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README.md

🚀 Lab 36: Energy Pack Ingestion and Threshold Detection

🧭 Lab Overview

Installed an energy-monitoring stack with InfluxDB, Telegraf, and Kapacitor, ingested energy metrics from a simulator, and created threshold-based alerting for abnormal power consumption.

🎯 Objectives

  • Ingest and monitor energy consumption metrics from system data
  • Configure threshold-based detection rules
  • Generate automated alerts when thresholds are exceeded
  • Implement a complete monitoring pipeline using open-source tools

🧩 Prerequisites

  • Basic Linux command-line proficiency
  • Understanding of system metrics and monitoring concepts
  • Familiarity with text editors (nano or vim)
  • Basic knowledge of configuration files and log analysis
  • Understanding of alerting concepts

🖥️ Lab Environment

  • Platform: Ubuntu 24.04 LTS cloud lab environment
  • Host: ip-172-31-10-187
  • Shell: Bash
  • Primary toolchain: InfluxDB, Telegraf, Kapacitor, InfluxQL/DBRP mapping, TICKscript, Bash

🛠️ Task Overview

  • Install and Configure the Monitoring Stack
  • Configure Threshold Detection and Alerting

📁 Repository Structure

lab36-energy-pack-ingestion-and-threshold-detection/
├── config/
│   ├── kapacitor/
│   │   └── kapacitor.conf
│   └── telegraf/
│       └── telegraf.conf
├── scripts/
│   ├── energy_simulator.sh
│   └── verify_lab.sh
├── tickscripts/
│   └── energy_threshold_alert.tick
├── commands.sh
└── output.txt

✅ Verification and Validation

  • Service checks, ingestion counts, Telegraf test output, Kapacitor logs, and the verification script all confirmed the pipeline.
  • InfluxDB, Telegraf, and Kapacitor were all active during verification.
  • Energy data was appended to the source log and ingested into the monitoring pipeline.
  • Kapacitor produced warning or critical alerts when simulated values exceeded thresholds.
  • The verification script confirmed running services, generated data, and alert log presence.

📘 What I Learned

  • How to connect collection, storage, and stream processing in one monitoring pipeline.
  • How to use threshold rules for operational alerting.
  • How DBRP compatibility supports 1.x-style queries in the stack used by the lab.
  • How to validate a telemetry pipeline from source log to alert output.

🌍 Why This Matters

Energy and facility monitoring needs fast detection of abnormal power usage before it becomes an operational or cost issue.

🏭 Real-World Applications

  • Data-center power monitoring.
  • Threshold alerting for operational telemetry.
  • Open-source observability for energy or facility systems.

🧪 Result

This lab was completed successfully and documented with separate source files, execution commands, runtime output, interview prep, and troubleshooting guidance.

🏁 Conclusion

This lab strengthened practical experience with energy pack ingestion and threshold detection in a hands-on Linux environment. The documented workflow, source files, and verification steps make the implementation reproducible and suitable for portfolio use.