Edge Computing For Big Data Training Course: Edge Network Data Processing

Introduction

Revolutionize your data processing capabilities with our Edge Computing for Big Data Training Course. This program is designed to equip you with the essential skills to process Big Data at the edge of the network, enabling you to build highly efficient and responsive data applications. In today's IoT and real-time data-driven world, mastering edge computing is crucial for minimizing latency and maximizing data processing efficiency. Our edge computing training course provides hands-on experience and expert guidance, empowering you to build robust edge solutions.

This Big Data edge computing training delves into the core concepts of edge computing, covering topics such as distributed data processing, edge device management, and real-time analytics. You'll gain expertise in using industry-standard tools and techniques to process Big Data at the edge of the network that meets the demands of modern data environments. Whether you're a data engineer, IoT developer, or system architect, this edge computing course will empower you to build powerful edge solutions.

Target Audience:

• Data Engineers
• IoT Developers
• System Architects
• Cloud Engineers
• Big Data Developers
• Network Engineers
• Anyone needing edge computing skills

Course Objectives:

• Understand the fundamentals of edge computing for Big Data.
• Master distributed data processing at the edge of the network.
• Utilize edge devices and gateways for data collection and processing.
• Implement real-time analytics and data filtering at the edge.
• Design scalable and efficient edge computing architectures.
• Utilize cloud-edge integration for seamless data flow.
• Troubleshoot and optimize edge computing deployments.
• Implement data security and access control in edge environments.
• Integrate edge computing with IoT and cloud platforms.
• Understand how to monitor and manage edge computing systems.
• Explore advanced edge computing patterns and techniques.
• Apply real world use cases for edge computing in Big Data.
• Utilize containerization and orchestration at the edge.

Duration

10 Days

Course content

Module 1: Introduction to Edge Computing for Big Data

• Fundamentals of edge computing and its benefits.
• Overview of edge computing architectures and patterns.
• Setting up an edge computing development environment.
• Introduction to edge computing tools and frameworks.
• Best practices for edge computing.

Module 2: Distributed Data Processing at the Edge

• Implementing distributed data processing techniques.
• Utilizing edge devices for local data processing.
• Implementing data aggregation and filtering at the edge.
• Designing scalable and efficient edge data pipelines.
• Best practices for distributed processing.

Module 3: Edge Devices and Gateways

• Utilizing edge devices for data collection and processing.
• Implementing edge gateways for data aggregation and routing.
• Managing edge device configurations and updates.
• Implementing edge device security measures.
• Best practices for edge devices.

Module 4: Real-Time Analytics at the Edge

• Implementing real-time analytics at the edge.
• Utilizing stream processing engines for edge data.
• Implementing data filtering and anomaly detection.
• Building real-time dashboards for edge data.
• Best practices for real-time analytics.

Module 5: Scalable Edge Computing Architectures

• Designing scalable edge computing architectures.
• Implementing edge data caching and storage.
• Utilizing edge clusters for distributed processing.
• Optimizing edge applications for performance.
• Best practices for scalability.

Module 6: Cloud-Edge Integration

• Integrating edge computing with cloud platforms.
• Utilizing cloud services for edge data management.
• Implementing seamless data flow between edge and cloud.
• Utilizing cloud-based edge orchestration tools.
• Best practices for cloud-edge integration.

Module 7: Troubleshooting and Optimization

• Debugging edge computing deployments.
• Analyzing edge system logs and metrics.
• Utilizing monitoring tools and dashboards.
• Optimizing edge application performance.
• Best practices for troubleshooting.

Module 8: Data Security and Access Control

• Implementing data security in edge environments.
• Utilizing authentication and authorization.
• Implementing data encryption and masking.
• Implementing data auditing and compliance.
• Best practices for data security.

Module 9: Integration with IoT and Cloud Platforms

• Integrating edge computing with IoT platforms.
• Utilizing cloud-based IoT services.
• Implementing edge-to-cloud data pipelines.
• Best practices for IoT and cloud integration.

Module 10: Monitoring and Management

• Monitoring edge computing system performance and health.
• Implementing alerting and notifications.
• Utilizing monitoring tools and techniques.
• Implementing edge system maintenance strategies.
• Best practices for monitoring.

Module 11: Advanced Edge Computing Patterns

• Implementing federated learning at the edge.
• Utilizing edge computing for AI inference.
• Implementing edge-based data compression and encoding.
• Advanced techniques for edge processing.
• Best practices for advanced patterns.

Module 12: Real-World Use Cases

• Implementing edge computing for industrial IoT.
• Utilizing edge computing for smart cities.
• Implementing edge computing for autonomous vehicles.
• Utilizing edge computing for healthcare applications.
• Best practices for real world applications.

Module 13: Containerization and Orchestration at the Edge

• Utilizing containerization for edge applications.
• Implementing Kubernetes for edge orchestration.
• Managing containerized edge deployments.
• Best practices for containerization.

Module 14: Edge Computing and Data Governance

• Implementing data governance policies in edge environments.
• Utilizing metadata management tools.
• Implementing data lineage and data dictionary.
• Best practices for data governance.

Module 15: Future Trends in Edge Computing for Big Data

• Emerging trends in edge computing.
• Utilizing AI and automation in edge computing.
• Implementing serverless edge computing.
• Best practices for future edge computing.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.