Kafka to the client.

Backpressure Propagation: Designing Flow Control in Microservices

Why BEGIN TRANSACTION doesn't work in microservices. Learn about the PACELC theorem and the fundamental challenges of distributed consistency.

Distributed Transactions Part 1: The Death of ACID

Consistency without distributed locks. Learn about Choreography vs. Orchestration and how to handle failures with compensating transactions.

Distributed Transactions Part 3: The Saga Pattern

Retries happen. Learn how to build idempotent consumers and producers to ensure exactly-once processing.

Distributed Transactions Part 5: The Idempotency Layer

Save 40-60% on your cloud data bill. Learn how to optimize DynamoDB, Kafka (MSK), and Redis (ElastiCache) for cost and performance.

Cloud Data Infrastructure: 5 Strategies for Cost Optimization

Master the art of real-time analytics. Learn the technical differences between Tumbling, Sliding, and Session windows in Kafka Streams and Flink.

Windowing in Stream Processing: Tumbling, Sliding, and Session Windows

How Lambda execution environments work, cold start mitigation strategies, concurrency limits and throttling, Lambda power tuning, VPC networking costs, and when Lambda is the wrong tool.

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

Resource requests and limits, liveness vs readiness probes, rolling deployments, HPA configuration, pod disruption budgets, and the mistakes that cause production outages in Kubernetes.

Kubernetes in Production: Patterns Every Backend Engineer Must Know

Production Terraform: module design, state management with S3 and DynamoDB locking, workspace strategies for multi-environment deployments, sensitive variable handling, drift detection, and the Terraform anti-patterns that cause outages.

Terraform Infrastructure as Code: Production Patterns and Pitfalls

Systematic AWS cost reduction: right-sizing EC2 and RDS instances, Savings Plans vs Reserved Instances, S3 lifecycle policies, data transfer cost elimination, EKS node optimization, RDS read replicas vs caching, and the observability stack for cost monitoring.

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

Deep comparison of Amazon ECS and EKS for container orchestration. Covers architecture differences, cost models, operational complexity, Fargate vs EC2, and migration strategies.

AWS ECS vs EKS: Choosing the Right Container Orchestration

Eliminate Lambda cold starts, tune memory for best price-performance, and architect serverless systems that handle production load. Covers Java GraalVM native, SnapStart, and Lambda Power Tuning.

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

Learn how to architect systems that handle millions of requests on AWS — covering load balancing, auto-scaling, RDS with read replicas, ElastiCache, SQS decoupling, and CloudFront CDN.

Staff Engineer Playbooks

Production-Ready

Interactive Progress

Certification

Course Curriculum

Core Lessons

Engineering Leadership

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