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Promotheus: Syllabus
Mastering Prometheus: Real-Time Monitoring and System Observability
Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. This book provides a hands-on approach to mastering Prometheus, covering real-time monitoring, metrics collection, alerting, and integration with modern data engineering stacks.
Module 1: Introduction to Monitoring and Prometheus
- What is Prometheus? Key concepts and architecture
- Understanding time-series databases and monitoring fundamentals
- Setting up and installing Prometheus
- Prometheus ecosystem overview (Alertmanager, Grafana, exporters)
Module 2: Collecting and Querying Metrics
- Understanding PromQL (Prometheus Query Language)
- Collecting system and application metrics
- Using built-in and custom exporters for data collection
- Optimizing Prometheus queries for efficiency
Module 3: Configuring and Managing Prometheus
- Configuring Prometheus for high availability and scalability
- Managing service discovery and relabeling configurations
- Implementing Prometheus data retention policies
- Securing Prometheus with authentication and authorization
Module 4: Setting Up Alerting and Notifications
- Understanding Alertmanager and alerting rules
- Creating alerts for system failures and performance anomalies
- Integrating alerts with Slack, PagerDuty, and other notification services
- Automating remediation with event-driven alerting workflows
Module 5: Visualizing Metrics with Grafana
- Connecting Prometheus to Grafana for interactive dashboards
- Designing real-time monitoring dashboards
- Creating advanced visualizations and alerts in Grafana
- Implementing best practices for dashboard usability
Module 6: Monitoring Kubernetes and Cloud Infrastructure
- Using Prometheus to monitor Kubernetes clusters
- Collecting container metrics with cAdvisor and node-exporter
- Monitoring cloud services (AWS, GCP, Azure) with Prometheus
- Implementing auto-scaling and resource optimization based on metrics
Module 7: Observability for Data Engineering Stacks
- Monitoring Apache Kafka producers and consumers
- Collecting performance metrics for Apache Spark jobs
- Using Prometheus to monitor MinIO object storage
- Tracking PostgreSQL query performance and database health
Module 8: Deploying Prometheus in Production
- Running Prometheus on Kubernetes with Helm
- Implementing federated monitoring for large-scale environments
- Configuring Prometheus for long-term storage with Thanos
- Best practices for securing and maintaining Prometheus deployments
Hands-On Projects
Project 1: Real-Time System Monitoring with Prometheus
- Set up Prometheus to collect CPU, memory, and disk metrics
- Configure alerting rules for performance anomalies
- Visualize system metrics in Grafana dashboards
Project 2: Monitoring a Streaming Data Pipeline with Prometheus
- Collect real-time metrics from an Apache Kafka pipeline
- Export data to Prometheus and analyze stream performance
- Set up alerts for high message latency or consumer lag
Project 3: Kubernetes Observability with Prometheus and Grafana
- Deploy Prometheus in a Kubernetes environment
- Monitor Kubernetes nodes, pods, and deployments
- Implement real-time alerting for resource limits
Project 4: Database Performance Monitoring with Prometheus
- Collect and analyze PostgreSQL query performance metrics
- Track database usage, slow queries, and connection pooling
- Generate insights for database optimization
Project 5: Full-Stack Observability for a Data Engineering Workflow
- Deploy Prometheus to monitor a complete data pipeline
- Integrate Prometheus with Apache Spark, Kafka, and MinIO
- Build an end-to-end monitoring dashboard in Grafana