Different Database Schemas

Solutions Review’s Tim King created this introductory resource to highlight the different database schemas for your reference.

Enterprise database schemas define the structure and organization of data within a database system. There are several common types of database schemas used in enterprise applications:

1. Relational Schema (Table-Based Schema):
   • The relational schema represents data in tables (relations) consisting of rows (tuples) and columns (attributes).
   • Each table has a name and a set of columns with specific data types.
   • Relationships between tables are established through keys, such as primary keys and foreign keys.
   • Common types of relational schemas include:
     • Star Schema: Often used in data warehousing, it consists of a central fact table surrounded by dimension tables. This design simplifies complex queries for business intelligence and reporting.
     • Snowflake Schema: An extension of the star schema where dimension tables are further normalized into sub-dimensions, reducing data redundancy.
2. Hierarchical Schema:
   • In a hierarchical schema, data is organized in a tree-like structure with parent-child relationships.
   • Each parent node can have multiple child nodes, but each child node has only one parent.
   • This schema is suitable for representing hierarchical data like organizational structures.
3. Network Schema (CODASYL Schema):
   • The network schema is an extension of the hierarchical schema.
   • It allows each child node to have multiple parent nodes, which results in a more complex and interconnected structure.
   • This schema is useful for representing complex relationships and is associated with older database systems like CODASYL databases.
4. Object-Oriented Schema:
   • Object-oriented schemas represent data as objects, similar to how they are represented in programming languages.
   • Objects have attributes (data) and methods (functions), allowing for the modeling of real-world entities and their behaviors.
   • This schema is well-suited for object-oriented database systems and object-relational mapping (ORM) in relational databases.
5. XML Schema (Document Schema):
   • XML schemas define the structure and rules for XML documents.
   • They specify the elements, attributes, and their relationships within an XML document.
   • XML schemas are commonly used in document databases and applications involving XML data.
6. JSON Schema:
   • JSON schemas define the structure and constraints of JSON documents.
   • They specify the allowed properties, their data types, and validation rules for JSON data.
   • JSON schemas are used in document databases and NoSQL databases that store JSON data.
7. Key-Value Schema:
   • Key-value stores have a simple schema consisting of keys and associated values.
   • Each key is a unique identifier for a piece of data, and the value can be any data type.
   • This schema is straightforward and efficient for quick data retrieval and is often used in distributed and NoSQL databases.
8. Column-Family Schema:
   • Column-family databases organize data into column families, which are containers for columns with similar characteristics.
   • Each row can have a different set of columns within a column family, allowing for flexible schema design.
   • This schema is commonly used in distributed databases and big data systems.
9. Graph Schema:
   • The graph schema represents data as nodes and edges, defining node types and edge types along with their properties.
   • Nodes represent entities, and edges represent relationships between entities.
   • This schema is ideal for applications that require modeling complex relationships and graph-like data structures.

The choice of a database schema depends on the type of data being managed, the requirements of the application, and the database management system being used. In some cases, a hybrid schema approach may be employed to meet various data modeling needs within an enterprise database system.