15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
- Solutions architects
- Software engineers
- Machine learning engineers
- AI engineers
- DevOps engineers
- Platform engineers
- Network engineers
- Systems architects
- Big data professionals
- Digital transformation professionals
- IT and technology managers
- Professionals responsible for developing distributed, IoT, or real-time data processing solutions
Session Objectives
- Understand the fundamentals of edge data processing.
- Master distributed data processing at the edge.
- Utilize edge device management and orchestration tools.
- Implement real-time data analytics at the edge.
- Design and build scalable edge computing architectures.
- Optimize edge data processing for low latency and high efficiency.
- Troubleshoot and address common issues in edge deployments.
- Implement data security and privacy in edge environments.
- Integrate edge data processing with cloud and on-premises systems.
- Understand how to handle data synchronization and communication in edge networks.
- Explore advanced edge computing techniques (e.g., federated learning, edge AI).
- Apply real world use cases for edge data processing.
- Leverage edge data processing tools and frameworks for efficient development.
About the Course
This advanced training equips participants with the knowledge and practical skills required to design, implement, and manage data processing solutions at the edge of the network. The course explores how edge computing enables organizations to process, analyze, and act on data closer to where it is generated, reducing latency, bandwidth consumption, and dependence on centralized cloud infrastructure.Participants will learn edge data architectures, real-time processing, IoT data pipelines, distributed analytics, edge AI, data synchronization, security, resource optimization, and cloud-edge integration. The training emphasizes building resilient and scalable edge processing environments for applications that require rapid decision-making and continuous operations.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of edge data processing.
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Workshop 1.2:
Overview of distributed data processing and edge computing concepts.
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Workshop 1.3:
Setting up an edge data processing development environment.
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Workshop 1.4:
Introduction to edge computing tools and frameworks.
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Workshop 1.5:
Best practices for edge data processing.
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Workshop 2.1:
Mastering distributed data processing at the edge.
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Workshop 2.2:
Utilizing distributed computing frameworks for edge devices.
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Workshop 2.3:
Implementing data partitioning and aggregation at the edge.
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Workshop 2.4:
Designing and building distributed data pipelines.
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Workshop 2.5:
Best practices for distributed processing.
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Workshop 3.1:
Utilizing edge device management and orchestration tools.
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Workshop 3.2:
Implementing device provisioning and configuration.
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Workshop 3.3:
Designing and building device management systems.
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Workshop 3.4:
Optimizing device management for remote operations.
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Workshop 3.5:
Best practices for device management.
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Workshop 4.1:
Implementing real-time data analytics at the edge.
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Workshop 4.2:
Utilizing stream processing for edge data.
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Workshop 4.3:
Designing and building real-time analytics applications.
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Workshop 4.4:
Optimizing analytics for low-latency insights.
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Workshop 4.5:
Best practices for real-time analytics.
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Workshop 5.1:
Designing and building scalable edge computing architectures.
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Workshop 5.2:
Utilizing containerization and orchestration for edge deployments.
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Workshop 5.3:
Implementing edge data storage and caching.
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Workshop 5.4:
Optimizing architectures for scalability and performance.
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Workshop 5.5:
Best practices for edge architectures.
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Workshop 6.1:
Optimizing edge data processing for low latency and high efficiency.
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Workshop 6.2:
Utilizing performance tuning and resource management.
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Workshop 6.3:
Implementing data compression and filtering.
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Workshop 6.4:
Designing efficient edge applications.
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Workshop 6.5:
Best practices for optimization.
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Workshop 7.1:
Troubleshooting and addressing common issues in edge deployments.
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Workshop 7.2:
Analyzing edge device logs and network issues.
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Workshop 7.3:
Utilizing problem-solving techniques for resolution.
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Workshop 7.4:
Resolving common edge computing errors.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data security and privacy in edge environments.
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Workshop 8.2:
Utilizing encryption and access control for edge data.
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Workshop 8.3:
Designing and building secure edge applications.
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Workshop 8.4:
Optimizing security for data protection.
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Workshop 8.5:
Best practices for security.
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Workshop 9.1:
Integrating edge data processing with cloud and on-premises systems.
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Workshop 9.2:
Utilizing data synchronization and communication protocols.
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Workshop 9.3:
Implementing edge-to-cloud data pipelines.
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Workshop 9.4:
Designing hybrid edge computing solutions.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle data synchronization and communication in edge networks.
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Workshop 10.2:
Utilizing messaging queues and data streaming.
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Workshop 10.3:
Implementing data replication and consistency.
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Workshop 10.4:
Designing efficient communication protocols.
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Workshop 10.5:
Best practices for synchronization.
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Workshop 11.1:
Exploring advanced edge computing techniques (federated learning, edge AI).
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Workshop 11.2:
Utilizing federated learning for distributed model training.
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Workshop 11.3:
Implementing edge AI for real-time inference.
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Workshop 11.4:
Designing and building advanced edge solutions.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing edge data processing for IoT sensor networks.
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Workshop 12.2:
Utilizing edge computing for autonomous vehicles.
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Workshop 12.3:
Implementing edge data processing for smart city applications.
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Workshop 12.4:
Utilizing edge computing for industrial automation.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing edge data processing tools and frameworks (EdgeX Foundry, KubeEdge).
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Workshop 13.2:
Implementing edge applications with specific tools.
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Workshop 13.3:
Designing and building automated deployment workflows.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing edge monitoring and logging.
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Workshop 14.2:
Utilizing edge monitoring tools and metrics.
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Workshop 14.3:
Designing and building performance dashboards.
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Workshop 14.4:
Optimizing monitoring for real-time insights.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in edge data processing.
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Workshop 15.2:
Utilizing AI for edge data analysis.
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Workshop 15.3:
Implementing serverless edge computing.
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Workshop 15.4:
Best practices for future applications.
