Subject content:
Advanced techniques and architectures for processing massive datasets in distributed environments. Design and implementation of scalable applications using contemporary frameworks for parallel Big Data processing on computing clusters. Design patterns for distributed processing and their practical application in solving engineering problems. Techniques for building fault-tolerant systems ensuring data consistency in distributed environments. Implementation of machine learning algorithms on large-scale datasets, covering classification and regression models, recommender systems, and cross-validation. Design and deployment of real-time streaming analytics solutions. Practical skills in selecting appropriate platforms and tools for specific Big Data processing challenges, incorporating current knowledge in the field of distributed systems and large-scale data processing.