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Observability Engineering: Metrics, Logs, and Traces · LearnSpace
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Observability Engineering: Metrics, Logs, and Traces

Курс от Edureka
Средний≈ 12.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This program explores how observability enables engineers to understand, monitor, and troubleshoot modern distributed systems by using metrics, logs, and traces. You’ll begin by learning the foundational principles of observability, understanding how it differs from traditional monitoring, and exploring the three pillars of observability. Through hands-on demonstrations with Prometheus and Node Exporter, you will learn how system telemetry is collected and how metrics provide visibility into infrastructure and application behavior. You’ll then design reliability-focused metrics strategies using concepts such as Golden Signals, Service-Level Indicators (SLIs), Service-Level Objectives (SLOs), and error budgets. Practical demonstrations show how to collect application metrics, write PromQL queries, and analyze latency and error patterns. You will also explore metrics visualization and alerting by building Grafana dashboards, configuring thresholds, and creating alert rules with Prometheus and Alertmanager to detect operational incidents quickly. Next, you’ll examine centralized logging and distributed tracing, learning how logs and traces provide deeper insight into system behavior. Using Loki, Fluent Bit, OpenTelemetry, and Jaeger, you will explore how logs are aggregated, how requests are traced across microservices, and how engineers analyze service dependencies and request latency. You will also learn how modern observability platforms use AI-powered anomaly detection in Grafana to identify unusual system behavior and support proactive monitoring. By the end of this program, you will be able to: -Explain the principles of observability and differentiate it from monitoring. -Collect and analyze system metrics using Prometheus and PromQL. -Design dashboards and visualizations using Grafana. -Configure alerts and incident notifications using Prometheus and Alertmanager. -Implement centralized logging pipelines using Loki and Fluent Bit. -Instrument distributed systems with OpenTelemetry and analyze traces using Jaeger. This program is designed for DevOps engineers, site reliability engineers, software developers, and cloud engineers who want to improve system reliability and operational visibility. A basic understanding of cloud infrastructure, containerized systems, and application architecture will help maximize your learning experience. Learners need a reliable internet connection, a modern web browser, and access to commonly used observability tools; no specialized hardware or complex infrastructure setup is required. Join us to master modern observability practices and learn how engineering teams monitor, diagnose, and optimize distributed systems using powerful open-source observability technologies.

Навыки, которые вы освоите

MicroservicesPrometheus (Software)Distributed ComputingSystem MonitoringAnomaly DetectionDashboard CreationService LevelGrafanaReliabilityPerformance MetricTime Series Analysis and ForecastingKubernetesEvent MonitoringPerformance AnalysisDevops ToolsSystems AnalysisIncident ResponseContinuous MonitoringSite Reliability EngineeringIssue Tracking

Программа курса

4 модулей · 81 учебных материалов

01Fundamentals of Observability and System Signals28 материалов

Observability Fundamentals and System Signals

Course IntroductionВидеоCourse SyllabusЧтениеScenario: Investigating Unexpected System BehaviourВидеоWhat is Observability?Видео

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Edureka

Преподаватель курса

Observability Engineering: Metrics, Logs, and Traces
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Обучение на Coursera

≈ 12.2 ч

4 модулей

Язык: Английский

Часть программы вашего университета
What is Monitoring?Видео
Observability vs Monitoring in Modern SystemsВидео
The Three Pillars of ObservabilityВидео
Demonstration: Installing Prometheus for Metrics CollectionВидео
Demonstration: Configuring Node Exporter for Host MetricsВидео
System Signals and Telemetry SourcesЧтение
Observability Terminology and Core SignalsЧтение
Practice Assignment: Fundamentals of Observability and System SignalsЗадание

Metrics Design, SLIs, and Reliability Targets

Metrics, Golden Signals, and Reliability IndicatorsВидеоService Reliability with SLIs, SLOs, and Error BudgetsВидеоDemonstration: Exploring Application Metrics Exposed with PrometheusВидеоDemonstration:PromQL Queries for Latency and Error MetricsВидеоDemonstration: Defining Service-Level Indicators Using Prometheus MetricsВидеоSLIs and Reliability Metrics in EngineeringЧтениеPractice Assignment: Metrics Design, SLIs, and Reliability TargetsЗадание

Metrics Storage and Querying with Prometheus

Prometheus Architecture and Time-Series Data ModelВидеоDemonstration: Scraping Metrics from a Sample ApplicationВидеоDemonstration: Using PromQL for Aggregation and FilteringВидеоPersisting Metrics Using Prometheus Local StorageЧтениеPrometheus Querying PatternsЧтениеPractice Assignment: Metrics Storage and Querying with PrometheusЗадание

Module Wrap-Up and Assessment

Module Summary: Observability Foundations and Metrics EngineeringЧтениеKnowledge Check: Observability Foundations and Metrics EngineeringЗаданиеDiagnosing System Reliability Issues with MetricsDIALOGUE
02Visualization, Alerting, and Logging Pipelines21 материалов

Metrics Visualization with Grafana

Metrics Visualization and Dashboard DesignВидеоDemonstration: Installing Grafana and Connecting PrometheusВидеоDemonstration: Creating Time-Series Dashboards in GrafanaВидеоDemonstration: Configuring Thresholds and Annotations in GrafanaВидеоVisualization Design for ObservabilityЧтениеPractice Assignment: Metrics Visualization with GrafanaЗадание

Alerting Strategies and Incident Signals

Alerting Strategies and Alert FatigueВидеоDemonstration: Creating Alert Rules in PrometheusВидеоDemonstration: Configuring Alertmanager for NotificationsВидеоDemonstration: Alert Trigger and Recovery ValidationВидеоAlerting and Incident Response PatternsЧтениеPractice Assignment: Alerting Strategies and Incident SignalsЗадание

Centralized Logging Architecture

Structured Logging and Log PipelinesВидеоDemonstration: Installing Loki for Log AggregationВидеоDemonstration: Shipping Application Logs to LokiВидеоDemonstration: Querying Logs Using LogQLВидеоLogging Architecture and RetentionЧтениеPractice Assignment: Centralized Logging ArchitectureЗадание

Module Wrap-Up and Assessment

Module Summary: Visualization, Alerting, and Logging PipelinesЧтениеKnowledge Check: Visualization, Alerting, and Logging PipelinesЗаданиеInvestigating System Alerts Using Metrics, Dashboards, and LogsDIALOGUE
03Distributed Tracing and End-to-End Observability26 материалов

Distributed Tracing and Context Propagation

Distributed Tracing Concepts and TerminologyВидеоTrace Context, Spans, and Service DependenciesВидеоDemonstration: Instrumenting an Application with OpenTelemetry SDKВидеоDemonstration: Exporting Traces to JaegerВидеоDemonstration: Analyzing Request Latency Across Services in JaegerВидеоDistributed Tracing with OpenTelemetry and JaegerЧтениеPractice Assignment: Distributed Tracing and Context PropagationЗадание

Observability for Containerized Applications

Observability Challenges in Kubernetes EnvironmentsВидеоDemonstration: Collecting Kubernetes Metrics Using PrometheusВидеоDemonstration: Collecting Container Logs with Fluent BitВидеоDemonstration: Tracing Requests Across Microservices in JaegerВидеоCloud-Native Observability PatternsЧтениеPractice Assignment: Observability for Containerized ApplicationsЗадание

Correlating Metrics, Logs, and Traces

Correlation Strategies Across Telemetry SignalsВидеоInvestigating System Incident Using Metrics and LogsЧтениеDemonstration: Analyzing Request Latency Using Distributed TracesВидеоCorrelating Metrics, Logs, and Traces for Complete ObservabilityЧтениеPractice Assignment: Correlating Metrics, Logs, and TracesЗадание

AI-Powered Observability with Grafana

Introduction to AI and Machine Learning in ObservabilityВидеоHow Grafana Uses AI for Anomaly Detection and InsightВидеоDemonstration: Enabling Machine Learning - Based Anomaly Detection in GrafanaВидеоAI-Assisted Observability Patterns in GrafanaЧтениеPractice Assignment: AI-Powered Observability with GrafanaЗадание

Module Wrap-Up and Assessment

Module Summary: Distributed Tracing and End-to-End ObservabilityЧтениеKnowledge Check: Distributed Tracing and End-to-End ObservabilityЗаданиеDiagnosing a Microservices Incident Using Traces and Telemetry SignalsDIALOGUE
04Course Wrap-Up and Assessment6 материалов

Course Wrap-Up and Assessment

Reflecting on Your Observability Learning JourneyDIALOGUEPractice Project: Building a Complete Observability Platform for QuantumOps TechnologiesЧтениеEnd Course Knowledge Check: Observability Engineering: Metrics, Logs, and Trace ЗаданиеDesigning a Modern Observability Architecture Using Metrics, Logs, and TracesЗаданиеCourse SummaryВидеоDescribe Your Learning JourneyОбсуждение