Grokking System Design Fundamentals
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System Design Fundamentals
Introduction to System Design
Load Balancing
Introduction to Load Balancing
Load Balancing Algorithms
Uses of Load Balancing
Load Balancer Types
Stateless vs. Stateful Load Balancing
High Availability and Fault Tolerance
Scalability and Performance
Challenges of Load Balancers
API Gateway
Introduction to API Gateway
Usage of API gateway
Advantages and disadvantages of using API gateway
Key Characteristics of Distributed Systems
Scalability
Availability
Latency and Performance
Concurrency and Coordination
Monitoring and Observability
Resilience and Error Handling
Fault Tolerance vs. High Availability
Network Essentials
HTTP vs. HTTPS
TCP vs. UDP
HTTP: 1.0 vs. 1.1 vs 2.0 vs. 3.0
URL vs. URI vs. URN
Domain Name System (DNS)
Introduction to DNS
DNS Resolution Process
DNS Load Balancing and High Availability
Caching
Introduction to Caching
Why is Caching Important?
Types of Caching
Cache Replacement Policies
Cache Invalidation
Cache Read Strategies
Cache Coherence and Consistency Models
Caching Challenges
Cache Performance Metrics
CDN
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Data Partitioning
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
Proxies
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
Redundancy and Replication
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
Introduction to CAP Theorem
Components of CAP Theorem
Trade-offs in CAP Theorem
Examples of CAP Theorem in Practice
Beyond CAP Theorem
System Design Trade-offs in Interviews
Introduction to Databases
SQL Databases
NoSQL Databases
SQL vs. NoSQL
ACID vs BASE Properties
Real-World Examples and Case Studies
SQL Normalization and Denormalization
In-Memory Database vs. On-Disk Database
Data Replication vs. Data Mirroring
Database Federation
What are Indexes?
Types of Indexes
Introduction to Bloom Filters
Benefits & Limitations of Bloom Filters
Variants and Extensions of Bloom Filters
Applications of Bloom Filters
Difference Between Long-Polling, WebSockets, and Server-Sent Events
What is Quorum?
What is Heartbeat?
What is Checksum?
Uses of Checksum
What is Leader and Follower Pattern?
What is Security and Privacy?
What is Authentication?
What is Authorization?
Authentication vs. Authorization
OAuth vs. JWT for Authentication
What is Encryption?
What are DDoS Attacks?
Introduction to Messaging System
Introduction to Kafka
Messaging patterns
Popular Messaging Queue Systems
RabbitMQ vs. Kafka vs. ActiveMQ
Scalability and Performance
What is a Distributed File System?
Architecture of a Distributed File System
Key Components of a DFS
Batch Processing vs. Stream Processing
XML vs. JSON
Synchronous vs. Asynchronous Communication
Push vs. Pull Notification Systems
Microservices vs. Serverless Architecture
Message Queues vs. Service Bus
Stateful vs. Stateless Architecture
Event-Driven vs. Polling Architecture
Quiz
Data Backup vs. Disaster Recovery

Data Backup and Disaster Recovery are critical components in an organization's data management and IT infrastructure strategy. While they are related and often used together for data protection, they serve different purposes and have distinct operational focuses.

Data Backup

Definition

  • Data Backup refers to the process of creating copies of data to be used in case the original data is lost or damaged. The primary goal is to restore data after events like deletion, corruption, or minor hardware failures.

Characteristics

  • Data Copies: Involves copying and archiving data for future restoration.
  • Frequency: Can be scheduled at regular intervals (daily, weekly, etc.).
  • Scope: Typically includes important files, databases, application data, etc.
  • Storage: Backups are stored on tapes, disks, or cloud storage.

Use Cases

  • Restoring accidentally deleted files.
  • Recovering data from corrupted databases.
  • Retrieving previous versions of data.

Example

  • An organization regularly backs up its database to a cloud storage service. An employee accidentally deletes a critical file, which is then restored from the most recent backup.

Disaster Recovery

Definition

  • Disaster Recovery is a broader strategy that includes policies, tools, and procedures for protecting and restoring an organization's IT infrastructure in the event of a major disaster, such as a natural disaster, cyberattack, or major hardware failure.

Characteristics

  • Comprehensive Planning: Includes plans for quickly re-establishing access to applications, data, and IT resources.
  • Business Continuity: Focuses on maintaining or quickly resuming mission-critical functions.
  • Infrastructure Recovery: Involves restoring entire servers, networks, and other critical infrastructure.
  • Testing and Documentation: Requires regular testing and clear documentation for effective implementation.

Use Cases

  • Reactivating IT operations after a major cyberattack like ransomware.
  • Resuming business operations at an alternate location after a natural disaster damages the primary data center.

Example

  • After a flood damages a company's primary data center, their operations are shifted to a secondary location where they can access their IT resources and maintain business continuity.

Key Differences

  1. Purpose:

    • Data Backup: Primarily focused on data preservation and restoration.
    • Disaster Recovery: A comprehensive approach to resuming business operations after catastrophic events.
  2. Scope:

    • Data Backup: Involves duplicating data.
    • Disaster Recovery: Encompasses restoring entire systems and infrastructure.
  3. Objective:

    • Data Backup: To protect against data loss and ensure data retrieval.
    • Disaster Recovery: To ensure business continuity and minimize downtime in the event of major disruptions.
  4. Scale of Incident:

    • Data Backup: Typically addresses smaller-scale data loss incidents.
    • Disaster Recovery: Deals with large-scale disruptions affecting entire IT systems.
  5. Complexity and Planning:

    • Data Backup: Relatively simpler; involves regular copying of data.
    • Disaster Recovery: More complex; requires extensive planning and testing.

Conclusion

While data backup is an essential component of disaster recovery, it is just one part of a comprehensive disaster recovery plan. Data backup is about protecting and restoring data, whereas disaster recovery is about maintaining or quickly resuming critical business operations during and after major incidents. An effective IT strategy incorporates both elements to ensure data protection and business resilience.

Replication Methods
Introduction to CAP Theorem
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On this page

Data Backup

Definition

Characteristics

Use Cases

Example

Disaster Recovery

Definition

Characteristics

Use Cases

Example

Key Differences

Conclusion