Time, Part 1

Fundamental Types of Time, Temporality, and Representing Time

This is Part 1 of 2 in the chapter on Time, a building block of data modeling. Time is often overlooked, and I want to give it sufficient and practical coverage. Here, I build the foundation of what you need to know about time that will help you with your data modeling.

The next part will discuss ways of querying time, which I also find overlooked. If you know how time is queried, you’ll have a better idea of how to model it.

NOTE - This draft section is replaced by the new full draft chapter on Time.

Thanks¹,

Joe

Introduction: Why Time Matters in Data Modeling

Time - “A finite extent or stretch of continued existence, as the interval separating two successive events or actions, or the period during which an action, condition, or state continues; a finite portion of time (in its infinite sense: see sense A.IV.34a); a period. Frequently with preceding modifying adjective, as a long time, a short time, etc.” - Oxford dictionary

Poor handling of time has cratered many applications, analyses, and ML/AI models. Time is a top-priority concern for any data system, yet it remains one of the most nuanced and misunderstood aspects of our work. Reality isn’t static. Events happen, states change, and when we model data, we are almost always attempting to capture a faithful record of these changes over time.

In this chapter, we will build a comprehensive framework for modeling time correctly. We'll start by defining the fundamental types of time and the concept of temporality for tracking history. From there, we'll cover the practical essentials of representing, storing, and querying time.