Part 1: Top 5 Considerations Designing Data-Intensive Applications

‘Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems’ – Martin Kleppman, details some of the key points initially into what considerations that can be taken when designing data-intensive applications. This post will be in multiple parts on some of the top considerations to make as of this writing.

1. “Big Data” not a useful terminology

As from Martin Kleppman’s analysis on today’s buzzword “Big Data”, he highlights that the term is too abstract for meaningful conversation. Instead, the focus should be on distinct components like capturing, analyzing, and storing data. Learn more about data capturing and analysis strategies.

2. Premature system optimisation

Avoid overengineering. As Kleppman puts it, “you’re not Google.” Optimize only when needed, allowing future extensibility instead of premature optimization. Explore best practices for system design.

3. Storage Engines

Choosing the right storage engine is vital. Faster reads, scalability, and high availability all depend on the engine. Consider researching options. Here’s a helpful guide to SQL vs NoSQL databases.

4. Reliable, scalable, and maintainable applications

Reliability, scalability, and maintainability should be at the core of your application’s architecture. These principles govern its long-term success.

5. Storing results of expensive operations

Known as memoization, storing results of expensive function calls improves performance. This technique helps alleviate database read demands.

📚 Further Reading & Related Topics

If you’re exploring designing data-intensive applications, these related articles will provide deeper insights:

• Distributed Data-Intensive Systems: Logical Log Replication – Learn how logical logging ensures data consistency and reliability in distributed architectures.

• What Are Monotonic Reads? – Understand how consistency models impact the design of scalable, data-intensive systems.

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