Kubernetes For Data Engineering Training Course: Deploy Data Workloads

Introduction

Revolutionize your data engineering infrastructure with our Kubernetes for Data Engineering Training Course. This program is designed to equip you with the essential skills to deploy and manage data engineering workloads on Kubernetes, enabling you to build scalable, resilient, and efficient data platforms. In today's cloud-native world, mastering Kubernetes for data engineering is crucial for organizations seeking to leverage container orchestration for their data pipelines. Our Kubernetes data engineering training course offers hands-on experience and expert guidance, empowering you to utilize Kubernetes for diverse data engineering tasks.

This deploy data workloads training delves into the core concepts of Kubernetes for data engineering, covering topics such as containerization, orchestration, and stateful application management. You'll gain expertise in using industry-standard Kubernetes tools and techniques to deploy and manage data engineering workloads on Kubernetes, meeting the demands of modern data-intensive organizations. Whether you're a data engineer, DevOps engineer, or cloud architect, this Kubernetes for Data Engineering course will empower you to design and implement high-performance data solutions on Kubernetes.

Target Audience:

• Data Engineers
• DevOps Engineers
• Cloud Architects
• Data Scientists
• Database Administrators
• System Administrators
• Anyone needing Kubernetes for data engineering skills

Course Objectives:

• Understand the fundamentals of Kubernetes for data engineering.
• Master containerization and deployment of data engineering tools.
• Utilize Kubernetes for orchestrating data pipelines and workflows.
• Implement stateful application management for databases and storage.
• Design and build scalable data engineering clusters on Kubernetes.
• Optimize Kubernetes configurations for data engineering workloads.
• Troubleshoot and address common issues in Kubernetes deployments.
• Implement data security and access control in Kubernetes environments.
• Integrate Kubernetes with various data storage and processing systems.
• Understand how to handle large datasets and data warehousing on Kubernetes.
• Explore advanced Kubernetes features for data engineering (e.g., operators, custom resources).
• Apply real world use cases for Kubernetes in data engineering.
• Leverage Kubernetes's ecosystem for efficient data engineering workflows.

Duration

10 Days

Course content

Module 1: Introduction to Kubernetes for Data Engineering

• Fundamentals of Kubernetes for data engineering.
• Overview of containerization, orchestration, and stateful applications.
• Setting up a Kubernetes development environment.
• Introduction to Kubernetes concepts and components.
• Best practices for Kubernetes data engineering.

Module 2: Containerization and Deployment

• Mastering containerization and deployment of data engineering tools.
• Utilizing Docker for container image creation.
• Implementing Kubernetes deployments and services.
• Designing and building containerized data engineering applications.
• Best practices for containerization.

Module 3: Orchestrating Data Pipelines

• Utilizing Kubernetes for orchestrating data pipelines and workflows.
• Implementing Kubernetes jobs and cron jobs.
• Designing and building data pipeline orchestration with Kubernetes.
• Optimizing Kubernetes workflows for data processing.
• Best practices for pipeline orchestration.

Module 4: Stateful Application Management

• Implementing stateful application management for databases and storage.
• Utilizing Kubernetes persistent volumes and stateful sets.
• Designing and building stateful data engineering deployments.
• Optimizing stateful applications for data persistence.
• Best practices for stateful applications.

Module 5: Scalable Data Engineering Clusters

• Designing and building scalable data engineering clusters on Kubernetes.
• Utilizing Kubernetes auto-scaling and resource management.
• Implementing cluster configuration and management.
• Optimizing clusters for large-scale data processing.
• Best practices for cluster scaling.

Module 6: Kubernetes Configuration Optimization

• Optimizing Kubernetes configurations for data engineering workloads.
• Utilizing resource requests and limits.
• Implementing node selectors and tolerations.
• Designing efficient Kubernetes configurations.
• Best practices for configuration optimization.

Module 7: Troubleshooting Kubernetes Deployments

• Debugging common issues in Kubernetes deployments.
• Analyzing Kubernetes logs and events.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common deployment errors.
• Best practices for troubleshooting.

Module 8: Data Security and Access Control

• Implementing data security and access control in Kubernetes environments.
• Utilizing Kubernetes RBAC and network policies.
• Designing and building secure Kubernetes deployments.
• Optimizing security for data protection.
• Best practices for security.

Module 9: Integration with Data Systems

• Integrating Kubernetes with various data storage and processing systems.
• Utilizing Kubernetes operators for data services.
• Implementing data integration with external databases and storage.
• Optimizing integration for data retrieval and processing.
• Best practices for integration.

Module 10: Large Datasets and Data Warehousing

• Understanding how to handle large datasets and data warehousing on Kubernetes.
• Utilizing distributed storage systems on Kubernetes.
• Implementing data partitioning and parallel processing.
• Designing scalable data warehousing solutions.
• Best practices for large datasets.

Module 11: Advanced Kubernetes Features

• Exploring advanced Kubernetes features for data engineering (operators, custom resources).
• Utilizing Kubernetes operators for database management.
• Implementing custom resources for data pipelines.
• Designing and building advanced Kubernetes solutions.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced features.

Module 12: Real-World Use Cases

• Implementing Kubernetes for data lake deployments.
• Utilizing Kubernetes for real-time data processing.
• Implementing Kubernetes for machine learning pipelines.
• Utilizing Kubernetes for data warehousing and analytics.
• Best practices for real-world applications.

Module 13: Kubernetes Tools Implementation

• Utilizing Kubernetes tools and frameworks (Helm, Kubeflow).
• Implementing data engineering tools on Kubernetes.
• Designing and building automated deployment workflows.
• Optimizing tool usage for efficient development.
• Best practices for tool implementation.

Module 14: Performance Monitoring and Logging

• Implementing performance monitoring and logging for Kubernetes deployments.
• Utilizing Prometheus and Grafana for monitoring.
• Designing and building performance dashboards.
• Optimizing monitoring for real-time insights.
• Best practices for monitoring.

Module 15: Future Trends in Kubernetes Data Engineering

• Emerging trends in Kubernetes for data engineering.
• Utilizing serverless Kubernetes for data processing.
• Implementing data mesh architectures on Kubernetes.
• Best practices for future applications.

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.