A Guide for DevOps engineers, data engineers, platform architects, and software developers seeking to build and manage scalable data pipelines on Kubernetes.

DRAFT

Table of Contents

Chapter 1: The Rise of Container-Native Workflow Orchestration

To introduce the need for workflow orchestration in modern data platforms and position Argo as a solution.

• 1.1 The Data Deluge and the Need for Automation
  • Exploding data volumes and velocity: A business perspective
  • Challenges of traditional data pipeline architectures
  • Introducing the concept of container-native workflows
• 1.2 Introducing the Argo Project Ecosystem
  • Argo Workflows, Argo Events, Argo CD, and Argo Rollouts: An overview
  • The Argo Project's philosophy: Kubernetes-native, declarative, and GitOps-ready
  • Use cases for Argo in data engineering and beyond
• 1.3 Container Orchestration for Data Pipelines: A Paradigm Shift
  • Benefits of Kubernetes for data processing: Scalability, resilience, and resource utilization
  • Briefly comparing Argo to other workflow tools: Airflow, Prefect, and cloud-native alternatives
  • Real-world examples: How companies are leveraging Argo for data pipelines
• 1.4 A Business Case for Argo Workflows and Events
  • Cost savings through efficient resource utilization
  • Increased agility and faster time-to-market for data products
  • Improved data quality and reliability through automated workflows

Chapter 1: The Rise of Container-Native Workflow Orchestration

Chapter 2: Argo Workflows: Core Concepts and Architecture

To provide a deep understanding of Argo Workflows' architecture and core concepts.

• 2.1 Workflows, Templates, and Steps: The Building Blocks
  • Understanding the Workflow CRD and its specification
  • Defining workflow templates: Container templates, script templates, and resource templates
  • Constructing workflows with steps and DAGs
  • WorkflowTemplate vs. ClusterWorkflowTemplate: Use cases and differences
• 2.2 Workflow Specification Structure and CRD Model
  • Detailed examination of the Workflow YAML structure
  • Parameterization and artifact management
  • Input and output parameters, global parameters
  • Using ConfigMaps and Secrets
• 2.3 Controller Architecture and Execution Model
  • The Argo Workflow Controller: Responsibilities and components
  • Workflow execution lifecycle: Submission, scheduling, execution, and completion
  • Understanding the pod lifecycle within a workflow
• 2.4 Advanced Workflow Features
  • Conditional execution and loops
  • Retry strategies and error handling
  • Resource allocation and limits
  • Parallelism and fan-out/fan-in patterns
• 2.5 Navigating the Argo Workflows UI and CLI
  • Overview of the Argo Workflows user interface and command-line interface
  • Monitoring workflow progress and status
  • Viewing logs, artifacts, and parameters
  • Resubmitting and terminating workflows