What is a Database Schema?
A database schema is a visual diagram that outlines how the data in a database is connected. Relational databases store data in tables, with each table containing information about a single entity, such as a customer or product. The entities are related to each other; for example, one customer can buy multiple products, but one product can only be purchased by one customer. A database schema visual represents these interconnections for efficient database design.
What are the benefits of a database schema?
As a database schema defines how a business organizes its data, there are several benefits to using one.
Improve organization
Businesses can organize their information into clear data structures to improve organization and ensure that the relationships between datasets are clear and consistent. A well-defined schema also enables businesses to scale their databases more easily.
Enhance data integrity
By implementing rules about how your business stores data with a schema, you ensure a high level of integrity, even in complex storage systems. Maintaining consistent rules helps ensure data validity and meet compliance requirements.
Increase accessibility
A database schema offers various views into the overall data structures you use. Using these various levels, designers, administrators, and stakeholders can all discuss the structure even without technical knowledge.
What are the types of database schemas?
There are three primary types of database schemas commonly used in a database management system.
Conceptual database schema
A conceptual database schema design is the highest-level view of a database, providing a user with an overall view of the entire database. What conceptual schemas make up for in breadth, they lack in specificity. They don’t contain implementation details about the data, such as its type or the constraints it follows. A conceptual schema is useful for charting the overall data flow in an organization, without offering too much detail.
Logical database schema
A logical database schema design provides an outline of how data within a database is structured. It describes the relationship between entities and shows more details about how data is organized. Each entity in the data schema is defined in relation to information like:
- Table names
- Entity relationships
- Integrity constraints
- Field names.
This form of schema ensures data consistency and integrity by providing constraints into which data will fit.
A logical database schema design is less conceptual (or removed from the actual data) than a conceptual database, but more so than a physical database schema. Logical database schemas do not typically include any technical requirements.
Physical database schema
A physical schema describes exactly where data can be found within a broader database structure. It includes technical storage details, identifying the file locations, specific storage formats, and indexing strategies used by each table to store its data. The physical schema is the least conceptual form of database schema and offers real insight into data locations.
What are some database schema styles?
Different database schema styles suit distinct business needs and data types. Here are some of the most popular database schema styles.
Star schema
Businesses can utilize a star schema to manage and organize large datasets based on two primary principles: facts and dimensions. In the context of a star schema, a fact is at the center of the structure and provides the most important piece of data. For example, the number of transactions, website clicks, or total purchases. A dimension then provides additional information about the fact, such as which customer made the purchase, where they made it from, and what product they bought.
Hierarchical schema
A hierarchical database schema employs a tree-like structure, with a root node at the top that branches out into other subdirectory branches. In a hierarchical model, each ‘parent’ piece of data can have multiple child tables, while each child table can only have one parent. For example, a hierarchical model could begin with a company, branch out to each department, and then branch further to individual employees within each department.
Snowflake schema
A snowflake schema, much like a star schema, uses a central fact table that then connects to multiple dimension tables. However, unlike the star schema, the snowflake schema dimension tables will then have a range of additional database tables that branch off them, offering more details on those dimensions. Using a snowflake schema is useful for data that has a large number of dimensions and sub-dimensions. Both star and snowflake schemas are often used in relational database management systems.
Relational database schema
A relational schema assigns each object to a table and then maps the connections between each table. This doesn’t prioritize any ‘fact’ center over other data types; instead, it has multiple bridging relationships between all pieces of data. Relational schemas utilize tables, columns, and rows to construct data systems, which they connect through relationships and constraints.
What is the process of designing a database schema?
The process of designing a database schema is called data modeling. Here are the main steps to produce a data model.
Gather requirements
Before creating a database, you must identify its purpose and outline the key information, such as the data you want it to contain and how you plan to use the database. The best database for you will vary depending on:
- Specific data you use
- Queries you need to interact with it
- Reports you want to generate,
This step outlines your objectives, guiding your database schema design process.
Create entity-relationship diagrams
An entity relationship diagram (ERD) maps out how tables, database objects, and individual entities within a database connect. Creating a conceptual schema view of your database enables you to visualize how the database functions and gain insight into the data it stores.
At this stage, you can also define the naming conventions that tables, columns, database objects, and indexes use in your database. Conventions help everyone to have a standard approach when inputting data.
Organize data entities into tables
Based on your ERD map, you can now organize all of your data into specific tables. Each entity in your database should have its own table, with individual columns holding related attributes. Define the primary key that will allow you to easily identify and retrieve specific data values.
Normalize data structures
Normalization is a process in database schema design aimed at reducing data redundancy and improving data integrity. It involves organizing data into tables in such a way that relationships among the data are well-structured and anomalies are minimized. There are several normal forms, each with specific requirements. Each successive normal form addresses a different redundancy or dependency type to enhance data consistency and make the schema more robust.
1NF
1NF requires that each column contains atomic (indivisible) values and that each record is unique. It removes repeating groups and multi-valued fields.
2NF
2NF builds on 1NF by ensuring that all non-key attributes are fully functionally dependent on the entire primary key (i.e., it eliminates partial dependencies).
3NF
3NF adds that all non-key attributes must depend only on the primary key, and not on other non-key attributes (i.e., it removes transitive dependencies).
Implement security measures
Create a permissions structure to ensure that only authorized users can access your database and view the information it contains. You can assign distinct privileges to different user groups in the database, such as the ability to read, write, or delete information, which helps keep your sensitive data safe. Define role based access controls to ensure that only authorized users can view or modify sensitive data
Test
Testing your database schema design with some basic queries and other interactions ensures everything functions as intended. Collecting data on how the database functions at this stage will inform any additional changes you need to make to ensure your schema is effective and free from performance issues.
What is the difference between a database schema and a database instance?
A database schema refers to the overall design of a database, providing information about its structure, what it may include, and the relationships between datasets. However, a data schema doesn’t actually contain any data.
A database instance is the active session that a database schema describes and holds the data at any given moment. An instance is where the actual data values are and will constantly change as new data is added, deleted, or updated.
What is database schema conversion?
A database schema conversion is the process of adapting the existing database schema to a new format. This may involve adding or modifying tables, columns, indexes, constraints, or relationships between tables.
The goal is often to support new application requirements, improve performance, or move to a different database platform. Schema conversion enables more efficient data organization or to support features of a new system.
Data migration may or may not require schema conversion, depending on the source and destination tooling.
How can AWS support your database schema requirements?
AWS Database Migration Service (AWS DMS) is a managed migration and replication service that helps you move your databases and analytics workloads to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime for applications that rely on it.
DMS Schema Conversion in AWS DMS makes database migrations between different types of databases more predictable. It can assess the complexity of your migration for your source data provider and convert database schemas and code objects. You can then apply the converted code to your target database.
You can also use the AWS Schema Conversion Tool (AWS SCT) to convert your existing database schema from one database engine to another. AWS DMS and AWS SCT work in conjunction to both migrate databases and support ongoing replication for a variety of uses, such as populating data lakes and warehouses, synchronizing database systems, and so on.
AWS DMS is traditionally used to move smaller relational workloads (<10 TB), whereas AWS SCT is primarily used to migrate large data warehouse workloads.
Get started with database schema conversion on AWS by creating a free account today.