---
title: Core Architecture | Tiger Data Docs
description: Learn the three pillars of TimescaleDB
---

**Tiger Data 101** → TimescaleDB | **Section:** What is TimescaleDB? | **⏱ Time:** \~3 min

## How it works: the core architecture

TimescaleDB introduces three core concepts that do the heavy lifting:

### Hypertables

A **hypertable** is a regular PostgreSQL table from your application's perspective. Under the hood, TimescaleDB automatically partitions it into smaller chunks by time (and optionally by another dimension like device ID).

```
         Hypertable: sensor_readings
         ┌──────────────────────────┐
         │  time  │ device │ value  │
         └──────────────────────────┘
                      │
         ┌────────────┼────────────┐
         ▼            ▼            ▼
    [chunk: Jan]  [chunk: Feb]  [chunk: Mar]
```

**Benefits:**

- Queries automatically target only the relevant chunks (no full-table scans)
- Each chunk is a real PostgreSQL table with its own indexes
- Old chunks can be compressed, moved to cheaper storage, or dropped automatically

### Hypercore

**hypercore** is TimescaleDB's columnar storage engine for chunks. When you compress a chunk with hypercore, it stores data column-by-column instead of row-by-row, following the same principle as analytical databases like Parquet or Redshift.

**Why it matters:**

- **10–40x storage compression** on typical time-series data
- Analytical queries that scan one column are dramatically faster
- You can mix compressed (cold) and uncompressed (hot) chunks in the same hypertable

### Continuous aggregates

**Continuous Aggregates (CAGGs)** are materialized views that TimescaleDB keeps automatically up to date as new data arrives.

Instead of recomputing an expensive rollup on every query:

```
-- Without CAGGs: scans raw data every time (slow)
SELECT time_bucket('1 hour', time), avg(temperature)
FROM sensor_readings
GROUP BY 1;


-- With CAGGs: reads pre-computed results (fast)
SELECT bucket, avg_temperature FROM hourly_sensor_summary;
```

CAGGs can be layered (daily from hourly, monthly from daily), enabling fast dashboards at any granularity without redundant raw data scans.

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[**Features and Comparison** — Explore the full feature set and how TimescaleDB compares to alternatives.](/learn/tiger-data-academy/tiger-data-101/what-is-timescaledb/features-and-comparison/index.md)