In the modern data ecosystem, mastering Containerized Data Engineering Workflows with Docker & Kubernetes is a fundamental skill for building reproducible, scalable, and portable data pipelines that can run consistently across any environment, from a local machine to a multi-cloud production cluster. This specialized expertise enables data engineers to eliminate the "it works on my machine" problem, streamline development-to-production cycles, and efficiently manage the compute and resource requirements of complex data processing jobs. This comprehensive training course is meticulously designed to equip data engineers, DevOps professionals, and ML engineers with the advanced knowledge and practical strategies required to containerize data applications, orchestrate them at scale with Kubernetes, and establish robust, automated workflows for building the data foundation of any enterprise. Without robust expertise in Containerized Data Engineering Workflows with Docker & Kubernetes, organizations risk inconsistent deployments, infrastructure sprawl, and significant operational overhead that hinders the agility and reliability of their data initiatives, underscoring the vital need for specialized expertise in this critical domain.

Duration: 10 Days

Target Audience

• Data Engineers and ETL Developers
• DevOps and Site Reliability Engineers
• Machine Learning Engineers
• Cloud Architects and Systems Administrators
• Software Developers with an interest in data infrastructure
• Data Scientists working on large-scale data projects
• Technical leaders and managers.

Objectives

• Understand the fundamental concepts of containerization and its benefits for data engineering.
• Learn to build and manage container images using Docker.
• Acquire skills in orchestrating containerized applications with Kubernetes.
• Comprehend techniques for designing and implementing reproducible data pipelines.
• Explore strategies for managing stateful data workloads in Kubernetes.
• Understand the importance of CI/CD for containerized data engineering workflows.
• Gain insights into managing resources and optimizing performance for data jobs.
• Develop a practical understanding of monitoring and logging in a containerized environment.
• Master the use of common open-source tools for containerized data stacks.
• Acquire skills in applying best practices for security and governance.
• Learn to integrate containerized pipelines with cloud-native services.
• Comprehend techniques for deploying and managing distributed processing frameworks like Spark on Kubernetes.
• Explore strategies for automating deployment and scaling of data services.
• Understand the importance of collaboration and version control for containerized workflows.
• Develop the ability to lead and implement production-ready Containerized Data Engineering Workflows with Docker & Kubernetes.

Course Content

Module 1: Introduction to Containerization for Data Engineering

• What are containers and why are they essential for modern data workflows?
• The role of Docker in creating reproducible environments.
• Benefits of containerization: portability, consistency, and resource isolation.
• The challenges of containerizing data applications.
• Overview of the container ecosystem and key players.

Module 2: Docker Fundamentals

• Installing Docker and understanding its architecture.
• Building Docker images with Dockerfiles.
• Running and managing Docker containers.
• The Docker registry (Docker Hub) and image management.
• Basic Docker commands and workflows.

Module 3: Advanced Docker for Data Engineering

• Multi-stage builds for smaller, more secure images.
• Docker Compose for defining multi-container applications.
• Container networking and linking services.
• Volume management for persistent data.
• Using Docker to create a reproducible development environment.

Module 4: Introduction to Kubernetes

• What is Kubernetes and why is it needed for production?
• Kubernetes architecture: master and worker nodes, pods, services.
• Key Kubernetes objects: Deployments, Services, ConfigMaps, Secrets.
• YAML manifests for declaring desired state.
• The kubectl command-line tool.

Module 5: Kubernetes for Data Engineering

• Deploying a stateless data processing application.
• Using Jobs and CronJobs for one-off and scheduled tasks.
• Managing persistent storage with Persistent Volumes (PVs) and Persistent Volume Claims (PVCs).
• StatefulSets for stateful applications like databases.
• The role of init containers and sidecars in data pipelines.

Module 6: Containerizing Data Ingestion Workflows

• Designing a containerized data ingestion pipeline.
• Building a custom Docker image for an ingestion script.
• Using Kubernetes to run ingestion jobs on a schedule.
• Best practices for handling credentials and secrets.
• Integrating with data sources like databases and APIs from within a container.

Module 7: Distributed Processing on Kubernetes

• The challenge of running frameworks like Spark and Flink on Kubernetes.
• Introduction to Spark on Kubernetes.
• Building and deploying a containerized Spark application.
• Managing Spark cluster resources within a Kubernetes cluster.
• Monitoring Spark jobs in a containerized environment.

Module 8: Workflow Orchestration with Kubernetes

• The need for a workflow orchestrator.
• Introduction to Apache Airflow and its components.
• Deploying a production-ready Airflow cluster on Kubernetes.
• Writing DAGs to manage containerized data jobs.
• Using Kubeflow for machine learning pipelines on Kubernetes.

Module 9: CI/CD for Containerized Data Workflows

• Applying DevOps principles to data engineering.
• Building a CI pipeline to test and build Docker images.
• Setting up a CD pipeline to deploy to a Kubernetes cluster.
• Using Git for version control of code and configuration.
• Popular CI/CD tools: Jenkins, GitLab CI, GitHub Actions.

Module 10: Monitoring, Logging, and Observability

• Strategies for monitoring container health and performance.
• Introduction to the Prometheus and Grafana stack.
• Centralized logging with the EFK stack (Elasticsearch, Fluentd, Kibana).
• Configuring a logging sidecar for applications.
• Setting up alerts for pipeline failures and resource issues.

Module 11: Security in a Containerized Environment

• Image security: scanning for vulnerabilities.
• Container runtime security and best practices.
• Kubernetes security contexts and network policies.
• Managing secrets and sensitive data.
• Role-Based Access Control (RBAC) in Kubernetes.

Module 12: Managing Data Storage and Databases

• Containerizing databases for development and testing.
• Using operators for managing databases in Kubernetes.
• Managing database backups and recovery.
• Designing a strategy for storing large datasets in a containerized environment.
• Choosing between stateful and stateless approaches for data processing.

Module 13: Optimizing Performance and Resource Management

• The importance of resource requests and limits in Kubernetes.
• Optimizing Docker images for size and speed.
• Performance tuning Spark jobs in a containerized environment.
• Scaling up and down clusters automatically based on load.
• Cost management in a cloud-native environment.

Module 14: Cloud-Native Kubernetes Services

• Introduction to managed Kubernetes services: EKS (AWS), AKS (Azure), GKE (GCP).
• Benefits of using a managed service.
• Setting up and configuring a managed Kubernetes cluster.
• Integrating with other cloud services (storage, messaging).
• Case studies of real-world deployments.

Module 15: Practical Workshop: End-to-End Project

• Participants work in teams to containerize a data pipeline.
• Exercise: Build Docker images for a data ingestion and processing job.
• Create Kubernetes YAML manifests to deploy the pipeline.
• Set up a simple CI/CD workflow to automate deployment.
• Monitor the running jobs and logs.
• Present the final project and discuss design choices.

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.