Course Overview of Workflow Orchestration with Cloud Composer

Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. This course provides a comprehensive guide to provisioning Composer instances, creating and managing Airflow Directed Acyclic Graphs (DAGs), and performing essential tasks such as testing, debugging, and monitoring workflows.

After completing Workflow Orchestration with Cloud Composer, students will be able to:

  • Explore Orchestration Solutions- Understand Apache Airflow and its managed implementation via Cloud Composer.
  • Provision Instances- Successfully set up and configure Cloud Composer environments.
  • Develop DAGs- Create and manage Airflow DAGs using industry best practices and common operators.
  • Control Flow- Manage complex triggers, dependencies, and flow control within workflows.
  • Integrate Services- Connect Airflow workflows with various Google Cloud Services.
  • Optimize and Debug- Leverage advanced Airflow features and perform debugging, testing, and performance scaling.
  • Monitor Operations- Observe running DAGs and implement security and access control.

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Key Features of Workflow Orchestration with Cloud Composer

  • Fully Managed Service- Leverages Cloud Composer, a managed service built on the open-source Apache Airflow project to simplify orchestration.

  • Cross-Platform Capability- Enables the creation and management of workflow pipelines that span across multiple clouds and on-premises data centers. 

  • End-to-End DAG Management- Covers the entire lifecycle of Directed Acyclic Graphs (DAGs), including creation, scheduling, and execution. 

  • Hands-On Lab Training- Includes three practical labs focused on provisioning, assembling data workflows, and monitoring. 

  • Google Cloud Integration- Deep dive into integrating Airflow workflows with other Google Cloud Services. 

  • Advanced Troubleshooting- Dedicated modules for testing, debugging, and resolving issues within Airflow DAGs. 

  • Enterprise-Grade Operations- Focuses on performance, scalability, security, and access control for production environments. 

  • Observability- Built-in techniques for monitoring and observing the health of running DAGs. 

Who should Attend Workflow Orchestration with Cloud Composer?

  • Customers and Data Engineers.

Prerequisites of Workflow Orchestration with Cloud Composer

Completion of "Building Batch Data Pipelines on Google Cloud" or equivalent knowledge of data analytics and engineering on Google Cloud.

Why choose CloudThat as your training partner for Workflow Orchestration with Cloud Composer?

  • Specialized GCP Focus: CloudThat specializes in cloud technologies, offering focused and specialized training programs. We are Authorized Trainers for the Google Cloud Platform. This specialization ensures in-depth coverage of GCP services, use cases, best practices, and hands-on experience tailored specifically for GCP. 
  • Industry-Recognized Trainers: CloudThat has a strong pool of industry-recognized trainers certified by GCP. These trainers bring real-world experience and practical insights into the training sessions, comprehensively understanding how GCP is applied in different industries and scenarios. 
  • Hands-On Learning Approach: CloudThat emphasizes a hands-on learning approach. Learners can access practical labs, real-world projects, and case studies that simulate actual GCP environments. This approach allows learners to apply theoretical knowledge in practical scenarios, enhancing their understanding and skill set. 
  • Customized Learning Paths: CloudThat understands that learners have different levels of expertise and varied learning objectives. We offer customized learning paths, catering to beginners, intermediate learners, and professionals seeking advanced GCP skills. 
  • Interactive Learning Experience: CloudThat's training programs are designed to be interactive and engaging. We utilize various teaching methodologies like live sessions, group discussions, quizzes, and mentorship to keep learners engaged and motivated throughout the course. 
  • Placement Assistance and Career Support: CloudThat often provides placement assistance and career support services. This includes resume building, interview preparation, and connecting learners with job opportunities through our network of industry partners and companies looking for GCP-certified professionals. 
  • Continuous Learning and Updates: CloudThat ensures that our course content is regularly updated to reflect the latest trends, updates, and best practices within the GCP ecosystem. This commitment to keeping the content current enables learners to stay ahead in their GCP knowledge. 
  • Positive Reviews and Testimonials: Reviews and testimonials from past learners can strongly indicate the quality of training provided. You can Check feedback and reviews about our GCP courses that can provide potential learners with insights into the effectiveness and value of the training. 

Learning Objectives of Workflow Orchestration with Cloud Composer

  • Explore Orchestration Solutions: Understand Apache Airflow and its managed implementation via Cloud Composer.  
  • Provision Instances: Successfully set up and configure Cloud Composer environments.  
  • Develop DAGs: Create and manage Airflow DAGs using industry best practices and common operators.  
  • Control Flow: Manage complex triggers, dependencies, and flow control within workflows.  
  • Integrate Services: Connect Airflow workflows with various Google Cloud Services.  
  • Optimize and Debug: Leverage advanced Airflow features and perform debugging, testing, and performance scaling.
  • Monitor Operations: Observe running DAGs and implement security and access control.  
  • Detect Model Issues: Understand and identify technical issues such as feature drift and skew in deployed models.  

Course Outline for Workflow Orchestration with Cloud Composer Download Course Outline

Lecture Content

  • The need for Workflow Orchestration and DAG (Directed Acyclic Graph) management in Data Engineering
  • Challenges of traditional cron-based scheduling and manual pipeline execution loops
  • Introduction to Apache Airflow architecture (Scheduler, Web Server, Workers, Database)
  • Overview of Cloud Composer as a fully managed, enterprise-grade Apache Airflow service
  • Environment configurations, node scaling profiles, and Airflow UI/Cloud Console navigation

Learning Objectives

  • Identify the functional requirements for programmatic workflow orchestration in big data pipelines
  • Articulate the core architectural differences between open-source Apache Airflow and managed Cloud Composer
  • Provision and browse the unified management interfaces of a functional Cloud Composer environment

Lab Content

  • Provisioning Cloud Composer Environments and Exploring Airflow UI

Lecture Content

  • Designing declarative Python scripts to build Directed Acyclic Graphs (DAGs)
  • Core operator deep-dive: BashOperator, PythonOperator, and specialized Google Cloud Transfer Operators
  • Defining upstream and downstream structural dependencies (>>, <<, and set_downstream)
  • Advanced flow management: Trigger rules (all_success, one_failed, all_done) and branching conditions
  • Securely executing connections and interacting natively with Google Cloud Services (BigQuery, Cloud Storage, Dataproc)

Learning Objectives

  • Construct custom, multi-step orchestration workflows programmatically using Pythonic LookML or Airflow logic
  • Implement structural pipeline flow controls and customized trigger rule exceptions to handle data anomalies
  • Build an integrated workflow that securely triggers multi-service data processes across Google Cloud platforms

Lab Content

  • Assembling a Data Processing Workflow with Cloud Storage and BigQuery

Lecture Content

  • Leveraging advanced features: Dynamic DAG generation, Airflow Variables, and Jinja Templating macros
  • Managing localized operational values, securely accessing credentials using Airflow Connections and Secret Manager
  • Troubleshooting runtime anomalies, reading task execution logs, and debugging parsing errors inside the IDE
  • Enforcing pipeline performance optimizations: Concurrency settings, pool allocations, and avoiding top-level DAG code blocks
  • Full-stack workflow observability: Setting up operational metrics via Cloud Logging and Cloud Monitoring dashboards

Learning Objectives

  • Implement dynamic, data-driven parameters within task blocks using Airflow macros and custom variables
  • Apply optimization techniques to scale scheduler cycles and eliminate configuration code bottlenecks
  • Diagnose and fix broken task execution instances using system logs and metric trackers

Lab Content

  • Extending, Debugging, and Monitoring DAGs within Cloud Composer Environments

Certification Details of Workflow Orchestration with Cloud Composer

    CloudThat Course Completion Certificate will be awarded to all learners who complete the training.

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Course ID: 28982

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FAQs for Workflow Orchestration with Cloud Composer

The course is built on Apache Airflow, implemented via Google Cloud’s managed service, Cloud Composer.

Yes, there are 3 dedicated labs covering provisioning, DAG assembly, and monitoring.

Yes, the curriculum specifically covers testing, debugging, and monitoring Airflow DAGs.

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