System Design Mastery: 10-Week Intensive Program

For Backend Engineers Moving from Intermediate → Advanced

Program Overview

This program transforms intermediate backend engineers into system design experts through structured, progressive learning. Each week builds on the previous, creating a comprehensive understanding of distributed systems.

Format: 1 hour daily, 5 days/week, 10 weeks Total Investment: 50 hours of focused study Prerequisites: Basic understanding of databases, APIs, and web services

How to Use This Plan

Each day includes:

Core concepts with intuitive explanations
Production-ready code implementations
Real-world case studies from top tech companies
Interview-focused practice problems
Trade-off discussions and decision frameworks

Daily Structure:

Concept Foundation: Understand the "why" before the "how"
Implementation Deep-Dive: See how concepts translate to code
Real-World Application: Learn from production systems
Interview Preparation: Practice explaining and defending designs

Document everything. Build your personal system design reference as you progress.

WEEK 0: FOUNDATIONS (Start Here)

Goal: Build the prerequisite knowledge needed for system design mastery

Before diving into system design patterns, you need a solid foundation in core concepts. Week 0 ensures everyone starts with the same mental models and vocabulary.

Week 0 Structure

Part 1: System Design Methodology

Core framework for approaching any design problem

What you'll learn:

High-Level Design (HLD) vs Low-Level Design (LLD)
The RADCE Framework: Requirements → API → Data → Core Components → Extended Features
How to structure your thinking in interviews
Common mistakes and how to avoid them

Key takeaways:

Never start designing without clarifying requirements
Always define APIs before implementation
Data model decisions constrain everything else

Part 2: Infrastructure Building Blocks

The components you'll combine into systems

What you'll learn:

Load Balancers (L4 vs L7, algorithms, health checks)
Reverse Proxies and API Gateways
Databases (SQL vs NoSQL, when to use each)
Caching layers (Redis, Memcached, CDN)
Message Queues (RabbitMQ, Kafka, SQS)
Storage systems (Block, Object, File)
DNS and how it affects system design

Key takeaways:

Each component has specific trade-offs
Choosing the right building block is half the design
Understanding internals helps you make better decisions

Part 3: Back-of-Envelope Estimation

The math that guides design decisions

What you'll learn:

Latency numbers every engineer should know
Traffic estimation (DAU → QPS → Storage)
The "Rule of 72" for capacity planning
Common estimation patterns and shortcuts
How to sanity-check your calculations

Key takeaways:

Estimation drives architecture decisions
Know your powers of 2 and time conversions
Practice until estimation becomes intuitive

Part 4: Networking Fundamentals

How data moves through your systems

What you'll learn:

OSI/TCP-IP model and practical implications
TCP vs UDP (when to use each)
HTTP/1.1 → HTTP/2 → HTTP/3 evolution
WebSockets and Server-Sent Events
gRPC and protocol buffers
TLS/SSL and security fundamentals
DNS deep-dive

Key takeaways:

Network is the foundation of distributed systems
Protocol choice affects latency, reliability, and complexity
Security must be built in, not bolted on

Part 5: Operating System Concepts

How your code actually runs

What you'll learn:

Processes vs Threads (and when to use each)
Memory management and virtual memory
I/O models (blocking, non-blocking, async, epoll)
File systems and persistence
Context switching and performance implications
Linux tuning for high-performance systems

Key takeaways:

OS concepts explain why systems behave as they do
Understanding I/O models is crucial for scalability
System limits are tunable (and often need tuning)

Part 6: Terminology and Theory

The language of distributed systems

What you'll learn:

CAP Theorem (and why it's misunderstood)
PACELC Theorem (extending CAP)
ACID vs BASE (and when to choose each)
Consistency models (linearizable → eventual)
Replication strategies (leader, multi-leader, leaderless)
Sharding and partitioning approaches
Consensus and coordination

Key takeaways:

Trade-offs are unavoidable in distributed systems
Terminology precision matters in interviews
Theory informs practice

Week 0 Summary

Part	Topic	Focus
1	Methodology	How to approach system design
2	Building Blocks	Components you'll use
3	Estimation	Math for decision-making
4	Networking	How data moves
5	Operating Systems	How code executes
6	Theory	Distributed systems concepts

Week 0 provides comprehensive coverage of all prerequisite knowledge.

PHASE 1: FOUNDATIONS OF SCALE (Weeks 1-2)

Goal: Build mental models for distributed thinking

Week 1: Data at Scale — Storage Trade-offs

Weekly Goal

Stop thinking "which database" → Start thinking "which storage pattern for which access pattern"

Key Concepts

Write-ahead logs and why every durable system uses them
LSM trees vs B-trees: When writes matter vs when reads matter
Partitioning strategies: Hash vs Range vs Directory-based
Replication: Sync vs Async and what you're actually trading
CAP theorem in practice (not theory): What does "partition" actually mean in your cloud?

Systems to Design

URL Shortener at 100M daily creates — Focus on: ID generation, hot-key problem, read vs write paths
Rate Limiter (distributed) — Focus on: Consistency vs availability, sliding window algorithms
User Session Store — Focus on: TTL strategies, consistency requirements, failover behavior

Daily Breakdown

Day 1: Partitioning Deep-Dive

Hash vs range partitioning trade-offs
Consistent hashing and virtual nodes
Designing for data locality

Day 2: Replication Trade-offs

Sync vs async replication implications
Read-after-write consistency
Handling replication lag

Day 3: Rate Limiting at Scale

Algorithm comparison (sliding window, token bucket, leaky bucket)
Distributed rate limiting challenges
Failure mode decisions

Day 4: Hot Keys and Skew

Detection and mitigation strategies
Scatter-gather patterns
Real-world examples (viral content, celebrity problem)

Day 5: Session Store Design

Sticky sessions vs distributed sessions
Failover and consistency
Technology selection (Redis Cluster, DynamoDB, custom)

Expected Outcomes

Can explain partitioning strategy trade-offs without hesitation
Know when to sacrifice consistency and when not to
Have designed 3 systems with explicit failure handling

Week 2: The Network is Not Reliable — Failure-First Design

Weekly Goal

Design systems assuming everything fails. Internalize: latency, partial failures, timeouts.

Key Concepts

Failure modes: Crash vs Omission vs Byzantine
Timeouts: Why they're hard. Cascading failures from bad timeout choices
Retry strategies: Exponential backoff, jitter, retry budgets
Circuit breakers: States, transitions, when they hurt more than help
Idempotency: Keys, deduplication windows, exactly-once is a lie

Systems to Design

Payment Processing Pipeline — Focus on: Exactly-once semantics, idempotency, reconciliation
Webhook Delivery System — Focus on: Retry strategies, dead letter queues, delivery guarantees
Distributed Cron / Job Scheduler — Focus on: Leader election, missed job handling

Daily Breakdown

Day 1: Timeout Management

Timeout budgets and propagation
Adaptive timeouts
Cascading failure prevention

Day 2: Idempotency in Practice

Idempotency key strategies
Client-generated vs server-generated keys
Deduplication windows and TTLs

Day 3: Circuit Breakers

States and transitions
Configuration tuning
When circuit breakers cause more harm

Day 4: Webhook Delivery

Delivery guarantees
Retry strategies with exponential backoff
Dead letter queue design

Day 5: Distributed Cron

Leader election patterns
Fencing tokens
Exactly-once execution strategies

Expected Outcomes

Can design systems assuming network failures from the start
Understand retry/timeout/circuit breaker trade-offs deeply
Have a production-ready idempotency strategy documented

PHASE 2: BUILDING BLOCKS OF DISTRIBUTED SYSTEMS (Weeks 3-5)

Goal: Master the components you'll compose into larger systems

Week 3: Messaging and Async Processing

Weekly Goal

Know when to use queues, when to use streams, when to use neither. Design for exactly the guarantees you need.

Key Concepts

Queue vs Log: RabbitMQ mental model vs Kafka mental model
Consumer groups, partitions, and ordering guarantees
Backpressure: Detection and handling strategies
Dead letter queues: Not a trash can, a signal
Transactional outbox pattern: Why "save to DB then publish" fails

Systems to Design

Order Processing Pipeline — Focus on: Ordering guarantees, failure handling, idempotent consumers
Event-Driven Notifications — Focus on: Fan-out, priority queues, rate limiting to external providers
Audit Log System — Focus on: Immutability, exactly-once semantics, long-term storage

Daily Breakdown

Day 1: Queue vs Stream

When RabbitMQ? When Kafka? Redis Streams? SQS?
Ordering guarantees and consumer groups
Technology selection framework

Day 2: Transactional Outbox

Why "save then publish" fails
Outbox pattern implementation
Polling vs CDC approaches

Day 3: Backpressure and Flow Control

Symptoms and detection
Response strategies
Designing for graceful degradation

Day 4: Dead Letters and Poison Pills

DLQ strategies
Debugging and replay mechanisms
Operational tooling

Day 5: Audit Log System

Immutability requirements
Compliance considerations
Storage and query patterns

Expected Outcomes

Can choose messaging system based on ordering/delivery requirements
Have implemented outbox pattern mentally (or actually in code)
Understand operational aspects of queue-based systems

Week 4: Caching — Beyond "Just Add Redis"

Weekly Goal

Cache strategically, not reflexively. Understand invalidation, consistency, and thundering herds.

Key Concepts

Cache-aside vs Read-through vs Write-through vs Write-behind
Invalidation strategies: TTL, event-driven, versioned keys
Thundering herd: Locking, probabilistic early expiration, request coalescing
Multi-tier caching: Edge → App → Database caches
Cache warming strategies

Systems to Design

Product Catalog Cache — Focus on: Consistency with inventory, invalidation strategies
User Feed Cache — Focus on: Personalization, cache-per-user vs shared cache
API Response Cache — Focus on: Varying on headers, authentication, cache busting

Daily Breakdown

Day 1: Caching Patterns

Cache-aside vs read-through vs write-through
When write-behind is safe
Pattern selection framework

Day 2: Invalidation Strategies

TTL-based vs event-driven
Why invalidation is the hardest problem
Consistency trade-offs

Day 3: Thundering Herd

Problem identification
Locking approaches
Probabilistic early expiration

Day 4: Feed Caching

Cache-per-user vs computed-on-request
Push-on-write vs pull-on-read
Celebrity/hot key problem

Day 5: Multi-Tier Caching

CDN → API Gateway → App → DB layers
What belongs where
Invalidation at each tier

Expected Outcomes

Can design multi-tier cache with clear invalidation strategy
Know how to prevent thundering herd in production
Understand when NOT to cache

Week 5: Consistency and Coordination

Weekly Goal

Understand the cost of consistency. Know when you need it and when you're paying for something you don't use.

Key Concepts

Consistency models: Strong, eventual, causal, read-your-writes
Distributed transactions: 2PC, Saga, and why they're both painful
Consensus: Raft/Paxos conceptually. When you need it vs when you think you do
Conflict resolution: Last-write-wins, vector clocks, CRDTs
Fencing and leader election in practice

Systems to Design

Inventory Management — Focus on: Preventing oversell, consistency vs performance
Money Transfer Between Accounts — Focus on: ACID across services, saga compensation
Collaborative Document Editing — Focus on: Conflict resolution, operational transforms vs CRDTs

Daily Breakdown

Day 1: Consistency Models in Practice

Strong vs eventual implications
Consistency guarantees spectrum
Choosing the right level

Day 2: Distributed Transactions — Saga Pattern

Why 2PC is avoided in microservices
Choreography vs orchestration
Compensation design

Day 3: Saga Orchestration in Detail

State machines
Workflow engines (Temporal/Cadence)
Failure handling

Day 4: Conflict Resolution

Last-write-wins problems
Vector clocks
CRDTs for specific use cases

Day 5: Leader Election and Coordination

Why leader election is hard
Fencing tokens
Split-brain prevention

Expected Outcomes

Can explain when strong consistency is worth the cost
Have designed a saga with compensation
Understand leader election failure modes

PHASE 3: COMPLETE SYSTEM DESIGNS (Weeks 6-8)

Goal: Design end-to-end systems with all components integrated

Week 6: Designing a Notification Platform

Weekly Goal

Design a complete notification system: multi-channel, reliable, scalable, observable.

Why This System

It touches everything: queues, rate limiting, external integrations, user preferences, delivery guarantees, observability.

System Requirements

Channels: Email, SMS, Push, In-app, Webhook
Scale: 10M notifications/day, bursty (marketing campaigns)
Reliability: At-least-once delivery, retry with backoff
Features: User preferences, rate limiting, scheduling, templates
Operational: Delivery tracking, debugging tools, cost monitoring

Daily Breakdown

Day 1: Core Architecture

High-level design
Component selection
Data flow design

Day 2: Queue Architecture and Flow

Queue topology
Channel isolation
Failure handling

Day 3: External Provider Integration

Provider abstraction
Fallback strategies
Health checking

Day 4: User Preferences and Rate Limiting

Preference storage
Frequency caps
Opt-out handling

Day 5: Observability and Operations

Key metrics
Debugging tools
Runbook creation

Expected Outcomes

Complete notification system design documented
Clear queue topology with isolation
Operational runbook for common issues

Week 7: Designing a Search System

Weekly Goal

Design search infrastructure for a product catalog or content platform.

Why This System

It teaches: indexing pipelines, eventual consistency, relevance tuning, performance optimization.

System Requirements

Content: 10M products/documents
Queries: 1000 QPS, p99 < 200ms
Features: Full-text search, filters, facets, autocomplete, typo tolerance
Freshness: New products searchable within 5 minutes
Operational: Index updates without downtime

Daily Breakdown

Day 1: Search Fundamentals and Architecture

Inverted index concepts
Technology selection (Elasticsearch vs Algolia vs custom)
High-level architecture

Day 2: Indexing Pipeline

Change data capture
Batch vs streaming indexing
Schema evolution

Day 3: Query Path Optimization

Query parsing and analysis
Caching strategies
Autocomplete design

Day 4: Relevance and Ranking

Scoring algorithms (TF-IDF, BM25)
Boosting and business rules
Personalization

Day 5: Resilience and Operations

Index replication
Cluster management
Degradation strategies

Expected Outcomes

Complete search system design documented
Indexing pipeline with consistency guarantees
Operational playbook for search issues

Week 8: Designing an Analytics Pipeline

Weekly Goal

Design a data pipeline from event ingestion to queryable analytics.

Why This System

It teaches: streaming vs batch, data modeling for analytics, late-arriving data, exactly-once in pipelines.

System Requirements

Ingestion: 100K events/second peak
Storage: 1 year retention, queryable
Query: Ad-hoc analytics, dashboards, real-time (< 5 min latency)
Data quality: Deduplication, late-arriving data handling
Cost: Storage and compute cost optimization

Daily Breakdown

Day 1: Event Ingestion

Schema design and versioning
Validation and rejection strategies
Backwards compatibility

Day 2: Streaming vs Batch

Lambda vs Kappa architecture
When streaming, when batch
Technology selection

Day 3: Storage and Data Modeling

Columnar storage
Partitioning strategies
Time-series patterns

Day 4: Late-Arriving Data and Correctness

Event time vs processing time
Watermarks and allowed lateness
Correction strategies

Day 5: Query Layer and Cost

OLAP databases
Materialized views
Cost optimization

Expected Outcomes

Complete analytics pipeline documented
Clear strategy for streaming + batch
Cost-aware storage and query design

PHASE 4: ADVANCED PATTERNS AND REAL-WORLD COMPLEXITY (Weeks 9-10)

Goal: Handle the messy realities of production systems

Week 9: Multi-Tenancy, Security, and Compliance

Weekly Goal

Design systems that serve multiple customers with isolation, security, and compliance requirements.

Key Concepts

Tenant isolation: Shared vs siloed. Database, compute, network isolation
Noisy neighbor: Detection and prevention
Data residency: GDPR, data localization requirements
Encryption: At rest, in transit, application-level
Audit and compliance: Logging, access control, data retention

Systems to Design

Multi-tenant SaaS Backend — Focus on: Tenant isolation strategies
GDPR-Compliant Data Architecture — Focus on: Right to deletion, data export, consent management

Daily Breakdown

Day 1: Tenant Isolation Strategies

Shared DB vs schema-per-tenant vs DB-per-tenant
Cost vs isolation trade-offs
Row-level security

Day 2: Noisy Neighbor Prevention

Resource quotas
Rate limiting per tenant
Fair scheduling

Day 3: Data Residency and GDPR

Data localization requirements
Cross-border transfers
Architecture implications

Day 4: Right to Deletion

GDPR Article 17 requirements
Deletion across systems
Audit trails

Day 5: Security Architecture

Defense in depth
Zero trust principles
PII protection

Expected Outcomes

Multi-tenant architecture with clear isolation
GDPR-compliant data handling design
Security architecture documented

Week 10: Production Readiness and Operational Excellence

Weekly Goal

Design systems that are operable, debuggable, and evolvable.

Key Concepts

SLIs, SLOs, SLAs: Defining and measuring reliability
Observability: Metrics, logs, traces — and how they connect
Deployment strategies: Blue-green, canary, feature flags
Capacity planning: Load testing, bottleneck identification
Incident management: On-call, runbooks, postmortems

Activities

This week focuses on operationalizing previous designs.

Daily Breakdown

Day 1: Defining SLOs

SLI vs SLO vs SLA
Error budgets
Measurement precision

Day 2: Observability Design

Three pillars (metrics, logs, traces)
Correlation IDs
Distributed tracing

Day 3: Deployment and Rollback

Canary deployments
Feature flags
Database migration strategies

Day 4: Capacity Planning

Load testing methodology
Bottleneck identification
Scaling decisions

Day 5: Incident Management

On-call best practices
Runbook structure
Blameless postmortems

Expected Outcomes

SLOs defined for all major systems
Observability strategy documented
Deployment and rollback procedures written
Incident response playbooks ready

Program Summary

Weekly Overview

Week	Theme	Focus
0	Foundations	Prerequisites and methodology
1	Data at Scale	Storage patterns and trade-offs
2	Failure-First Design	Reliability patterns
3	Messaging	Async processing patterns
4	Caching	Performance optimization
5	Consistency	Coordination and transactions
6	Notification System	Complete design exercise
7	Search System	Complete design exercise
8	Analytics Pipeline	Complete design exercise
9	Multi-tenancy	Security and compliance
10	Operations	Production readiness

Recommended Resources

Books (skim relevant chapters, don't read cover-to-cover):

"Designing Data-Intensive Applications" — Chapters on replication, partitioning, consistency
"System Design Interview Vol 1 & 2" — Use as problem prompts, not solutions
"Site Reliability Engineering" — Operations and SLO chapters

Engineering Blogs (read when relevant to weekly topic):

Uber, Stripe, Netflix, LinkedIn engineering blogs
Specific posts on systems you're designing that week

Tools to Know:

Draw.io or Excalidraw for diagramming
Keep a shared doc for all your designs

Success Metrics

After completing this program, you should be able to:

Design any backend system from scratch in 45 minutes with clear trade-offs
Critique designs by injecting failures and scale challenges
Speak precisely about consistency, availability, and partition tolerance
Produce documentation that a team could implement from
Operate systems — know what metrics to watch, how to debug, when to page

Final Note

This program is intense. You'll feel uncomfortable. That's the point.

The goal isn't to memorize solutions — it's to build intuition for trade-offs.

After 10 weeks, when someone asks "how would you design X?", your first instinct should be: "What are the constraints? What can we sacrifice?"

That's senior engineering thinking.

Good luck. Build something great.

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