Data Intensive Use Cases

Identifying Data Intensive Applications

• Definition: Applications that use or generate large amounts of data, with rapid changes in data complexity and speed.
• Examples: Big websites like LinkedIn, Facebook, and Google.

Architecture of Data Intensive Applications

• Components:
  • Users: Millions can access simultaneously.
  • API Server: Acts as a bridge for user requests.
  • Traffic Layers: Include load balancing to manage incoming traffic.
  • Application Logic: Processes user requests after authentication and authorization checks.
    • Cache:
      • Quick read response if data is available.
      • If cache miss: update primary database.
      • Change capture mechanism to refresh cache.
    • Indices:
      • Full-text search helps in efficient data lookups.
    • Message Queues:
      • E.g., Kafka for handling asynchronous processes (e.g., sending emails).

Key Components Summary

• Database: Source of truth (e.g., MySQL).
• Caches: Speed up read operations (e.g., Memcache, Redis).
• Full Text Indexes: For keyword searches (e.g., Apache Lucene).
• Message Queues: For inter-process communication.
• Stream Processing: Near real-time data aggregation (e.g., Spark, Samza).
• Batch Processing: Large data processing in chunks (e.g., Hadoop, Apache Spark).
• Application Code: Main connective tissue between components.

Role of the Application Developer

• Goals: Design systems that are reliable, scalable, and maintainable.
  • Ability to serve requests from different sources (cache, database, index).
  • Handle asynchronous processes where applicable.

Key Pillars of Application Development

Reliability

• Definition: Fault tolerance from human, software, or hardware faults.
• Features:
  • Ensure expected output and authorized access.
  • Conduct chaos testing for identifying issues.
  • Hardware fault tolerance design.
  • Automate tests and staging before production.
  • Enable quick rollbacks for failures.

Scalability

• Definition: Ability of a system to scale with higher traffic and complexity.
• Models: Consider peak traffic load and simultaneous users for modeling.
• Techniques:
  • Scaling Up: Purchasing more powerful machines.
  • Scaling Out: Distributing load across multiple smaller machines.
• Monitoring: Track end-to-end response times for server and network performance.

Maintainability

• Definition: Ease of operation, testing, and evolution of the system.
• Assessing Maintainability:
  • Is the system operable and easy to monitor?
  • Is it easy to test and configure?
  • Is it evolvable and flexible to change?
• Practices:
  • Use good design patterns and documentation.
  • Regularly refactor and manage code debt.

Conclusion

• Each high-scale data-intensive application is unique, tailored to specific needs.
• Mastering components like reliability, scalability, and maintainability is crucial to building effective systems.
• The series will guide you to effectively create data-intensive applications handling millions of users at scale.