Denormalization

Database Design: Normalization and Beyond Good database design starts with normalization. It helps remove repeated data, keeps information consistent, and makes updates safer. By splitting data into related tables and linking them with keys, you reduce the chance of mistakes when values change. Two big ideas guide this work: dependencies and keys. A functional dependency shows that one set of attributes determines another. A foreign key connects records across tables, so you can join data without duplicating it. ...

Data Modelling Essentials for Relational and Nonrelational Databases Data modeling helps teams plan how information will live in a database. A good model gives you reliable behavior, fast reads, and safer updates. The same ideas apply to relational and nonrelational databases, but you design around different strengths. Relational modeling basics Relational databases rely on a structured schema. Focus on entities, attributes, and relationships. Use normalization to remove duplicates and ensure data integrity. Define primary keys for each table and foreign keys to show connections. Simple, well-structured models are easier to maintain and scale for many users and requests. ...

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. ...

NoSQL Data Modeling Patterns NoSQL databases come in different flavors, but they share a practical goal: models should fit how the application will query and update data. With NoSQL, you often trade strict normalization for fast reads, simple writes, and scalable storage. The key is to design around access patterns. Document-oriented data modeling In document stores, you decide what to embed in a document versus what to store separately. Embedding related data can speed up reads because all information is in one place. For example, a blog post document might include the title, content, author name, and a list of tags. But if the embedded array can grow without bound, or if you update the embedded data frequently, it can become costly. In that case, keep some data in separate documents and use references. ...

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 ...