---
title: What Makes Data "Time-Series"? | Tiger Data Docs
description: Learn the fundamental definition and characteristics of time-series data
---

**Tiger Data 101** → TimescaleDB | **Section:** Working with Time-Series Data | **⏱ Time:** \~2 min

## Learning objectives

By the end of this module, you will be able to:

- **Define** time-series data and **identify** its characteristics and use cases (Remember/Understand)
- **Explain** the differences between time-series and relational data models (Understand)
- **Analyze** time-series data scenarios and **determine** appropriate storage solutions (Analyze)

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## What Makes Data "Time-Series"?

**Time-series data** is an ordered sequence of data points indexed by time. Think of any measurement that happens repeatedly over time:

- A server reporting CPU usage every 10 seconds
- A smart meter recording energy consumption every minute
- A stock exchange ticking prices thousands of times per second
- A factory sensor logging temperature and pressure 24/7
- A weather station recording temperature, humidity, and pressure hourly
- An IoT device transmitting location coordinates every few seconds

The key insight: **the timestamp is the organizing principle**. Data flows in as a continuous or near-continuous stream, and you query it by time ranges, not by discrete identifiers.

Note

In databases, time-series data is often referred to as **TSDB** data or belonging to a **Time-Series Database** (TSDB).

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## Why Does This Matter?

Time-series data has fundamentally different access patterns than traditional relational data:

| Traditional Relational Data      | Time-Series Data                                |
| -------------------------------- | ----------------------------------------------- |
| "Give me the user with ID 12345" | "Give me all metrics for device X from 9am–5pm" |
| Emphasis on random access        | Emphasis on range queries                       |
| Updates are common               | Append-only (rarely updated)                    |
| Row-based storage is efficient   | Column-based storage is more efficient          |

Standard databases can store time-series data, but without specialized tooling they become slow, expensive, and painful to manage at scale.

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## Ready to Go Deeper?

Next, you'll explore the **characteristics** that define time-series data and how they shape storage and query design.