Microservices Architecture: Designing, Deploying and Managing in a Cloud Native Environment

Why choose this training?

Microservices architecture is the dominant approach to building modern cloud-native applications, offering independent scaling, technological flexibility and the ability to deliver business value faster through autonomous development teams. The training introduces participants to the practical aspects of designing, deploying and managing microservice systems — from fundamental principles and philosophy, through key design patterns such as API Gateway, service mesh, Circuit Breaker and the Saga pattern, to containerization and orchestration with Docker and Kubernetes. The program covers both inter-service communication strategies, from REST and gRPC to message queues and event-driven architecture, and data management in a distributed environment using the database per service pattern and eventual consistency. Participants learn the practical aspects of deploying microservices in the cloud, from deployment strategies and rolling updates, through service mesh with Istio, to CI/CD pipelines dedicated to distributed architectures. Particular emphasis is placed on microservices security, implementing OAuth2 and JWT, and the monitoring and observability needed to manage distributed systems. The workshops are based on real-world scenarios in which participants build a complete microservice system from scratch.

What makes our approach unique?

EITT offers intensive workshops led by architects and senior developers with practical experience in building microservice systems in cloud-native production environments. Our trainings focus on hands-on experience: each participant implements a real microservice system, containerizes it with Docker, deploys it to Kubernetes and configures a CI/CD pipeline. The sessions use modern tools and platforms, from RabbitMQ and Apache Kafka, through Kubernetes, to service mesh and monitoring tools. The trainers share proven design patterns, techniques for debugging distributed systems and the observability tools used in commercial projects. The training materials include the complete source code of a sample system, Kubernetes manifests, pipeline definitions and best-practice checklists for various scenarios.