Observability Tools for Cloud Environments provides an in-depth exploration of monitoring and analytics solutions tailored for cloud infrastructures. Participants will engage with a variety of observability tools, gaining hands-on experience through project-based learning that emphasizes real-world applications. This course is structured to enhance participants’ ability to implement effective monitoring strategies, optimize performance, and troubleshoot issues within cloud environments.
The curriculum is designed to bridge the gap between theoretical knowledge and practical implementation. By collaborating on projects, participants will not only deepen their understanding of observability tools but also have the opportunity to publish their findings in Cademix Magazine, contributing to the broader professional community. This interactive approach ensures that learners develop the skills necessary to excel in the rapidly evolving landscape of cloud technology.
Introduction to Observability Concepts
Overview of Cloud Infrastructure and Services
Key Metrics and Logs in Cloud Environments
Tools for Metrics Collection (e.g., Prometheus, Grafana)
Distributed Tracing Techniques (e.g., Jaeger, Zipkin)
Log Management Solutions (e.g., ELK Stack, Splunk)
Setting Up Alerts and Notifications
Performance Monitoring and Optimization Strategies
Final Project: Implementing an Observability Solution in a Cloud Environment
Publishing Results and Insights in Cademix Magazine

Practical Use of Kibana for Visualization equips participants with the essential skills to effectively analyze and visualize data using Kibana, a powerful analytics and visualization platform. The course emphasizes hands-on, project-based learning, allowing participants to engage directly with real-world datasets and scenarios. By the end of the program, learners will be able to create insightful dashboards and visual representations that support data-driven decision-making in various business contexts.
The curriculum is structured to cover a comprehensive range of topics, ensuring that participants not only grasp the fundamental concepts of Kibana but also apply them in practical settings. Through interactive sessions, learners will collaborate on projects that culminate in tangible outputs, which can be showcased in Cademix Magazine. This approach fosters a deeper understanding of data visualization techniques and enhances participants’ portfolios, making them more competitive in the job market.
Introduction to Kibana and its Ecosystem
Setting Up the Kibana Environment
Exploring Data Sources: Elasticsearch Integration
Building Visualizations: Charts, Graphs, and Maps
Creating and Customizing Dashboards
Utilizing Kibana’s Query Language for Data Analysis
Implementing Filters and Drilldowns for Enhanced Insights
Monitoring Data in Real-Time with Kibana
Best Practices for Effective Data Visualization
Final Project: Develop a Comprehensive Dashboard for a Case Study

Advanced Techniques in Distributed Tracing equips participants with the essential skills and knowledge to implement and optimize distributed tracing systems within complex software architectures. The course emphasizes hands-on, project-based learning, allowing participants to engage deeply with real-world scenarios. By focusing on practical applications, attendees will gain insights into how distributed tracing improves system observability, performance monitoring, and troubleshooting processes.
Throughout the program, participants will explore advanced concepts and tools related to distributed tracing, including instrumentation techniques, context propagation, and integration with monitoring solutions. The course culminates in a final project where learners will design and implement a distributed tracing solution tailored to a specific use case, fostering an environment of innovation and practical application. Participants are encouraged to publish their findings and results in Cademix Magazine, contributing to the broader community of software engineering and DevOps professionals.
Syllabus:
Introduction to Distributed Tracing: Concepts and Importance
Key Tools and Frameworks for Distributed Tracing (e.g., OpenTracing, Jaeger)
Instrumentation Techniques for Microservices
Context Propagation and Correlation IDs
Integrating Distributed Tracing with Monitoring Solutions
Analyzing Trace Data for Performance Optimization
Troubleshooting Distributed Systems Using Tracing
Case Studies: Real-world Applications of Distributed Tracing
Final Project: Designing a Distributed Tracing Solution
Best Practices for Implementing Distributed Tracing in Production Environments