Cloud-Native Data Engineering Course: Leveraging Kubernetes, Docker, and Cloud Services

Course Introduction

The digital era has ushered in a new paradigm of data engineering powered by cloud-native technologies. Organizations today demand scalable, resilient, and cost-efficient infrastructures to process massive volumes of data in real-time. Cloud-native architectures, powered by Kubernetes, Docker, and managed cloud services, form the backbone of modern data engineering, enabling enterprises to streamline data ingestion, integration, transformation, and delivery at scale.

This course equips participants with the knowledge and applied expertise to design and implement cloud-native data pipelines. Learners will gain practical experience in containerization with Docker, orchestration with Kubernetes, and integration with cloud services such as AWS, Azure, and GCP. Emphasis is placed on building distributed, fault-tolerant pipelines that align with enterprise analytics and AI workloads.

Through hands-on projects, participants will explore the full lifecycle of cloud-native data engineering from designing containerized applications to deploying resilient pipelines across hybrid and multi-cloud environments. This ensures learners not only understand cloud-native theory but also acquire the skills necessary to deliver production-ready systems.

A critical component of the course is data observability and governance, ensuring data engineers can monitor, troubleshoot, and secure pipelines effectively. Emerging topics such as serverless data engineering, infrastructure as code, and data mesh will be covered to align participants with the latest industry practices and innovations.

Practical case studies will help learners apply concepts in real-world scenarios, such as real-time analytics, IoT data processing, and machine learning pipeline deployment. These applied exercises provide participants with skills that are directly transferable to enterprise use cases.

By the end of the course, learners will have a deep understanding of cloud-native architectures and the confidence to design, build, and manage data pipelines that leverage Kubernetes, Docker, and cloud services for scalability, flexibility, and performance.

Who Should Attend

• Data Engineers seeking to transition to cloud-native architectures.
• Software Engineers and Developers working with distributed applications.
• Cloud Engineers specializing in AWS, Azure, or GCP.
• DevOps Professionals building CI/CD pipelines for data systems.
• Database Administrators managing cloud-based infrastructures.
• Machine Learning Engineers deploying cloud-native ML workflows.
• IT Architects designing enterprise-scale data solutions.
• Business Intelligence Specialists integrating analytics pipelines.
• Consultants advising on cloud-native data strategies.
• Technical Project Managers overseeing data modernization projects.

Duration

10 days

Course Objectives

• Understand the fundamentals of cloud-native data engineering and architectures.
• Learn containerization concepts with Docker and their application in data systems.
• Gain expertise in deploying and managing Kubernetes clusters for data pipelines.
• Integrate cloud services (AWS, Azure, GCP) into end-to-end data workflows.
• Design and implement fault-tolerant, scalable, and secure data pipelines.
• Apply infrastructure as code (IaC) for reproducible and automated deployments.
• Explore serverless data engineering frameworks and their applications.
• Implement monitoring, logging, and observability for cloud-native pipelines.
• Understand data governance, lineage, and compliance in cloud-native systems.
• Develop practical skills through hands-on case studies and project simulations.
• Explore advanced topics such as data mesh, hybrid, and multi-cloud strategies.
• Translate business requirements into efficient and production-ready cloud-native data solutions.

Comprehensive Course Outline

Module 1: Foundations of Cloud-Native Data Engineering

• Introduction to Cloud-Native Principles
• Evolution of Data Engineering Architectures
• Cloud vs. On-Prem vs. Hybrid Approaches
• Benefits and Challenges of Cloud-Native Data Systems

Module 2: Containerization with Docker

• Docker Fundamentals for Data Engineering
• Building and Managing Docker Images
• Container Networking and Volumes
• Best Practices for Secure Containerized Applications

Module 3: Orchestration with Kubernetes

• Kubernetes Architecture and Components
• Deploying and Scaling Data Pipelines with Kubernetes
• ConfigMaps, Secrets, and Persistent Storage
• Managing Stateful Data Workloads on Kubernetes

Module 4: Cloud Infrastructure Essentials

• Overview of AWS, Azure, and GCP Services
• Cloud Storage Options (S3, ADLS, GCS)
• Cloud Databases and Data Warehouses
• Networking and Security in Cloud Environments

Module 5: Infrastructure as Code (IaC)

• Introduction to Terraform and CloudFormation
• Automating Data Pipeline Infrastructure
• Reproducibility and Version Control with IaC
• Best Practices for IaC in Data Engineering

Module 6: Data Ingestion in Cloud-Native Systems

• Batch Ingestion from Legacy Systems
• Streaming Ingestion with Kafka, Pub/Sub, and Kinesis
• Using APIs and Connectors for Data Capture
• Designing Secure and Scalable Ingestion Flows

Module 7: Data Transformation and Integration

• ETL vs. ELT in Cloud-Native Architectures
• Using Spark, Flink, and Beam for Transformations
• Serverless Data Processing with Lambda, Dataflow, and Functions
• Data Standardization and Schema Evolution

Module 8: Storage and Data Lake Architectures

• Designing Cloud Data Lakes and Lakehouses
• Storage Optimization and Partitioning
• Integrating Cloud Storage with Analytics Systems
• Emerging Lakehouse Technologies

Module 9: Workflow Orchestration

• Orchestration with Airflow and Prefect in the Cloud
• DAG Design for Cloud-Native Pipelines
• CI/CD Integration for Data Workflows
• Monitoring and Failure Recovery

Module 10: Observability and Monitoring

• Logging, Metrics, and Tracing in Data Systems
• Tools: Prometheus, Grafana, and ELK Stack
• Detecting and Responding to Failures
• Data Quality and Validation Monitoring

Module 11: Security and Compliance

• Authentication and Authorization in Cloud-Native Systems
• Data Encryption at Rest and in Transit
• Secrets Management in Kubernetes and Cloud Services
• Compliance with GDPR, HIPAA, and Industry Standards

Module 12: Scaling and Performance Optimization

• Horizontal vs. Vertical Scaling in Cloud Pipelines
• Resource Optimization in Kubernetes and Cloud Services
• Caching Strategies for Performance
• Designing High Availability Pipelines

Module 13: Advanced Cloud-Native Architectures

• Microservices in Data Engineering
• Event-Driven Data Architectures
• Multi-Cloud and Hybrid Deployments
• Data Mesh and Data Fabric Concepts

Module 14: Case Studies in Cloud-Native Data Engineering

• Real-Time Analytics Pipeline with Kafka and Spark
• IoT Data Processing with Kubernetes and Cloud Services
• Customer 360 Pipeline in a Multi-Cloud Environment
• ML Pipeline Deployment with Spark and Kubernetes

Module 15: Project – Cloud-Native Data Pipeline

• Defining Business and Technical Requirements
• Designing and Containerizing Data Pipelines
• Deploying to Kubernetes with Cloud Service Integrations
• Final Presentation and Documentation

Module 16: Future Trends and Emerging Issues

• Serverless Data Engineering Evolution
• AI-Powered Automation for Pipelines
• Sustainability and Green Data Engineering
• Future of Cloud-Native Data Architectures

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: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill 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 Upskill 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: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.