Data-Consistency

Data Modeling Techniques for Modern Apps Data modeling shapes how well an app can grow, adapt, and perform. In modern systems, teams face changing requirements, multiple data sources, and the need for fast reads and reliable writes. A clear model helps engineers, product people, and customers alike. When you start, pick a primary model for core data. Relational databases give strong consistency and powerful queries. Document stores offer flexible schemas and quick reads for denormalized views. Many teams use polyglot persistence, combining models for different parts of the system to fit each use case. ...

Databases for Microservices: Patterns and Pitfalls Microservices split an application into small, independently deployable parts. Each service often owns its own data store. This reduces coupling, but it also creates new data challenges. You must think about consistency, migrations, and how to surface data to clients across services. The right choices depend on your domain, team, and tolerance for latency. Patterns to consider Database per service: Each microservice manages its own database, aligned to its boundaries. This reduces cross-service coupling but requires clear data ownership and disciplined migrations. Shared database with careful governance: A single database can simplify some reads but creates tight coupling and coordination needs between teams. Event-driven integration: Services publish events when data changes. Other services react, update read models, and keep data eventually consistent. Saga pattern: Long-running, multi-service workflows use compensating actions to handle failures, avoiding distributed transactions. CQRS and read models: Separate write and read paths let you scale reads and tailor models to each consumer. Polyglot persistence: Different databases suit different workloads. A service might use a document store for catalogs and a relational store for payments. Pitfalls to watch ...

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. ...