definition

What is Relationship Cardinality?

February 9, 2026 by Ian

When you’re designing a relational database, you need to define how tables relate to each other. You do this by creating relationships between the tables. Relationship cardinality describes how many records in one table can be associated with records in another table. It’s a fundamental concept that affects how you structure your database and write your queries.

Understanding cardinality helps you avoid design mistakes that lead to data integrity issues, performance problems, or queries that return unexpected results.

What is a Vector Database?

February 7, 2026 by Ian

A vector database is a specialized database designed to store, index, and query high-dimensional vectors. These are arrays of numbers that represent data in mathematical space. Unlike traditional databases that store text, numbers, or structured data, vector databases work with embeddings, which are numerical representations of complex data like text, images, audio, or video that capture their semantic meaning.

These databases solve the specific problem of finding similar items based on meaning rather than exact matches. Traditional databases excel at finding exact matches or simple comparisons. For example “find all users named Bella” or “find products under $50.” Vector databases excel at similarity searches. These could look something like “find images similar to this one” or “find documents with similar meaning to this query,” even when the exact words or pixels are different.

What is a Multi-Model Database?

February 2, 2026 by Ian

A multi-model database is a database system that supports multiple data models within a single, integrated backend. Instead of being limited to one way of organizing data (like relational tables, documents, or graphs) a multi-model database lets you store and query different types of data using the most appropriate model for each use case.

Traditional database systems typically specialize in one data model. A relational database like MySQL organizes everything into tables with rows and columns. A document database like MongoDB stores JSON-like documents. A graph database like Neo4j focuses on nodes and relationships. With a multi-model database, you get several of these capabilities in one system, storing relational data, documents, key-value pairs, and graphs side by side.

What is a Snowflake Schema?

January 29, 2026 by Ian

In relational databases, a snowflake schema is a variation of the star schema where dimension tables are normalized into multiple related tables. Instead of keeping all dimensional attributes in a single wide table, you break them down into a hierarchy of tables that branch out like (you guessed it) a snowflake.

It’s basically what happens when you apply database normalization principles to a star schema’s dimension tables while keeping the central fact table intact.

What is Database as a Service (DBaaS)?

February 2, 2026January 26, 2026 by Ian

Database as a Service (DBaaS) is a cloud computing model where a provider hosts and manages your database for you. Instead of installing database software on your own servers, configuring it, and handling ongoing maintenance, you access a fully managed database over the internet. The provider takes care of all the technical heavy lifting while you focus on using the database for your applications.

What is a Distributed Database?

February 2, 2026January 23, 2026 by Ian

A distributed database is a database system where data is stored across multiple physical locations. This could be either on different servers in the same data center or across multiple data centers in different geographic regions. Instead of all your data residing on a single machine, it’s spread across several computers that work together as a unified system.

From an application’s perspective, a distributed database often looks like a single database. You connect to it and run queries as usual. Behind the scenes, however, the database system coordinates multiple servers to store data, process queries, and maintain consistency across all locations.

What is an Attribute in a Database?

January 21, 2026 by Ian

In database terminology, an attribute is a piece of information that describes an entity. If an entity is a thing you want to store data about, attributes are the specific details you’re storing. They’re the characteristics, properties, or facts that define what you know about each entity.

When you create a relational database, attributes become the columns in your tables. Each attribute represents one type of information you’re tracking.

What is a Database Entity?

January 21, 2026January 20, 2026 by Ian

In database design, an entity is something you want to store information about. It’s a person, place, thing, event, or concept that matters to your application and has data associated with it that you need to track.

Entities are the building blocks of database design. Before you create tables, write queries, or think about indexes, you need to identify what entities exist in your domain and what information you need to store about them.

What is a Missing Index in SQL Server?

January 17, 2026 by Ian

SQL Server has a concept of “missing indexes”. And no, it’s not referring to an index that used to be there but has now disappeared. Rather, the missing index concept is designed to help us improve the performance of our database.

A missing index is an index that doesn’t yet exist on your table but probably should. SQL Server actually tracks queries that would benefit from indexes and stores suggestions in a set of dynamic management views (DMVs) called the missing index DMVs.

When you run a query and the optimizer thinks that this would be way faster with an index on those columns, it logs that suggestion. Over time, these suggestions accumulate, giving you a prioritized list of indexes that could improve your database’s performance.

What is a Slowly Changing Dimension?

January 21, 2026January 14, 2026 by Ian

In data warehousing, a slowly changing dimension (SCD) is a dimension table where the attributes change over time, but not very frequently. The term “slowly changing” refers to the fact that these changes happen occasionally (perhaps days, weeks, or months apart) rather than constantly like transactional data.

The challenge is figuring out how to handle these changes while maintaining accurate historical analysis. When a customer moves to a new state or a product gets recategorized, you need a strategy that preserves the integrity of your historical data.