What is a Relational Database?
Relational data, as its name suggests, refers to data that showcases relationships between different elements. Its core objective is to uphold precise documentation of objects and their interconnections with one another. This type of data is dynamic and subject to frequent updates to ensure ongoing accuracy.
Relational databases serve as versatile solutions for storing and retrieving various types of data. They structure data into tables composed of rows and columns, employing SQL (Structured Query Language) for data querying and management. They are widely used in different applications where data integrity, ACID (Atomicity, Consistency, Isolation, Durability) observation, and complex querying abilities are required, like eCommerce, finance, and traditional business applications.
Time Series Database vs Relational Database: Top Differences
Choosing between a time-series database and a relational database isn’t merely a matter of data storage location. It’s pivotal for shaping your organization’s data handling practices — it’s about enhancing how you extract insights from your data. This decision significantly impacts speed, efficiency, and the precision of your operations.
Consider the potential: smoother operations, swifter decision-making, and a competitive edge in your sector. This is the influence of selecting the correct database. However, to maximize its services, it’s important to understand the differences between these databases and leverage their different strengths to your benefit.
This article gives you a brief discussion between time series databases and relational databases. We will try to keep it simple, breaking down the terms and helping you choose the right option for your requirements.