Introduction

As data engineering workflows continue to grow in complexity, managing infrastructure and ensuring scalability becomes increasingly important. Kubernetes and Docker have emerged as essential tools for managing containers and orchestrating complex data pipelines. Docker allows data engineers to package applications and dependencies into portable containers, while Kubernetes provides the orchestration needed to manage those containers at scale, automating deployment, scaling, and operations.

This course provides data engineers with a solid foundation in using Docker and Kubernetes to build, deploy, and manage data engineering pipelines in containerized environments. Through hands-on experience, participants will learn to leverage these tools to enhance the efficiency, portability, and scalability of their data engineering systems.

Prerequisites

• Basic understanding of data engineering concepts and tools.
• Familiarity with Linux command-line interface (CLI).
• Experience with programming languages such as Python, Java, or SQL is helpful.
• Basic knowledge of cloud computing and containerization concepts is recommended, but not required.

Table of Contents

1. Introduction to Containerization and Orchestration
   1.1 What is Containerization?
   1.2 Introduction to Docker: Benefits for Data Engineering
   1.3 What is Kubernetes?
   1.4 Key Concepts in Kubernetes: Pods, Nodes, and Services
   1.5 Docker vs. Kubernetes: Understanding Their Roles in Data Engineering
2. Setting Up Docker for Data Engineering
   2.1 Installing Docker and Docker Compose
   2.2 Understanding Docker Images and Containers
   2.3 Building Custom Docker Images for Data Pipelines
   2.4 Docker Volumes for Persisting Data
   2.5 Networking with Docker: Connecting Containers
   2.6 Best Practices for Dockerizing Data Engineering Applications
3. Managing Data Engineering Workloads with Kubernetes
   3.1 Setting Up a Kubernetes Cluster
   3.2 Kubernetes Architecture: Pods, Deployments, and Services
   3.3 Managing Data Workloads with Kubernetes Pods
   3.4 Scaling Data Workloads with Kubernetes Deployments
   3.5 Kubernetes ConfigMaps and Secrets for Managing Configuration
   3.6 Monitoring and Logging Kubernetes Clusters
4. Building and Orchestrating Data Pipelines with Docker and Kubernetes
   4.1 Dockerizing Data Engineering Pipelines
   4.2 Integrating ETL Jobs into Docker Containers
   4.3 Using Kubernetes for Pipeline Orchestration
   4.4 Managing Task Dependencies and Scheduling with Kubernetes Jobs
   4.5 Deploying Real-Time Data Pipelines with Kubernetes
   4.6 Automating Data Ingestion and Transformation Pipelines
5. Data Storage and Persistence in Containerized Environments
   5.1 Using Docker Volumes for Persistent Storage
   5.2 Kubernetes Persistent Volumes and Persistent Volume Claims
   5.3 Managing Data Lakes and Warehouses in Kubernetes
   5.4 Integrating Kubernetes with Cloud Storage Solutions (e.g., S3, GCS)
   5.5 Ensuring Data Consistency in Containerized Data Pipelines
6. Optimizing Performance and Resource Management
   6.1 Resource Requests and Limits in Kubernetes
   6.2 Tuning Docker Containers for High-Performance Data Engineering
   6.3 Horizontal and Vertical Scaling in Kubernetes for Data Pipelines
   6.4 Managing Data Volume and I/O Performance in Containers
   6.5 Optimizing Network Traffic for Data Workflows
7. Security and Data Governance in Kubernetes and Docker
   7.1 Securing Data Pipelines in Docker Containers
   7.2 Kubernetes Security Best Practices
   7.3 Role-Based Access Control (RBAC) in Kubernetes
   7.4 Protecting Sensitive Data with Kubernetes Secrets
   7.5 Ensuring Compliance and Auditing in Containerized Environments
8. Monitoring and Troubleshooting Data Pipelines
   8.1 Monitoring Docker Containers: Logs and Metrics
   8.2 Kubernetes Monitoring: Prometheus and Grafana
   8.3 Debugging Data Pipelines in Docker and Kubernetes
   8.4 Setting Up Alerts for Data Pipeline Failures
   8.5 Using Distributed Tracing for Data Engineering Pipelines
9. Advanced Topics in Kubernetes and Docker for Data Engineering
   9.1 Integrating Kubernetes with Apache Kafka for Real-Time Data Streaming
   9.2 Using Kubernetes Operators for Automating Data Pipeline Management
   9.3 Running Big Data Frameworks (e.g., Hadoop, Spark) in Kubernetes
   9.4 Serverless Data Engineering with Kubernetes
   9.5 Leveraging Kubernetes for Machine Learning Pipelines
10. Case Studies and Real-World Implementations
    10.1 Containerizing and Orchestrating Data Pipelines in a Cloud Environment
    10.2 Using Kubernetes to Scale ETL Pipelines in a Data Lake
    10.3 Real-World Use Case: Big Data Processing with Docker and Kubernetes
    10.4 Managing Hybrid and Multi-Cloud Data Pipelines with Kubernetes
    10.5 Future Trends in Containerization and Orchestration for Data Engineering

Conclusion

Mastering Docker and Kubernetes is essential for data engineers who want to build scalable, flexible, and efficient data pipelines. These tools offer immense power in terms of containerizing applications, automating workflows, and managing complex data infrastructures. By completing this course, participants will have the skills to design and manage containerized data engineering workflows that can easily scale across cloud platforms and on-premise environments.

As organizations increasingly move toward cloud-native architectures, Docker and Kubernetes will continue to play a critical role in data engineering. The expertise gained in this course will not only enhance your ability to manage modern data pipelines but also prepare you to handle the challenges of scaling and securing data operations in distributed environments.