Relational Database

Relational databases were developed by data scientist E.F. Codd during his time as an employee at IBM. The relational model, as theorized by Codd about 50 years ago, laid the foundation for a widely accepted data management system.

The relational model is a method of managing data in a structured setting. In practice, users can store and organize data for easy querying.

What is a Relational Database?

A relational database houses data structured in columns and rows. It presents the data in straightforward tables. In turn, relationships can be easily intuited between data points.

They are user-friendly, allowing for ease of querying and filtering. Additionally, they are designed to take on additional data, reduce data redundancy and support concurrency within a secure framework.

SQL (Structured Query Language is is the most common structured database language in use today.

vs. Non-relational

The primary difference between SQL and a non-relational database is structure. As discussed already, a relational database is built upon a rigid table with columns of specific types of information.

A non-relational database is useful when handling semi- or unstructured data without clearly defined schema. In non-relational dynamics, NoSQL is used. NoSQL („Not only SQL“) databases store non-structured data in a single document that can contain text, photos, videos and other content. Examples include: MongoDB, Oracle NoSQL Database and Apache Ignite.

Relational databases offer an advantage when users want to query often and perform data analysis. Transactions within the database will be more reliably processed in this format as well.

You want your database ACID compliant to guarantee validity of transactions. ACID is an acronym that refers to the set of properties that ensure the accurate processing of database transactions.

Snowflake’s unique archictecture can ingest and combine both the structured (SQL) and semi-structured data sets many businesses need to make informed business decisions.