Data Infrastructure On Kubernetes Training Course: Deploy Big Data Tools

Introduction

Revolutionize your data infrastructure with our Data Infrastructure on Kubernetes Training Course. This program is designed to equip you with the essential skills to deploy Big Data tools on Kubernetes, enabling you to build scalable and resilient data platforms. In today's cloud-native world, mastering Kubernetes for data infrastructure is crucial for achieving agility and efficiency. Our Kubernetes training course provides hands-on experience and expert guidance, empowering you to orchestrate complex data workloads seamlessly.

This Big Data Kubernetes training delves into the core concepts of deploying and managing data tools on Kubernetes, covering topics such as containerization, orchestration, and scalability. You'll gain expertise in using industry-standard tools and techniques to build data infrastructure on Kubernetes that meets the demands of modern data environments. Whether you're a data engineer, DevOps professional, or system administrator, this Kubernetes for Big Data course will empower you to effectively deploy Big Data tools on Kubernetes.

Target Audience:

• Data Engineers
• DevOps Engineers
• System Administrators
• Big Data Developers
• Cloud Engineers
• Kubernetes Administrators
• Anyone needing Kubernetes for Big Data skills

Course Objectives:

• Understand the fundamentals of deploying Big Data tools on Kubernetes.
• Master containerization and orchestration for data infrastructure.
• Utilize Kubernetes for scalable and resilient data deployments.
• Implement persistent storage and networking for Big Data applications.
• Deploy and manage popular Big Data tools on Kubernetes.
• Optimize Kubernetes resources for data workloads.
• Troubleshoot and debug Kubernetes deployments for Big Data.
• Implement data security and access control in Kubernetes.
• Integrate Kubernetes with existing data infrastructure.
• Understand how to monitor and maintain data deployments on Kubernetes.
• Explore advanced Kubernetes patterns for Big Data.
• Apply real world use cases for data infrastructure on Kubernetes.
• Automate deployment and scaling tasks using Kubernetes operators.

Duration

10 Days

Course content

Module 1: Introduction to Kubernetes for Data Infrastructure

• Fundamentals of Kubernetes and container orchestration.
• Overview of deploying Big Data tools on Kubernetes.
• Setting up a Kubernetes development environment.
• Introduction to Kubernetes concepts and terminology.
• Best practices for Kubernetes in data infrastructure.

Module 2: Containerization for Big Data Tools

• Containerizing Big Data tools using Docker.
• Building and managing Docker images.
• Utilizing container registries for image storage.
• Implementing multi-container applications.
• Best practices for containerization.

Module 3: Kubernetes Orchestration for Data Workloads

• Deploying and managing pods, deployments, and services.
• Utilizing Kubernetes namespaces and resource quotas.
• Implementing stateful sets for stateful applications.
• Managing configuration using config maps and secrets.
• Best practices for Kubernetes orchestration.

Module 4: Persistent Storage on Kubernetes

• Utilizing persistent volumes and persistent volume claims.
• Implementing storage classes for dynamic provisioning.
• Managing storage for stateful Big Data applications.
• Implementing distributed file systems on Kubernetes.
• Best practices for persistent storage.

Module 5: Networking for Big Data Applications

• Implementing Kubernetes networking concepts (services, ingress).
• Utilizing network policies for security.
• Implementing service discovery and load balancing.
• Managing network traffic for Big Data applications.
• Best practices for networking.

Module 6: Deploying Big Data Tools on Kubernetes

• Deploying Apache Spark on Kubernetes.
• Deploying Apache Kafka on Kubernetes.
• Deploying Elasticsearch on Kubernetes.
• Deploying Hadoop on Kubernetes.
• Deploying Databases on Kubernetes.

Module 7: Resource Optimization for Data Workloads

• Optimizing Kubernetes resources (CPU, memory) for data workloads.
• Utilizing resource requests and limits.
• Implementing horizontal pod autoscaling.
• Utilizing node selectors and taints/tolerations.
• Best practices for resource optimization.

Module 8: Troubleshooting and Debugging

• Debugging Kubernetes deployments for Big Data.
• Analyzing pod logs and events.
• Utilizing Kubernetes debugging tools (kubectl describe, logs).
• Resolving common Kubernetes deployment issues.
• Best practices for troubleshooting.

Module 9: Security and Access Control

• Implementing Kubernetes security best practices.
• Utilizing role-based access control (RBAC).
• Implementing network policies for security.
• Managing secrets and credentials.
• Best practices for security.

Module 10: Integration with Data Infrastructure

• Integrating Kubernetes with existing data storage and processing systems.
• Utilizing data connectors and APIs.
• Implementing data pipelines on Kubernetes.
• Best practices for integration.

Module 11: Monitoring and Maintenance

• Monitoring Kubernetes deployments for Big Data.
• Utilizing monitoring tools (Prometheus, Grafana).
• Implementing alerting and notifications.
• Managing Kubernetes upgrades and maintenance.
• Best practices for monitoring.

Module 12: Advanced Kubernetes Patterns

• Implementing Kubernetes operators for automation.
• Utilizing custom resource definitions (CRDs).
• Implementing service meshes for microservices.
• Utilizing Kubernetes for edge computing.
• Advanced techniques for Kubernetes deployments.

Module 13: Kubernetes in Cloud Environments

• Deploying Kubernetes on cloud platforms (AWS EKS, Azure AKS, Google GKE).
• Utilizing cloud-based Kubernetes services.
• Managing cloud resources for Kubernetes.
• Best practices for cloud deployment.

Module 14: Kubernetes and Data Governance

• Implementing data governance policies in Kubernetes.
• Utilizing metadata management tools.
• Implementing data lineage and data dictionary within Kubernetes deployments.
• Best practices for data governance.

Module 15: Future Trends in Kubernetes for Data Infrastructure

• Emerging trends in Kubernetes for Big Data.
• Utilizing AI and automation in Kubernetes deployments.
• Implementing serverless Big Data on Kubernetes.
• Best practices for future Kubernetes.

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.