TRAINING COURSE
Java Microservices:
Innovating with Big Data
and Artificial Intelligence
STARTING TWO TIMES A MONTH
DURATION 8 WEEKS
‎ 9 000 €
A program for developers and engineers looking to deepen their knowledge of building microservices in Java, integrating big data, and using artificial intelligence. Students will learn how to develop and scale microservices, process real-time data, and implement machine learning models to improve the performance of their applications.
Microservices Architecture Mastery

Real-time Data Processing

AI Integration Techniques

Training Course Program
STARTING TWO TIMES A MONTH
Lecture 1:
Introduction to microservices and their architecture
  • Basic principles of microservice architecture
  • Advantages and disadvantages of microservices
  • Comparison with monolithic architecture
  • Tools and technologies for developing microservices in Java
Lecture 2:
Big data fundamentals: concepts and tools
  • Introduction to big data and its characterization (Volume, Velocity, Variety, Veracity)
  • Basic big data technologies and platforms (Hadoop, Spark, Kafka)
  • Data warehouses and databases for big data (HDFS, NoSQL, NewSQL)
Lecture 3:
Integrating AI into microservices
  • The role of AI in microservice architecture
  • Introduction to machine learning and its application in microservices
  • Tools and libraries for AI in Java (Deeplearning4j, Weka, MOA)
  • Examples of using AI to improve the performance of microservices
Lecture 4:
Developing and Deploying Microservices in Java
  • Creating microservices using Spring Boot and Spring Cloud
  • Managing microservice configurations
  • Service loggers and load balancing (Eureka, Ribbon)
  • Deployment and orchestration techniques for microservices (Docker, Kubernetes)
Lecture 5:
Real-time streaming data processing
  • Introduction to streaming data processing
  • Using Apache Kafka and Apache Flink to process data streams
  • Application of AI in stream processing (real cases)
  • Real-time data processing and analysis
Lecture 6:
Security and data protection in microservices
  • Fundamentals of microservices security
  • Authentication and authorization methods (OAuth2, JWT)
  • Data encryption and key management
  • Ensuring data confidentiality and integrity
Lecture 7:
Microservices for big data analytics
  • Using microservices for data analytics
  • Integrating analytical models into microservices
  • Examples of analytic microservices for different industries
  • Metrics and monitoring of analytic microservices
Lecture 8:
The future of microservices and big data using AI
  • The latest trends and technologies in microservices, big data and AI
  • Prospects and opportunities for developers
  • Discussion of innovative cases and projects
  • Summarizing the course and discussing questions from the audience
Practical Session 1:
Creating a simple microservice on Spring Boot
  • Creating a Spring Boot project using Spring Initializr
  • Developing a RESTful API with GET, POST, PUT, DELETE methods
  • Setting up and connecting to the H2 database
Practical Session 2:
Data processing using Apache Spark
  • Install and configure Apache Spark
  • Create a Spark application in Java to read and process data from CSV
  • Performing basic transformation operations and actions (map, filter, reduce)
Practical Session 3:
Integrating a machine learning model into a microservice
  • Train and export a machine learning model (e.g. linear regression)
  • Create a microservice on Spring Boot for predictions using the model
  • Integrating the model and implementing a RESTful API for predictions
Practical Session 4:
Deployment and management of microservices using Docker and Kubernetes
  • Creating a Dockerfile and building a Docker container for a microservice
  • Writing Kubernetes manifests (Deployment, Service)
  • Deploying the microservice in a Kubernetes cluster and setting up monitoring
What kind of result will you get
Participants in this course will be able to integrate artificial intelligence models into microservice architectures to improve efficiency and real-time analytics. Participants will also gain skills to securely deploy and manage microservices in cloud environments using Docker and Kubernetes.
Student feedback on the course
The course is perfectly structured and useful for professionals who want to master modern microservices and AI technologies. Lots of practice and examples!
Ann Blade
Perfect combination of theory and practice! Finally figured out how to combine big data and microservices with AI.
Helen Swansson
Excellent course for experienced developers: useful assignments, clear presentation and up-to-date tools. I recommend it!
Ben Gliss
What you will get after the course
  1. Skills in designing fault-tolerant microservices architecture for high load applications.
  2. Techniques for handling real-time data streams using Apache Kafka and Flink.
  3. Fundamentals of microservices security including authentication and authorisation.
  4. Development of RESTful APIs using Spring Boot for interaction between microservices.
  5. Using Hadoop and Spark to efficiently process and analyse big data.
Official school certificate
Recordings of all lectures
10% discount on any of our courses
Price without discount 10 000 €
9 000 €
Course fees
If you register early (over a month prior to the beginning of the course), you will receive a 10% discount on the entire program
Register for the course and get 10% off!
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"Cosmopolitacademy" LLC
264.110.1320023
TIN: 08256519
Komitas 19/1, Erevan, Armenia