What is a Database Schema? Definition, Benefits, Types, and Requirements

Data management is crucial in today’s world to make informed decisions, leverage information for insights, and meet regulatory obligations. An effective data management strategy starts with a well-constructed database, and a well-constructed database depends on a quality database schema. Before your database contains any data, you should understand the importance of database schemas as the foundational element for organizing data.

What is a database schema?

Every database contains not just data, but also a schema—the information about the data. The database schema is a structure that defines the database, including the definition of the tables, data types, and fields (or columns). The schema also defines the relationship between different tables, the primary and foreign keys, and indexes. Database schemas can also extend the base tabular structure of databases with more programmatic elements like stored procedures and functions.

The language used to define a schema depends on the database management system (DBMS). The most widely used definition language for schema is SQL. For example, in MySQL, PostgreSQL, and Oracle, the command CREATE SCHEMA statement is used to create a new schema. While the specifics may vary, each DBMS contains similar schema elements.

First, the schema defines tables. Tables are units that store data in a structured format within the database. Data in an Excel spreadsheet is a graphical depiction of a table. Each table contains columns of data. Columns define the attributes of the data stored in the table. You would then specify the data types for each column, such as integer, string, and date data types. You would also identify the primary key of the table. The primary key is a unique identifier for each record in the table, which allows for data integrity and efficient data operations.

Finally, you would define the relationships between the tables. Relational databases contain multiple interrelated tables, and you need the ability to reference different tables. For example, an orders table would need to be able to reference a product table for product details and a customer table for customer details. Relational database tables contain foreign keys for locating information in a separate but related table. For example, the orders table has foreign keys containing product numbers and foreign keys containing customer numbers.

The difference between a database schema and a database instance

The database schema contains the database’s structure before it contains actual data. In other words, the schema is the “blueprint” for the data to come. The database instance, on the other hand, is a specific instance of a database running on a server, a “snapshot” of the data at a given moment in time. It includes the structures and processes necessary to interact with the data. You query and manage data in the database instance.

8 Key benefits of a database schema

There are many benefits of developing a detailed database schema, including the following.

1. Data organization and structure: A database that adheres to a schema is structured and predictable, making it easy to understand and manage the data.
2. Data relational integrity: Schemas define the relationships between tables (one-to-one, one-to-many, and many-to-many relationships). Database schemas also define the foreign key relationships between tables, keeping the relationships consistent.
3. Data integrity and validation: Tables defined with constraints on data types ensure data integrity. For example, a date column in a table must contain dates. Random data, such as a string, is not accepted.
4. Efficient data retrieval and querying: Data that conforms to a schema is faster to retrieve by querying. The schema can also include indexes that significantly improve performance.
5. Scalability and maintenance: Databases designed with a clear schema are easier to scale. You can add or modify a table without affecting the entire database. They are also easier to maintain because developers know they must adhere to the blueprint. There is less likely to be data corruption, since the schema enforces rules for data entry.
6. Facilitating data sharing and collaboration: A schema allows for better collaboration with other team members, such as database administrators and developers. With a well-defined schema, you can share only the part of the database that is needed.
7. Security: A well-planned schema with interconnected tables allows administrators to enable granular access control at the table, even at the column level.
8. Ease of backup and recovery: A well-structured schema is easier to back up and restore. Changes in a database with good schema are usually in specific tables, making it easier to perform incremental backups. In the event of a partial data loss, a well-structured schema makes it easier to identify and recover the lost data without affecting the integrity of the remaining data.

3 Types of database schemas

A database schema, as designed by the DBMS, is comprised of a conceptual schema, logical schema, and a physical database schema. These three types are described below.

1. Conceptual schema: A conceptual schema is an abstract model of the database from a high level, showing the main structure and the types of data that need to be stored. At the conceptual level, the specific type of database is not important. A conceptual schema usually consists of a diagram outlining the database structure.
2. Logical schema: With a logical schema, you now need to know the type of database you will be using so you can create a more detailed schema, including specific tables, columns, primary and foreign keys, and data types. A logical schema introduces constraints to apply to the data.
3. Physical schema: The physical schema is the actual storage of data on the physical storage devices. Typically, the physical schema is not defined by users but rather by the engineers of the database (e.g. Microsoft, Oracle). Physical schemas focus on optimizing performance and storage.

4 Database schema model designs

There are many different types of schema models, but the four described below are the most used.

1. Flat model: This is the simplest type of database schema. It is a simple table, similar to the data you would find in an Excel spreadsheet. It is easy to use and share as a CSV file, but it can lead to data redundancy.
2. Relational model: This type of schema is used in object-oriented programming. The schema is broken down into separate tables for each object. Each table contains object attributes that can relate to multiple tables. Unlike the star and snowflake schemas described below, there is no central fact table in the relational model.
3. Star schema: This popular schema is for storing large amounts of data in a data warehouse. The star schema consists of a central fact table linked to multiple dimension tables, forming a star pattern. The fact table usually has numerical values that link to the dimension tables. The star schema makes data analysis easier since the data is organized in distinct tables. For example, in retail, you would have a sales table containing foreign keys linking to a customer table, a product table, and a store table.
4. Snowflake schema: This type of schema is also used in data warehousing and allows for complex queries and analytics. The snowflake schema also contains a central fact table. However, the dimension tables are further broken down into child tables arranged in a hierarchical structure. This is a more complex structure than the star schema, but it reduces data redundancy and improves data integrity.

Database schema integration requirements

In a large enterprise, you may come across the situation of integrating data from multiple data sources. These data sources potentially contain different schemas.

The following are requirements for successfully integrating different database schema without losing data integrity. The result of data integration is combined data that is preserved without duplication or loss.

• Overlap and extended overlap preservation: When you integrate data from multiple schemas, every overlapping element from each schema must be in a database schema table. This is called overlap preservation. Sometimes when you integrate data, some elements appear in only one schema but are related to the overlapping elements from other schemas. This is extended overlap preservation.

  To help achieve overlap and extended overlap preservation, you should identify common elements in each schema, such as tables, columns, and data constraints. You should also resolve any naming conflicts and data type mismatches between the schemas.

• Elements and relationships normalization: Normalization is an important part of integrating database schemas. Normalizing data involves removing redundancies, standardizing data types, defining clear foreign key relationships between tables, and splitting up composite attributes (such as composite first name/last name fields).
• Element preservation of normality: You want to maintain the normalized state of individual schema elements during and after the integration process. That means preventing the introduction of redundant data or relationships, maintaining consistency of data relationships and data types, and preserving relationships between elements.

Dynamic schema management with CData Sync

CData Sync allows users keep pace with evolving data needs. CData Sync offers automated data integration between tables with different schema, giving you the ability to combine and synchronize data from multiple sources across various formats into your centralized data warehouse.

One standout feature of Sync is ‘Change Schema,’ which compares the source and destination schema in every run to detect discrepancies and combat schema drift. If CData Sync detects a structure difference between two schemas, CData Sync automatically modifies the destination schema so that your data is represented accurately.