---
title: Time-Series vs. Relational Data Models | Tiger Data Docs
description: Understand the fundamental differences in how these two paradigms approach data
---

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

## The Fundamental Difference

| Dimension                | Relational Model                   | Time-Series Model                             |
| ------------------------ | ---------------------------------- | --------------------------------------------- |
| **Organizing principle** | Entity identity (ID)               | Timestamp                                     |
| **Primary query**        | "Get me entity X" (lookup)         | "Get me data from time T1 to T2" (range scan) |
| **Data mutability**      | Frequent updates, deletes          | Append-only                                   |
| **Storage optimization** | Row-based (fast for random access) | Column-based (fast for range queries)         |
| **Example use case**     | Customer, Order, Product records   | Sensor readings, stock prices, events         |

---

## Side-by-Side Example

### Relational: User Profiles

```
-- A typical relational table
CREATE TABLE users (
  id BIGINT PRIMARY KEY,
  name TEXT,
  email TEXT,
  account_tier VARCHAR(50),
  updated_at TIMESTAMP
);


-- Relational query: Find a specific user
SELECT * FROM users WHERE id = 12345;
```

**Characteristics:**

- Queries are **lookups by ID** or small filters
- Data **updates frequently** (email, tier, preferences)
- Each row is independent
- Storage is **row-based** (all columns for one user together)

### Time-Series: Sensor Readings

```
-- A time-series table (hypertable in TimescaleDB)
CREATE TABLE sensor_readings (
  time TIMESTAMP NOT NULL,
  device_id TEXT NOT NULL,
  temperature FLOAT,
  humidity FLOAT,
  pressure FLOAT
);


-- Time-series query: Get data over a time range
SELECT * FROM sensor_readings
WHERE time >= '2025-01-01'::timestamp
  AND time < '2025-01-02'::timestamp
  AND device_id = 'sensor_042';
```

**Characteristics:**

- Queries are **range scans** (fetch data between two timestamps)
- Data **never updates** (readings are immutable)
- Each row is part of a larger time-ordered sequence
- Storage is **column-based** (all temperatures, all humidities, etc.)

---

## Why the Differences Matter

### Relational Optimization

Relational databases optimize for:

- **Random access**: Fast row lookups by ID
- **In-place updates**: Modify values efficiently
- **Complex relationships**: JOINs between entities

When you apply relational optimization to time-series data, you get **slow queries and expensive storage**.

### Time-Series Optimization

Time-series databases optimize for:

- **Sequential writes**: Bulk appends are cheap
- **Range queries**: Scanning a time window is fast
- **Compression**: Encode columns efficiently
- **Lifecycle**: Automatically manage old data

When you apply time-series optimization to relational data, you get **slow random lookups**.

Note

The same database can hold both! TimescaleDB is a PostgreSQL extension, so you can store relational data (users, orders, metadata) **and** time-series data (metrics, events, sensor readings) in the same instance, with each optimized appropriately.

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[NEXT PAGE→](/learn/tiger-data-academy/tiger-data-101/working-with-time-series-data/common-time-series-patterns/index.md)

[Common Time-Series Patterns](/learn/tiger-data-academy/tiger-data-101/working-with-time-series-data/common-time-series-patterns/index.md)

[Explore the most common patterns for time-series data: aggregations, downsampling, windowing, and anomaly detection.](/learn/tiger-data-academy/tiger-data-101/working-with-time-series-data/common-time-series-patterns/index.md)