Schema Evolution

Database Design Patterns for Reliability Reliability in a database means you can trust the data and recover from failures quickly. Good design reduces data loss, avoids inconsistent reads, and keeps services available during problems. A practical approach blends patterns for data structure, operations, and recovery. Event logs and event sourcing Store changes as an append-only stream. The current state is rebuilt by replaying events in order. This pattern gives a clear audit trail and makes recovery straightforward. For example, orders move from OrderPlaced to PaymentCompleted, then OrderShipped, all as events with timestamps and IDs. If a crash happens, replaying events brings the system back to the last known state. ...

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. ...