---
title: From Development to Production | Tiger Data Docs
description: Scale your TimescaleDB deployment
---

**Module:** Tiger Data 101 → An Introduction to TimescaleDB\
**Section:** Putting TimescaleDB Into Action\
**Estimated time:** 10–12 minutes total (this page: \~2 minutes)

---

## Learning objectives

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

- **Evaluate** TimescaleDB capabilities against specific use cases
- **Design** solutions that scale from development to production
- **Analyze** real-world case studies and **apply** lessons learned

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## Development vs. Production

### Development

- Single machine TimescaleDB instance
- Standard PostgreSQL client libraries
- Manual data management (compression, tiering, retention)

### Production (Tiger Cloud)

- Fully managed service (automated backups, scaling, monitoring)
- High availability with read replicas
- Built-in connection pooling
- Automated lifecycle policies

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## Connection Pooling

Essential for applications with many concurrent connections:

```
Application (1000 connections)
    ↓
PgBouncer (connection pool, 100 actual connections)
    ↓
TimescaleDB
```

Benefits: Fewer database resources, faster connection recycling

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## Schema Design Best Practices

1. **Use appropriate time column types:**

   - `TIMESTAMPTZ` for global applications (timezone-aware)
   - `TIMESTAMP` for local times

2. **Denormalize selectively:**

   - Store device metadata alongside readings
   - Reduces JOIN overhead

3. **Index strategically:**

   - (time, tag) composite index for time-filtered, tagged queries
   - (tag, time) for tag-filtered, time-ordered queries

---

→NEXT MODULE

[**Data Lifecycle Management** — Learn retention policies, compression, and tiering strategies.](/learn/tiger-data-academy/tiger-data-101/putting-timescaledb-into-action/data-lifecycle-and-design-for-scale/index.md)