CAP Theorem

Databases 101: From Relational to NoSQL Databases help apps store and retrieve information. Two large families shape many choices today: relational databases and NoSQL systems. Relational databases organize data into tables with rows and columns. They use SQL for queries and enforce rules that keep data clean. NoSQL covers several families that trade some rigidity for flexibility and speed. The result is a practical mix: strong structure for some parts, and flexible storage for others. ...

Databases Demystified: From SQL to NoSQL Databases come in many shapes. SQL and NoSQL are not enemies; they fit different tasks. SQL databases organize data in tables with rows and columns. The schema is defined in advance, and the system checks rules to keep data clean. Transactions try to keep all parts of a change correct, even in busy apps. NoSQL databases arrive with a different idea. They scale more easily across many machines and handle flexible data. They often trade strict consistency for speed and availability. This makes them useful for content storage, logs, or user sessions where fast writes matter. ...

Database Design for Scalable Applications Databases are the backbone of modern apps. As traffic grows, slow queries or data bottlenecks break user trust. The goal is to keep data consistent, fast, and easy to evolve. Start with clear goals for reads and writes. Identify the most common queries and how data is accessed across services. This helps decide storage technology and data models that fit the workload. Choosing the right storage approach Relational databases shine with structured data and strong consistency. They work well for financial records, inventories, and user profiles. NoSQL databases offer flexible schemas and fast writes at scale, useful for logs, sessions, and catalogs. In practice, many apps use a mix: a transactional store for critical data plus a fast access layer for reads. ...

Distributed Databases: Consistency, Latency, and Availability Distributed databases store data across multiple machines and locations. This design helps scale, stay resilient, and serve users quickly. But it also creates a classic trade-off among consistency, latency, and availability, a trio often summarized by the CAP idea. In practice, teams pick a balance based on user needs and failure scenarios. Consistency models guide how up-to-date data must be. Strong consistency makes every read show the latest write. It is easier to reason about, but it can add latency if writes must reach a majority of replicas. Eventual consistency allows faster reads and writes and can survive partitions, but reads may see older data for a while. Causal consistency is a middle ground: operations that depend on each other stay ordered, while unrelated actions may be stale. ...