Databases
Choosing the right database architecture is critical to application performance, scalability, and maintainability. We work with various database paradigms, each suited to different use cases.
Choosing a Database
| Type | Best For | Examples |
|---|---|---|
| RDBMS | Structured data, transactions | PostgreSQL, MySQL |
| Schemaless | Flexible schemas, documents | MongoDB, DynamoDB |
| Column-store | Analytics, aggregations | Redshift, BigQuery |
📄️ Column-Store Databases
Column-store (or columnar) databases store data by column rather than by row. This fundamental difference makes them exceptionally efficient for analytical workloads where queries typically access a subset of columns across many rows.
📄️ RDBMS
A Relational Database Management System (RDBMS) organises data into tables with rows and columns, enforcing relationships between tables through foreign keys. It's the most widely used database paradigm and the foundation of most business applications.
📄️ Schemaless Databases
Schemaless (or schema-flexible) databases don't require a predefined structure for data. Unlike RDBMS, you can store documents with varying fields without altering a schema first.