Document Stores

Databases in the Real World: From SQL to NoSQL Databases decide how your app stores and retrieves data. In practice, developers choose between SQL databases, which enforce a strict, structured model, and NoSQL databases, which are more flexible and scalable for modern apps. SQL databases, such as PostgreSQL or MySQL, store data in tables with rows and columns. They shine when data has clear relationships, and when you need reliable transactions, filters, and reports. If your app tracks customers, orders, and inventory with joins, SQL often fits best. ...

Databases Demystified: From SQL to NoSQL Databases help apps store and fetch data. Two big families exist: SQL databases use tables, rows, and fixed schemas. NoSQL databases offer documents, key-value pairs, and other flexible formats. Both aim to support fast, reliable data access, but they solve problems in different ways. Understanding SQL and relational models SQL databases use a relational model. Tables hold data in rows and columns. Relationships are shown with keys, and queries pull exact results. They often enforce ACID rules to keep data correct, even during concurrent use. If your data is well defined and relationships are important, SQL stays strong and predictable. ...

Databases in the Real World: From SQL to NoSQL In the real world, the database you choose shapes performance, cost, and even how your team works. Two broad families stand out: SQL databases that enforce structure and transactions, and NoSQL options that adapt as data grows. The right pick depends on current needs and future plans. Start by mapping data shapes, access patterns, and constraints, then choose what fits best. ...

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. ...