---
title: Enabling Hypercore & Best Practices | Tiger Data Docs
description: Apply compression and manage data lifecycle
---

**Tiger Data 101** → TimescaleDB | **Section:** Hypercore | **⏱ Time:** \~3 min

## Hot vs. cold data: mixed chunk access

TimescaleDB allows you to keep recent chunks uncompressed (hot) while compressing older chunks (cold), all in the same hypertable:

```
sensor_readings
├── Jan 2025 (compressed, 50MB)  ← cold, compressed, queryable
├── Feb 2025 (compressed, 48MB)
├── Mar 2025 (compressed, 52MB)
└── Apr 2025 (uncompressed, 500MB) ← hot, fast writes, recent data
```

**Benefits:**

- Recent data stays fast for writes and quick queries
- Old data compressed for storage savings
- No schema changes needed—hypertable hides the complexity

---

## When to use hypercore

**Use compression when:**

- Storing historical data you query infrequently
- Data has patterns (sensors, metrics, events)
- You need to reduce storage costs significantly
- Analytical queries outweigh random access

**Avoid compression when:**

- Data is frequently updated or deleted
- You need ultra-low latency random access
- Compression would add overhead that doesn't save space

---

## Knowledge check

### Knowledge Check

Question 1 of 3

#### A. Kempower achieved approximately what storage footprint reduction using hypercore compression?

A)2xB)5xC)15xD)100x

Check Answer

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[NEXT CHAPTER→](/learn/tiger-data-academy/tiger-data-101/continuous-aggregates/the-problem-and-solution/index.md)

[Continuous Aggregates](/learn/tiger-data-academy/tiger-data-101/continuous-aggregates/the-problem-and-solution/index.md)

[Learn how to pre-compute rollups for fast analytics with automatic incremental refresh and hierarchical aggregations.](/learn/tiger-data-academy/tiger-data-101/continuous-aggregates/the-problem-and-solution/index.md)