1. Home
  2. Certification

Master in Observability Engineering
(MOE)

(5.0) G 4.5/5 f 4.5/5
Course Duration

Approx 15 - 20 Hours

Lab Project

50+

Certification

Industry recognized

Training Format

Online/Classroom/Corporate

Master in Observability Engineering (MOE)
8000+

Certified Learners

15+

Years Avg. faculty experience

40+

Happy Clients

4.5/5.0

Average class rating

ABOUT MASTER IN OBSERVABILITY ENGINEERING (MOE) TRAINING AND CERTIFICATION

Observability has become an essential pillar of modern software development and IT operations. It empowers engineers to maintain the health, performance, and reliability of complex systems in a dynamic and ever-changing landscape. To cater to this rising demand, the Master in Observability Engineering (MOE) training and certification program equips individuals with the knowledge and skills to excel in this critical field.

What is Observability Engineering?

Observability engineering is the practice of designing and implementing systems that are not only functional but also emit rich and actionable data that enables comprehensive monitoring, troubleshooting, and performance optimization. It goes beyond traditional monitoring by focusing on gathering the right data, analyzing it effectively, and using the insights to make informed decisions.

The MOE Training and Certification Program:

The MOE program is a comprehensive offering that delves deep into the theoretical and practical aspects of observability engineering. It covers a wide range of topics, including:

  • Monitoring fundamentals: Metrics, tracing, logging, and alerting.
  • Time series databases: Master in Observability Engineering (MOE), Grafana, and other popular tools.
  • Distributed tracing: Understanding how requests flow through complex systems.
  • Incident response: Effectively diagnosing and resolving issues.
  • DevOps practices: Integrating observability into the software development lifecycle.
  • Cloud-native observability: Monitoring microservices and containerized applications.
  • The program is designed to be vendor-agnostic, providing a well-rounded understanding of the core principles and best practices in observability engineering.

Benefits of Attending the MOE Program:

  • Stay ahead of the curve: Gain the skills and knowledge to thrive in the rapidly evolving field of observability.
  • Boost your career prospects: Become a valuable asset to any organization that relies on complex and distributed systems.
  • Improve system reliability and performance: Proactively identify and resolve issues before they impact users.
  • Enhance development efficiency: Use observability data to improve code quality and optimize resource utilization.
  • Earn a recognized certification: Validate your expertise and stand out from the crowd with the MOE certification.

How DevOpsSchool.com will help in your Master in Observability Engineering (MOE) Training Needs?

DevOpsSchool provides you the expert Master in Observability Engineering (MOE) trainer at cost-efficient price. Our instructors are highly qualified and certified with the experience and technology background of many years of industry Master in Observability Engineering (MOE). We will provide you the course completion certification of that particular course. Every participant have lifetime access of all learning materials like PDFs, PPTs, and Videos. We will provide you the training in different cities like Bangalore, Pune, and Hyderabad. If you are fresher’s and want to learn with free contents on several topics then you also browse our Tutorial section.

Instructor-led, Live & Interactive Sessions

Duration
Mode
Agenda
Batches
Course Price at
Approx 15 - 20 Hours
Online (Instructor-led)
Master in Observability Engineering (MOE)
Public batch

29,999/-
Approx 15 - 20 Hours
Videos (Self Learning)
Master in Observability Engineering (MOE)
Public batch

14,999/-
5 Days
Corporate (Online/Classroom)
Master in Observability Engineering (MOE)
Corporate Batch
Contact US

Master in Observability Engineering (MOE)

Master in Observability Engineering (MOE)

Upon completion of this program you will get 360-degree understanding of Master in Observability Engineering (MOE). This course will give you thorough learning experience in terms of understanding the concepts, mastering them thoroughly and applying them in real work environment.



Project

You will be given industry level real time projects to work on and it will help you to differentiate yourself with multi-platform fluency, and have real-world experience with the most important tools and platforms.




Interview

As part of this, You would be given complete interview preparations kit, set to be ready for the Master in Observability Engineering (MOE) hotseat. This kit has been crafted by 200+ years industry experience and the experiences of nearly 10000 DevOpsSchool's Master in Observability Engineering (MOE) learners USA.

Agenda of the Master in Observability Engineering (MOE) Download Curriculum

  • Introduction to Observability
    • Overview of Observability
    • Importance in Modern IT Infrastructure
    • Difference between Monitoring and Observability
  • Key Components of Observability
    • Understanding Logs, Metrics, and Traces
    • How these components contribute to system insights Break
  • Tools and Practices for Effective Observability
    • Overview of Popular Observability Tools (e.g., Master in Observability Engineering (MOE), Grafana, ELK Stack)
    • Best Practices in Implementing Observability
  • Practical Aspects of Observability
    • Setting up Basic Observability in an Application/Service
    • Demonstration: Using an Observability Tool
  • Advanced Topics in Observability
    • Advanced Data Analysis for Observability
    • AI and Machine Learning in Observability
  • Q&A and Wrap-Up
    • Open Session for Questions
    • Summary and Key Takeaways
  • Closing Remarks
    • Next Steps in Learning and Implementing Observability
  • Introduction to Monitoring and Master in Observability Engineering (MOE)
    • Overview of Monitoring and Observability
    • Introduction to Master in Observability Engineering (MOE): History and Key Concepts
    • Master in Observability Engineering (MOE) Architecture: Components and Data Model
    • Installation and Setup of Master in Observability Engineering (MOE)
  • Master in Observability Engineering (MOE) Basics
    • Basic Configuration: Master in Observability Engineering (MOE) YAML Configuration
    • Targets and Service Discovery
    • Scraping and Relabeling Concepts
    • Introduction to PromQL: Querying Metrics
  • Advanced PromQL
    • Aggregation and Filtering in PromQL
    • Vector Matching and Joining
    • Rate and Increase Functions
    • Alerting Rules and Alertmanager Configuration
  • Exporters and Instrumentation
    • Overview of Exporters
    • Using Node Exporter for System Metrics
    • Exporters for Common Services (e.g., MySQL, Redis)
    • Custom Instrumentation and Client Libraries
  • Integrating Master in Observability Engineering (MOE)
    • Introduction to Grafana: Features and Architecture
    • Adding Master in Observability Engineering (MOE) as a Data Source
    • Creating Dashboards with Grafana
    • Templating and Variables in Grafana Dashboards
  • Grafana Panels and Features
    • Common Grafana Panels: Graph, Table, Gauge, etc.
    • Annotations and Alerts in Grafana
    • Templating and Dashboard Variables
    • Dashboard Best Practices
  • Master in Observability Engineering (MOE) Federation and High Availability
    • Introduction to Federation in Master in Observability Engineering (MOE)
    • High Availability and Horizontal Scaling
    • Best Practices for Large-Scale Deployments
  • Monitoring Kubernetes with Master in Observability Engineering (MOE)
    • Monitoring Kubernetes Components
    • Service Discovery in Kubernetes
    • Using Master in Observability Engineering (MOE) Operator for Kubernetes Integration
    • Best Practices for Kubernetes Monitoring
  • Master in Observability Engineering (MOE) and Cloud Services
    • Integrating Master in Observability Engineering (MOE) with Cloud Services (AWS, GCP, Azure)
    • Monitoring Serverless Architectures
    • Using Master in Observability Engineering (MOE) with Managed Kubernetes Services
  • Troubleshooting and Best Practices
    • Troubleshooting Common Issues
    • Monitoring and Tuning Master in Observability Engineering (MOE) Performance
    • Best Practices for Efficient Metrics Collection
    • Security Considerations in Master in Observability Engineering (MOE)
  • Community Resources and Next Steps
    • Exploring Documentation and Official Resources
    • Joining the Master in Observability Engineering (MOE) Community
    • Contributing to Master in Observability Engineering (MOE)
    • Further Learning Paths and Advanced Topics
  • Introduction to Distributed Tracing
    • Overview of Distributed Tracing
    • Importance of Tracing in Microservices Architectures
    • Benefits of Using Distributed Tracing Systems
  • Introduction to Jaeger
    • Overview of Jaeger: History and Key Concepts
    • Jaeger Architecture: Components and Data Model
    • Integration with the OpenTracing API
  • Installation and Setup
    • Installing Jaeger in a Local Development Environment
    • Deployment Options: All-in-One vs. Distributed Setup
    • Configuring Jaeger for Different Environments
  • Basic Jaeger Operations
    • Tracing Lifecycle: Spans and Trace Context
    • Jaeger Web UI: Exploring Traces and Services
    • Querying and Filtering Traces
  • Instrumenting Applications for Tracing
    • Introduction to Tracing Instrumentation
    • Instrumenting Applications with Jaeger Clients
    • Integrating Jaeger with Popular Programming Languages
  • Integrating Jaeger with Kubernetes
    • Tracing in Kubernetes Environments
    • Deploying Jaeger in Kubernetes
    • Configuring Service Discovery and Instrumentation in Kubernetes
  • Advanced Jaeger Features
    • Trace Sampling Strategies
    • Tags, Logs, and Baggage in Jaeger Spans
    • Trace Context Propagation in Microservices
  • Jaeger Storage and Backends
    • Storage Options: Elasticsearch, Cassandra, and Kafka
    • Configuring Jaeger for Different Storage Backends
    • Best Practices for Storage and Retrieval
  • Instrumenting Specific Services
    • Instrumenting HTTP and RPC Communication
    • Database Query Instrumentation
    • Custom Instrumentation for Specialized Services
  • Monitoring and Alerts with Jaeger
    • Setting Up Alerts Based on Trace Data
    • Integrating Jaeger with Monitoring Systems
    • Best Practices for Alerting and Monitoring in Jaeger
  • Jaeger and Observability in Kubernetes
    • Integrating Jaeger with Master in Observability Engineering (MOE) and Grafana
    • Leveraging Observability Tools in Kubernetes
    • Best Practices for End-to-End Observability
  • Troubleshooting and Best Practices
    • Troubleshooting Common Jaeger Issues
    • Optimizing Performance in Jaeger
    • Security Considerations in Jaeger Deployments
  • Community Resources and Next Steps
    • Exploring Documentation and Official Resources
    • Joining the Jaeger Community
    • Contributing to Jaeger
    • Further Learning Paths and Advanced Topics
  • Introduction to ELK Stack
    • Overview of ELK Stack: Elasticsearch, Logstash, Kibana
    • Role of Each Component in the Stack
    • Use Cases for Log Management and Analytics
  • Elasticsearch Fundamentals
    • Introduction to Elasticsearch: Key Concepts and Features
    • Indexing and Searching Data
    • Cluster Architecture and Node Roles
  • Logstash Basics
    • Introduction to Logstash: Overview and Architecture
    • Logstash Configuration and Pipelines
    • Input, Filter, and Output Plugins
  • Kibana Introduction
    • Overview of Kibana: Features and Use Cases
    • Connecting Kibana to Elasticsearch
    • Exploring Kibana Dashboards and Visualizations
  • Log Ingestion with Logstash
    • Setting Up Logstash for Log Ingestion
    • Parsing and Enriching Log Data
    • Filtering and Transformation in Logstash
  • Elasticsearch Querying and Mapping
    • Query DSL in Elasticsearch
    • Index Mapping and Settings
    • Aggregations and Search Techniques
  • Advanced Logstash Configuration
    • Using Logstash Conditionals
    • Handling Time and Date in Logstash
    • Grok Patterns and Custom Pattern Creation
  • Creating Visualizations in Kibana
    • Introduction to Kibana Visualizations: Line Charts, Pie Charts, etc.
    • Creating Dashboards in Kibana
    • Adding Filters and Interactivity
  • Logstash for Advanced Data Processing
    • Using Logstash for Anomaly Detection
    • GeoIP Processing in Logstash
    • Introduction to Beats for Lightweight Data Shippers
  • Elasticsearch Index Management
    • Index Lifecycle Management (ILM)
    • Sharding and Replication
    • Index Optimization and Maintenance
  • Advanced Kibana Features
    • Canvas for Custom Visualizations
    • Timelion for Time-Series Data Analysis
    • Machine Learning Integration in Kibana
  • Security in ELK Stack
    • Introduction to Security Features in Elasticsearch
    • Configuring Secure Communication
    • User Authentication and Authorization in Kibana
  • ELK Stack Best Practices
    • Performance Tuning and Optimization
    • Scaling ELK Stack for Large Environments
    • Disaster Recovery and Backup Strategies
  • ELK Stack in Production
    • Monitoring and Alerting with the ELK Stack
    • High Availability and Fault Tolerance
    • Case Studies and Real-world Deployments
  • Community Resources and Next Steps
    • Exploring Documentation and Official Resources
    • Joining the Elastic Community
    • Contributing to the ELK Stack
    • Further Learning Paths and Advanced Topics
  • Introduction to Observability and OpenTelemetry
    • Overview of Observability in Modern Applications
    • Introduction to OpenTelemetry: Goals and Objectives
    • Role of OpenTelemetry in the Observability Landscape
  • OpenTelemetry Components and Architecture
    • Understanding the Components: SDKs, Instrumentation Libraries, and Collectors
    • OpenTelemetry Architecture: Tracers, Metrics, and Context Propagation
    • Instrumentation and Observability Data Collection
  • Installation and Basic Setup
    • Installing and Configuring OpenTelemetry SDKs
    • Setting Up Instrumentation for Different Languages
    • Configuring Exporters for Data Export
  • Tracing with OpenTelemetry
    • Introduction to Distributed Tracing
    • Tracing in OpenTelemetry: Spans, Traces, and Context Propagation
    • Integrating OpenTelemetry with Applications
  • Metrics with OpenTelemetry
    • Introduction to Observability Metrics
    • Instrumenting Code for Metrics with OpenTelemetry
    • Configuring and Exporting Metrics Data
  • Context Propagation and Baggage
    • Understanding Context Propagation
    • Leveraging Baggage for Passing Data Across Requests
    • Best Practices for Context Propagation
  • Sampling and Trace Configuration
    • Importance of Sampling in Distributed Tracing
    • Configuring Sampling Strategies in OpenTelemetry
    • Sampling Techniques and Considerations
  • OpenTelemetry Instrumentation Libraries
    • Overview of Instrumentation Libraries for Popular Frameworks and Libraries
    • Using Instrumentation Libraries for Common Services
    • Custom Instrumentation for Specialized Applications
  • OpenTelemetry and Service Mesh
    • Integrating OpenTelemetry with Service Mesh (e.g., Istio)
    • Observability in Microservices Environments
    • Tracing and Metrics in Service Mesh Deployments
  • Exporters and Data Export
    • Overview of Exporters in OpenTelemetry
    • Configuring Exporters for Various Backends (e.g., Jaeger, Master in Observability Engineering (MOE))
    • Exporting Data to Third-Party Observability Platforms
  • OpenTelemetry and Cloud-Native Environments
    • Observability in Cloud-Native Architectures
    • Using OpenTelemetry with Kubernetes and Container Orchestration
    • Best Practices for Observability in Cloud-Native Deployments
  • OpenTelemetry Best Practices
    • Performance Optimization and Overhead Considerations
    • Best Practices for Efficient Data Collection
    • Security Considerations in OpenTelemetry Deployments
  • Community Resources and Next Steps
    • Exploring Documentation and Official Resources
    • Joining the OpenTelemetry Community
    • Contributing to OpenTelemetry
    • Further Learning Paths and Advanced Topics
  • Introduction to Grafana
    • Overview of Grafana: Features and Use Cases
    • Grafana's Role in the Observability Stack
    • Key Concepts: Dashboards, Panels, and Data Sources
  • Installing and Configuring Grafana
    • Installing Grafana on Different Operating Systems
    • Basic Configuration: Data Directory, Ports, and Authentication
    • Securing Grafana: Users, Roles, and Permissions
  • Exploring the Grafana Interface
    • Dashboard Navigation and Layout
    • Adding and Configuring Panels
    • Time Range Selection and Dashboard Variables
  • Data Sources in Grafana
    • Introduction to Data Sources
    • Configuring and Adding Data Sources in Grafana
    • Supported Data Source Types: Master in Observability Engineering (MOE), InfluxDB, Elasticsearch, etc.
  • Creating Basic Visualizations
    • Line Charts, Bar Graphs, and Single Stat Panels
    • Configuring Visualization Options
    • Annotations and Thresholds
  • Templating and Variables
    • Using Variables for Dynamic Dashboards
    • Templating in Queries and Dashboards
    • Creating Reusable Dashboards with Templating
  • Advanced Visualization Techniques
    • Gauge and Table Panels
    • Heatmaps and World Maps
    • Plugins for Additional Visualization Options
  • Dashboard Plugins and Apps
    • Introduction to Plugins and Apps
    • Installing and Configuring Plugins
    • Exploring Popular Community Plugins
  • Alerting in Grafana
    • Setting Up Alerts for Panels
    • Configuring Notification Channels
    • Alerting Best Practices
  • Grafana and Time Series Databases
    • Integrating Grafana with Time Series Databases
    • Configuring Queries for Time Series Data
    • Visualizing Metrics and Time Series Data
  • Grafana and Logs
    • Integrating Grafana with Log Data
    • Configuring Logs as Data Sources
    • Building Dashboards with Log Data
  • Dashboards and Panels Best Practices
    • Designing Effective Dashboards
    • Layout, Styling, and Theming
    • Optimizing Dashboards for Performance
  • Grafana in Production
    • Backup and Restore Strategies
    • High Availability and Scaling Grafana
    • Monitoring Grafana Performance
  • Integrating Grafana with Other Tools
    • Grafana and Master in Observability Engineering (MOE) Integration
    • Grafana with InfluxDB, Elasticsearch, and Other Data Sources
    • Using Grafana in a Multi-Tool Observability Stack
  • Community Resources and Next Steps
    • Exploring Documentation and Official Resources
    • Joining the Grafana Community
    • Contributing to Grafana
    • Further Learning Paths and Advanced Topics
  • Introduction to Amazon CloudWatch
    • Overview of CloudWatch: Purpose and Key Features
    • Role of CloudWatch in AWS Monitoring and Management
    • Understanding CloudWatch Metrics, Logs, Events, and Insights
  • CloudWatch Metrics and Alarms
    • Introduction to CloudWatch Metrics
    • Creating Custom Metrics
    • Configuring CloudWatch Alarms for Metric Thresholds
    • Using Metric Math for Advanced Monitoring
  • CloudWatch Logs
    • Overview of CloudWatch Logs
    • Configuring Log Groups and Log Streams
    • Ingesting Log Data from EC2 Instances and Other Sources
    • Querying Logs with CloudWatch Insights
  • CloudWatch Events
    • Introduction to CloudWatch Events
    • Creating Rules and Targets
    • Integrating CloudWatch Events with Lambda Functions
    • Scenario-based Use Cases for CloudWatch Events
  • CloudWatch Dashboards
    • Creating and Customizing Dashboards
    • Adding Metrics, Logs, and Text Widgets to Dashboards
    • Sharing Dashboards and Setting Permissions
  • CloudWatch Synthetics
    • Overview of CloudWatch Synthetics
    • Configuring Canaries for Monitoring Endpoints
    • Monitoring API Endpoints and User Flows
  • CloudWatch Anomaly Detection
    • Configuring Anomaly Detection for Metrics
    • Understanding Anomaly Detection Algorithms
    • Creating and Managing Anomaly Detection Alarms
  • CloudWatch Contributor Insights
    • Introduction to Contributor Insights
    • Analyzing High-Cardinality Data
    • Creating and Customizing Contributor Insights Rules
  • CloudWatch Logs Insights
    • Leveraging CloudWatch Logs Insights for Log Analysis
    • Writing Queries for Log Data
    • Visualizing Log Data in Insights
  • CloudWatch Container Insights
    • Monitoring Containers in Amazon ECS and EKS
    • Integrating CloudWatch with Containerized Environments
    • Analyzing Container Performance and Logs
  • CloudWatch and AWS Integrations
    • CloudWatch Integration with AWS Services (e.g., EC2, RDS)
    • Using CloudWatch Metrics for Billing and Cost Monitoring
    • Integrating CloudWatch with AWS Config
  • CloudWatch API and CLI
    • Interacting with CloudWatch Using the AWS CLI
    • Automating CloudWatch Operations with the AWS SDKs
    • Exploring CloudWatch API Reference and Documentation
  • Best Practices for CloudWatch
    • Designing Efficient and Cost-Effective Monitoring
    • Security Best Practices for CloudWatch
    • Implementing Tagging and Resource Organization
  • Troubleshooting with CloudWatch
    • Common Issues and Error Messages
    • Debugging and Diagnosing Problems in CloudWatch
    • Utilizing CloudWatch Logs for Troubleshooting
  • Community Resources and Next Steps
    • Exploring CloudWatch Documentation and AWS Resources
    • Joining the AWS Community and Forums
    • Staying Updated with CloudWatch Releases
    • Advanced Topics and Further Learning Paths
  • Introduction to Azure Monitor
    • Overview of Azure Monitor: Purpose and Key Features
    • Role of Azure Monitor in Azure Management and Monitoring
    • Understanding Metrics, Logs, and Diagnostics in Azure Monitor
  • Azure Monitor Metrics
    • Introduction to Azure Monitor Metrics
    • Azure Monitor Metrics Explorer
    • Configuring and Viewing Metrics for Azure Resources
    • Creating Alerts Based on Metrics
  • Azure Monitor Logs
    • Overview of Azure Monitor Logs (Log Analytics)
    • Configuring Log Analytics Workspaces
    • Ingesting and Querying Log Data
    • Creating and Customizing Log Queries
  • Azure Monitor Application Insights
    • Introduction to Application Insights
    • Instrumenting Applications for Telemetry
    • Monitoring Performance, Errors, and Dependencies
    • Analyzing Application Insights Data
  • Azure Monitor Alerts
    • Creating Alerts in Azure Monitor
    • Configuring Alert Rules and Conditions
    • Integrating Alerts with Action Groups
    • Managing and Responding to Alerts
  • Azure Monitor Diagnostics
    • Introduction to Azure Diagnostics
    • Configuring Diagnostic Settings for Azure Resources
    • Sending Diagnostics Data to Azure Monitor
    • Visualizing Diagnostic Data in Azure Monitor
  • Azure Monitor Dashboards
    • Creating and Customizing Dashboards in Azure Monitor
    • Adding Metrics, Logs, and Insights to Dashboards
    • Sharing Dashboards and Setting Permissions
  • Module 8: Azure Monitor Autoscale
    • Overview of Azure Monitor Autoscale
    • Configuring Autoscale Rules for Azure Resources
    • Scaling Resources Based on Metrics
  • Azure Monitor Service Map
    • Understanding Service Map in Azure Monitor
    • Mapping Dependencies Between Azure Resources
    • Analyzing and Troubleshooting Application Performance
  • Azure Monitor for Containers
    • Monitoring Containers in Azure Kubernetes Service (AKS)
    • Integrating Azure Monitor with Containerized Environments
    • Analyzing Container Performance and Logs
  • Azure Monitor and Azure Security Center Integration
    • Leveraging Azure Monitor Data for Security Monitoring
    • Integrating Azure Security Center with Azure Monitor
    • Using Logs and Metrics for Security Insights
  • Azure Monitor and Azure DevOps Integration
    • Integrating Azure Monitor with Azure DevOps
    • Monitoring Application Performance in CI/CD Pipelines
    • Using Azure Monitor for DevOps Insights
  • Azure Monitor API and CLI
    • Interacting with Azure Monitor Using Azure CLI
    • Automating Azure Monitor Operations with Azure SDKs
    • Exploring Azure Monitor API Reference and Documentation
  • Best Practices for Azure Monitor
    • Designing Efficient and Cost-Effective Monitoring
    • Security Best Practices for Azure Monitor
    • Implementing Tagging and Resource Organization
  • Troubleshooting with Azure Monitor
    • Common Issues and Error Messages
    • Debugging and Diagnosing Problems in Azure Monitor
    • Utilizing Logs and Diagnostics for Troubleshooting
  • Community Resources and Next Steps
    • Exploring Azure Monitor Documentation and Microsoft Resources
    • Joining the Azure Community and Forums
    • Staying Updated with Azure Monitor Releases
    • Advanced Topics and Further Learning Paths
  • Introduction to Datadog
    • Overview of Datadog: Purpose and Key Features
    • Datadog's Role in Observability and Monitoring
    • Understanding Metrics, Logs, Traces, APM, and Security in Datadog
  • Datadog Metrics
    • Introduction to Datadog Metrics
    • Configuring and Sending Metrics to Datadog
    • Exploring Metrics Explorer
    • Creating Alerts Based on Metrics
  • Datadog Logs
    • Overview of Datadog Logs
    • Configuring Log Collection and Ingestion
    • Searching and Analyzing Logs in Datadog
    • Creating Log-based Alerts and Monitors
  • Datadog Traces
    • Introduction to Datadog Traces
    • Instrumenting Applications for Distributed Tracing
    • Tracing Requests and Dependencies
    • Analyzing Traces in Datadog APM
  • Datadog APM (Application Performance Monitoring)
    • Overview of Datadog APM
    • Configuring APM Agents for Various Languages
    • Analyzing Application Performance Metrics
    • Troubleshooting Performance Issues with APM
  • Datadog Security Monitoring
    • Introduction to Datadog Security Monitoring
    • Configuring Security Monitoring Rules
    • Detecting and Investigating Security Incidents
    • Integrating Datadog with Security Information and Event Management (SIEM) Systems
  • Datadog Dashboards
    • Creating and Customizing Dashboards in Datadog
    • Adding Metrics, Logs, and Traces to Dashboards
    • Sharing Dashboards and Setting Permissions
  • Datadog Monitors and Alerts
    • Creating Monitors and Alerts in Datadog
    • Configuring Alerting Policies and Notification Channels
    • Incident Management and Collaboration
  • Datadog Synthetics
    • Overview of Datadog Synthetics
    • Configuring Synthetic Checks for Website Monitoring
    • Analyzing Synthetic Test Results
    • Proactive Monitoring and Alerting with Synthetics
  • Datadog Infrastructure Monitoring
    • Monitoring Infrastructure with Datadog
    • Integrating Datadog with Cloud Providers (e.g., AWS, Azure)
    • Visualizing and Analyzing Infrastructure Metrics
  • Datadog Logs and Security Analytics
    • Analyzing Logs for Security Insights
    • Leveraging Logs for Incident Investigation
    • Integrating Datadog with Security Analytics Tools
  • Datadog Continuous Profiler
    • Introduction to Datadog Continuous Profiler
    • Profiling Applications for Performance Optimization
    • Analyzing Profiler Data in Datadog
  • Datadog and Container Orchestration
    • Monitoring Containerized Environments with Datadog
    • Integrating Datadog with Kubernetes and Docker
    • Visualizing and Analyzing Container Metrics
  • Datadog API and CLI
    • Interacting with Datadog Using the Datadog API
    • Automating Datadog Operations with Datadog CLI
    • Exploring Datadog API Documentation
  • Best Practices for Datadog
    • Designing Efficient and Cost-Effective Monitoring
    • Security Best Practices for Datadog
    • Implementing Tagging and Resource Organization
  • Troubleshooting with Datadog
    • Common Issues and Error Messages
    • Debugging and Diagnosing Problems in Datadog
    • Utilizing Datadog Logs, Metrics, and Traces for Troubleshooting
  • Community Resources and Next Steps
    • Exploring Datadog Documentation and Community Resources
    • Joining the Datadog Community and Forums
    • Staying Updated with Datadog Releases
    • Advanced Topics and Further Learning Paths
  • Introduction to New Relic
    • Overview of New Relic: Purpose and Key Features
    • New Relic's Role in Observability and Monitoring
    • Understanding APM, Logs, and Infrastructure Monitoring in New Relic
  • New Relic APM
    • Introduction to New Relic APM
    • Configuring Agents for Various Languages
    • Analyzing Application Performance Metrics
    • Troubleshooting Performance Issues with APM
  • New Relic Logs
    • Overview of New Relic Logs
    • Configuring Log Collection and Ingestion
    • Searching and Analyzing Logs in New Relic
    • Creating Log-based Alerts and Monitors
  • New Relic Infrastructure Monitoring
    • Monitoring Infrastructure with New Relic
    • Integrating New Relic with Cloud Providers (e.g., AWS, Azure)
    • Visualizing and Analyzing Infrastructure Metrics
  • New Relic Synthetics
    • Overview of New Relic Synthetics
    • Configuring Synthetic Checks for Website Monitoring
    • Analyzing Synthetic Test Results
    • Proactive Monitoring and Alerting with Synthetics
  • New Relic Dashboards
    • Creating and Customizing Dashboards in New Relic
    • Adding Metrics, Logs, and Traces to Dashboards
    • Sharing Dashboards and Setting Permissions
  • New Relic Alerts
    • Creating Alerts in New Relic
    • Configuring Alert Policies and Notification Channels
    • Incident Management and Collaboration
  • New Relic One
    • Introduction to New Relic One: A Full-Stack Observability Platform
    • Leveraging New Relic One for Unified Observability
    • Building Custom Applications and Dashboards
  • New Relic Insights
    • Overview of New Relic Insights
    • Querying and Analyzing Data in Insights
    • Creating Custom Dashboards with Insights
  • New Relic Mobile Monitoring
    • Monitoring Mobile Applications with New Relic
    • Configuring Mobile Agents
    • Analyzing Performance and User Experience for Mobile Apps
  • New Relic Browser Monitoring
    • Introduction to New Relic Browser Monitoring
    • Configuring Browser Agents for Web Applications
    • Analyzing User Sessions and Performance Metrics
  • New Relic and Container Orchestration
    • Monitoring Containerized Environments with New Relic
    • Integrating New Relic with Kubernetes and Docker
    • Visualizing and Analyzing Container Metrics
  • New Relic API and CLI
    • Interacting with New Relic Using the REST API
    • Automating New Relic Operations with the CLI
    • Exploring New Relic API Documentation
  • Best Practices for New Relic
    • Designing Efficient and Cost-Effective Monitoring
    • Security Best Practices for New Relic
    • Implementing Tagging and Resource Organization
  • Troubleshooting with New Relic
    • Common Issues and Error Messages
    • Debugging and Diagnosing Problems in New Relic
    • Utilizing New Relic Logs, Metrics, and Traces for Troubleshooting
  • Community Resources and Next Steps
    • Exploring New Relic Documentation and Community Resources
    • Joining the New Relic Community and Forums
    • Staying Updated with New Relic Releases
    • Advanced Topics and Further Learning Paths

OUR COURSE IN COMPARISON

FEATURES DEVOPSSCHOOL OTHERS
Master in Observability Engineering (MOE)
Faculty Profile Check
Lifetime Technical Support
Lifetime LMS access
Real-time interview questions
Master in Observability Engineering (MOE) with Grafana
  • There is a huge future scope and career growth for those who opt for this technology.
  • Certification in Master in Observability Engineering (MOE) will show a wide range of opportunities for job seekers as well as professionals.
  • It has a demanding profile and most of the companies are looking for certified professionals in this field.
  • You should have some familiarity with monitoring tools.
  • Experience using a Terminal, Git, Bash, or Shell would be beneficial but not essential.
  • Basic of linux and windows

Need Assistance

Feel Free To Contact Us -
+91 84094 92687
(INDIA)
+1 (469) 756-6329
(USA)
For More Queries-
Contact@DevOpsSchool.com

Master in Observability Engineering (MOE) CERTIFICATION

What are the benefits of "Master in Observability Engineering (MOE)" Certification?

Certification can also help play a vital role in displaying your technical skills and gain the career of your dreams. Moreover, it shows your talent to join the real-time projects. Many companies are preferring candidates with certification and also provide adequate salary packages.

This certification emphasizes the fundamentals of claims bundling and the distinction of claims associated with traditional programs due to potentially preventable complications. Certification sets you apart from non-certified peers, and you can demand the best salaries at leading companies.

DevOpsSchool Certificates

FREQUENTLY ASKED QUESTIONS

You can go through the sample class recording and it would give you a clear insight about how the classes are conducted, quality of instructors and you can ensure that you are in right place. We ensure you will be getting complete worth of your money by assigning the best instructor in that technology.

Our instructors working professionals from the Industry and have at least 10-12 yrs of relevant experience in various domains inculding Devops and who understands how to convey things in technical as well as subject matter experts.

The system requirements include Windows / Mac / Linux PC, Minimum 2GB RAM and 20 GB HDD Storage with Windows/CentOS/Redhat/Ubuntu/Fedora.

The trainer will help you setup the instance in cloud (AWS, Cloudshare & Azure), the same VMs can be used in this training and we make sure you acquire practical hands-on training by providing you with every utility that is needed for your understanding of the course.

Also, We will provide you with step-wise installation guide to set up the Virtual Box Cent OS environment on your system which will be used for doing the hands-on exercises, assignments, etc.

Payments can be made using any of the following options:-

  • NEFT or IMPS from all leading Banks.
  • Debit card/Credit card
  • Google Pay/Paytm
  • Xoom and Paypal (For USD Payments)

No worries. It might happen. You have two options here.

Each session is uploaded in the LMS where you can review the missed session. You will get 3 months live session access. You can attend the missed session, in any other live batch free of cost upto 3 months.

We offer this course in “Live Instructor-Led Online Training” mode. Through this way you won’t mess anything in your real-life schedule. You will be shared with live meeting access while your session starts using GoToMeeting.

Once you successfully complete the training program along with all the real-world projects, quizzes and assignments you will be awarded the DevOpsSchool verified certification which is industry recognized and does holds value.

No, But we help you to get prepared for the interview. Since there is a big demand for this skill, we help our students for resumes preparations, work on real life projects and provide assistance for interview preparation.

You can avail the online training reciept if you pay us via Paypal You can also ask for send you the scan of the fees receipt.

DEVOPSSCHOOL ONLINE TRAINING REVIEWS

Avatar

Abhinav Gupta, Pune

(5.0)

The training was very useful and interactive. Rajesh helped develop the confidence of all.

indrayani

Indrayani, India

(5.0)

Rajesh is very good trainer. Rajesh was able to resolve our queries and question effectively. We really liked the hands-on examples covered during this training program.

ravi daur

Ravi Daur , Noida

(5.0)

Good training session about basic Devops concepts. Working session were also good, howeverproper query resolution was sometimes missed, maybe due to time constraint.

sumit

Sumit Kulkarni, Software Engineer

(5.0)

Very well organized training, helped a lot to understand the DevOps concept and detailed related to various tools.Very helpful

vinaya

Vinayakumar, Project Manager, Bangalore

(5.0)

Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.


kshitiij gupta

Abhinav Gupta, Pune

(5.0)

The training with DevOpsSchool was a good experience. Rajesh was very helping and clear with concepts. The only suggestion is to improve the course content.

View more

4.1
Google Ratings
4.1
Videos Reviews
4.1
Facebook Ratings

Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.

Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.

Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.

Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.

Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.

Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.

prev next

RELATED COURSE

RELATED BLOGS

OUR GALLERY

DevOpsSchool
Typically replies within an hour

DevOpsSchool
Hi there 👋

How can I help you?
×
Chat with Us

  DevOpsSchool is offering its industry recognized training and certifications programs for the professionals who are seeking to get certified for DevOps Certification, DevSecOps Certification, & SRE Certification. All these certification programs are designed for pursuing a higher quality education in the software domain and a job related to their field of study in information technology and security.


  • Bangalore :-
    DevOpsSchool Training Venue (Vervenest Technologies Private Limited) 3478J HAL 2ND Stage, Chirush Mansion, 2nd & 3rd Floors, 13th Main Road, HAL 2nd Stage,Indiranagar, Bengaluru, Karnataka 560008
  • Hyderabad :-
    DevOpsSchol Training Venue(Palmeto Solutions) 8th floor, Vaishnavi Cynosure,Telecom Nagar, Gachibowli, Telangana -500032 Land Mark : Reliance Digital Building, Next to Gachibowli Flyover.
TRENDING COURSES

COMPANY
SUPPORT
Home
Terms & Conditions
Privacy Policy
Copyright © 2019 DevOpsSchool Inc All Rights Reserved