Dataset schema

What is a dataset schema?

A dataset schema is a blueprint that outlines how particular data, such as in a database, is structured, configured and organized. It provides a reference point that indicates what fields of information the project contains. This makes the data easily understandable and improves management and efficiency. A schema does not contain the actual data but describes the structure and constraints that apply to that data.

There are three main types of data schema:

• Relational (or Database) Schema: Commonly used in relational database management systems (RDBMS), this shows the logical storage of the data in a database. It represents the organization of data and provides information about the relationships between items such as tables in a given database. Star schema and Snowflake schema are both examples of database/data warehouse schema.
• XML Schema: This defines the structure and content of XML documents, facilitating data exchange and interoperability between different systems and platforms.
• JSON Schema: This defines the structure and validation rules for JSON (JavaScript Object Notation) data, allowing consistency and standardization.

Why are dataset schemas important?

Dataset schemas are central to organizing data, helping users identify relationships between different fields, columns and tables and therefore better manage data. They deliver six benefits:

• Data integrity. They increase accuracy, as schema reduce the possibility of incorrect information being entered in a database, and enforce rules around consistency.
• Data security and compliance. They improve security, preventing users from accessing or modifying sensitive information.
• Query optimization. A well-structured data schema improves the performance of running queries or retrieving data, increasing efficiency and lowering processing time.
• Data interoperability. By standardizing data schemas, it is easier to exchange and integrate data between different databases and systems.
• Data scalability. Alongside better performance, a well-designed schema is able to scale to accommodate growing data volumes and changing business needs.
• Data insights. Clear data schema make it easier for data professionals to understand their data and therefore create faster insights from their information.

What does a dataset schema contain?

Data schemas can operate at one of three levels – conceptual, logical or physical, depending on how close they are to the data itself.

Conceptual schema

This provides a high-level presentation of the structure and relationships in a database. It describes the main concepts of data, at an abstract level, as well as how they are related to each other. However, it does not go into detail about specific objects such as tables, views, and columns. This overview helps database developers to understand the underlying structure and identify and fix any problems or inconsistencies. This is then used to create more detailed schemas.

Logical schema

This provides a more detailed description of the data than a conceptual schema, including specific objects such as tables and columns. It sets out the structure and relationships between various entities within a database, as well as how data is stored in the tables. As the name suggests, the aim of the logical scheme is to ensure that data is logically organized and stored efficiently.

Physical schema

This is the most detailed level of a database design and describes how data is physically stored in the system and outlines specific objects such as tables, columns, indexes, and views. Demonstrating the level of detail it covers, it also includes information about the storage media used for each table, such as a cloud data warehouse or data lakehouse, as well as any constraints or triggers associated with the data or storage methodology.

What are dataset schemas used for?

In the same way that the blueprint of a building helps builders, a schema saves time and money by avoiding the need to make changes once the database has been created. Data schemas allow data managers to plan how their database will be structured, before they develop and deploy it. That makes it vital to involve all stakeholders in dataset schema design and to understand and plan forthcoming needs to create a future-proofed data schema.