Normalization

Databases 101: From Storage to Query Optimization Databases are more than files on a disk. They organize data so apps can read and write quickly, safely, and predictably. This guide walks from how data is stored to how a database chooses a fast plan to answer a query. Storage and data models. In a relational system, data sits in tables with rows and columns. The storage engine decides how these rows are kept on disk—row-oriented pages for fast single-row access, or columnar blocks for analytics. Other models, like key-value or document stores, use different layouts but still rely on fast lookups. ...

SQL and NoSQL Data Modeling for Real World Choosing the right data model is often more important than choosing the right database. In real projects, teams balance SQL and NoSQL to meet needs like data integrity, speed, and developer velocity. This guide offers practical ideas you can use today, with simple examples you can adapt to your app. Begin with how your app reads and writes data. Ask yourself: What queries are more common? Do you need strong consistency for orders, or is eventual consistency OK for analytics? How large can the data grow, and how hot will the reads be? Answering these questions guides the model and helps you pick the right store or mix of stores. ...

Data Modeling Techniques for Modern Apps Data modeling shapes how fast apps work, how they scale, and how easily they evolve. In modern systems, teams mix different stores and patterns to fit real user needs. Start by mapping the business domain: who are the main entities, what rules govern them, and how decisions change data over time. A clear model helps with reliability, performance, and future changes. Start with a clear domain model ...

Database Design: Normalization, Indexing, and Tuning Clear database design helps data stay clean and queries stay fast. By balancing normalization, smart indexing, and thoughtful tuning, you can support growth without chaos. This guide uses plain language and small examples you can apply in many projects. Normalization keeps data in small, well defined tables. It reduces duplication and makes updates reliable. Start with 1NF, which means each column holds a single value and records do not contain repeating groups. For example, a single row should not list three product names in one column. Move to 2NF by ensuring every non‑key attribute depends on the whole primary key, so split information into related tables like Customers and Orders. Finally, 3NF removes transitive dependencies, so attributes depend only on keys (for instance, a customer region is linked via a Regions table). The result is a flexible schema that stays coherent as you add more data. ...