What is database schema?
A database schema is the skeleton structure that represents the logical view of the entire database. It decides how information is stored and how the relations among them are formed in a database.
A proper schema makes sure your business meets the objectives of projects without losing direction. Since schema also represents the relationship among tables, different databases have different schema designs to support varying business requirements.
To fully understand database schema meaning, you should know it includes the following:
- All relevant or important data.
- Consistent formats for all data entries.
- Unique keys for all database objects and entries.
- Every column in a table has its name and a data type.
The size and sophistication of your database schema is based on the size of your project. The visual design of a database schema enables programmers to structure the database and its relationships properly before coding. And the process of planning a database design is called data modeling.
- More detail on Database vs Data Warehouse
Benefits of database schema
Database schemas are important in many ways. Let’s look at some main database schema benefits:
- Organize data better and provide a source of truth.
- Promote better communication within stakeholders thanks to better organization and documentation of data.
- Control access via permission of database for admin. This helps to increase the security level.
- Avoid data duplication because admin can manage the normalization process.
- Allow users to understand the logical constraints, so users can monitor compliance in the schema database design.
Besides, schema of database tells the database what your data is and how to work with it. This way, database schemas help the database engine understand these patterns, which enables it to implement constraints on the data, fetch information when directed, and manipulate it per how users request.
Good schemas tend to reduce implied information to make it visible to the system and its users. Schemas in relational databases can ensure data consistency, minimize information redundancy, and build structures for joining related data. Good schemas align the storage format with the access patterns needed for your application. This way, they enable high performance and scalability.
Types of database schema
Logical schema, physical schema, and view schema
When designing database schemas, we need to keep in mind two terms that are usually confusing. They are logical and physical schema.
1. Logical schema
When designing database schemas, this type is a general design for categorizing data into different groups, defining properties of the data, and deciding the best structure. It can be considered as a blueprint and implemented in many database systems.
When it comes to database architecture, logical schema refers to a visible database entity that users interact with. This means objects like keys, tables, views, and indexes are abstractions that users build up and manage using the database software. The layout is a part of the logical schema that the database presents.
2. Physical schema
When designing database schemas, we regard a physical schema as the next step in the design process where implementation-specific details are completed. In this step, names of entities, keys, indexes, constraints, and others are defined and listed out on the logical schema. Through this, a specific plan for implementation is provided by using an available database platform.
And when it is about database architecture, physical schema is how the database software tackles the data, files, and storage when interacting with the filesystem. For instance, the physical schema of the database architecture can decide whether the system stores an individual file for each database or each table and defines how those can be partitioned in different servers.
3. View Schema
View schema is the view level design in a database. This schema type reflects the interaction of users with the database.
Static schema vs dynamic schemas
There is another way of categorizing schemas to help clarify the differences between schema in relational and non-relational databases. Here are two database schema types.
1. Static schema
Static schemas are usually associated with relational databases. They are defined beforehand as a definition of the shape that data must follow if it wants to be accepted by the system. The database system can enforce these patterns when using static schema because static schema is an assertion of the desired state that the database system can validate input against.
2. Dynamic schema
By contrast, dynamic schemas are better known in non-relational contexts. They are less rigid and may lack any predetermined organizational structure. Instead, dynamic schemas show up based on the qualities of the data that is input into the system. While many non-relational databases can store information using an arbitrary internal structure, regular patterns tend to appear in most real-world use cases.
As dynamic schemas are emergent structures, they cannot be used as a conformance tool by the database system. However, it is still vital to understand and develop around as a user. Understanding how your data will look in a general sense and how your applications will need to interact with it will help you choose structures that meet your needs, perform well, and avoid unnecessary inconsistency.
Database schema vs database instance
| Database schema | Database instance | |
|---|---|---|
| Definition | A database schema is the “blueprint” of a database. It describes the organization and relationship between data. | A database instance is the snapshot of the database at any given time. |
| Contain | Does not contain any data or information. | Contains data values. |
| Change | Does not change with time. | Change over time. |
Database schema integration requirements
If you need to integrate multiple sources into a single schema, please ensure the following requirements:
| Overlap preservation | You need to maintain the database table format for every schema overlap element when you are integrating. |
| Extended overlap preservation | If an element is associated with overlap elements even though it only appears in one source, you need to copy it to the database schema. |
| Normalization | You should not connect independent relationships and entities in the same table in the database schema. |
| Minimality | You should ensure that all elements of the sources are present. |
In case you do not exactly understand what is going on and how to choose the most suitable one for your business, we got you covered! Synodus experts could lend you a hand and check up on your business to determine and consult you about which way is the best to build your own schema
Final words
Data schemas are essential as they tell the database what your data is and make all information visible and clear to the system and its users. Hope that this post has clarified enough information about what database schemas are and their types so that you can effectively design database schemas for your business.
