Technologies / Data & Analytics

Apache Hama - large-scale graph processing

Specialized training on graph processing in a distributed environment using Apache Hama. The program covers the foundations of graph theory, implementation of graph algorithms and techniques for optimizing computation in a Big Data environment. During the workshop, participants work with real graph datasets, learning to design and implement effective analytical solutions. The training combines mathematical theory with practical implementation aspects, focusing on the performance and scalability of solutions.

Issues

Graph theory
BSP model
Graph algorithms
Distributed processing
Performance optimization
Network analysis
Data partitioning
Design Patterns
Monitoring of systems
Graph visualization
Load balancing
Integration of systems

Benefits

Deep understanding of graph processing principles in a distributed environment
Practical knowledge of the implementation of graph algorithms in Apache Hama
Ability to design scalable solutions for analyzing large graphs
Knowledge of graph computing optimization techniques
Experience in implementing real-world graph analytics use cases
Ability to effectively integrate with the Big Data ecosystem

Who is this training for?

Big Data application developers

Data scientists

Distributed systems engineers

Graph analytics specialists

Architects of Big Data solutions

Analytical application developers

Machine learning engineers

Prerequisites

Knowledge of graph theory and algorithms
Java programming experience
Basic knowledge of distributed systems
Understanding the concept of Big Data

Training program

BSP computational model
Apache Hama Architecture
Design of graph algorithms
Implementation of algorithms
Graph search algorithms
Calculation of centrality measures

Path analysis
Optimization and scaling

Caching techniques

Load balancing strategies
Integration and implementation
Integration with the Hadoop ecosystem
Monitoring and diagnostics

Deployment in a production environment

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

What are the prerequisites for this training?

For Apache Hama - large-scale graph processing we recommend: Knowledge of graph theory and algorithms; Java programming experience; Basic knowledge of distributed systems.

What is the format and duration of this training?

The training lasts 2 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Big Data application developers; Data scientists; Distributed systems engineers.

What practical skills will I gain from this training?

You will gain deep understanding of graph processing principles in a distributed environment, practical knowledge of implementing graph algorithms in Apache Hama, the ability to design scalable solutions for analyzing large graphs, knowledge of graph computing optimization techniques, and experience in implementing real-world graph analytics use cases.

Do I receive a certificate after completing this training?

Yes, upon successful completion you receive an EITT certificate confirming your skills in Apache Hama and large-scale graph processing. The certificate is recognized by employers in the IT industry.

What types of graph problems can be solved with Apache Hama?

Apache Hama implements the Bulk Synchronous Parallel (BSP) computing model, making it suitable for graph algorithms such as shortest path computation (Dijkstra, Bellman-Ford), PageRank, connected components, and graph partitioning. The training demonstrates these algorithms on real-world datasets such as social networks and infrastructure graphs.