---
title: Industrial, APM & Infrastructure | Tiger Data Docs
description: Use cases in industrial systems, monitoring, and observability
---

**Module:** Tiger Data 101 → TimescaleDB Core Use Cases\
**Section:** Core Use Cases\
**Estimated time:** 8–10 minutes total (this page: \~2 minutes)

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## Industrial & Energy

**The challenge:** Manufacturing lines, power grids, HVAC systems generate continuous streams of operational data. Downtime is expensive. Organizations need both real-time alerting AND long-term trend analysis in one place.

**How TimescaleDB helps:**

- hypertables handle continuous ingestion from dozens or hundreds of data streams
- Continuous Aggregates power trend dashboards
- Retention + tiering policies keep decades of data accessible without runaway costs
- SQL-native anomaly detection: window functions for rolling standard deviation

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## Application & Performance Monitoring (APM)

**The challenge:** Distributed applications produce enormous volumes of metrics, traces, and logs. Traditional tools either cap retention or become prohibitively expensive at scale.

**How TimescaleDB helps:**

- hypertables absorb continuous metric streams from any number of services
- hypercore compression keeps months of metric history without exploding storage
- SQL enables cross-service correlation queries that purpose-built metrics tools can't express
- Native integrations with Prometheus and Grafana

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## Infrastructure Monitoring

**The challenge:** Centralized infrastructure monitoring (servers, containers, cloud resources) at scale requires storing millions of data points per minute while keeping queries fast.

**How TimescaleDB helps:**

- Acts as long-term storage backend for Prometheus
- Telegraf writes directly via the PostgreSQL output plugin
- Grafana connects natively
- Continuous Aggregates power capacity planning dashboards

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## Key patterns across all use cases

| Pattern                           | TimescaleDB Feature                  |
| --------------------------------- | ------------------------------------ |
| High-volume ingestion             | hypertables with automatic chunking  |
| Storage cost management           | hypercore compression + data tiering |
| Fast analytics at any granularity | Continuous Aggregates (hierarchical) |

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