Apache Airflow on Cloud Platforms Training Course

 Apache Airflow on Cloud Platforms Training Course 

Introduction 

Apache Airflow on Cloud Platforms Training Course provides comprehensive practical knowledge and technical expertise in cloud-based workflow orchestration, automated data pipeline management, and enterprise data engineering. The course explores Apache Airflow architecture, Directed Acyclic Graphs (DAGs), task scheduling, workflow automation, cloud-native infrastructure, and distributed computing. Participants gain practical skills in deploying and managing Apache Airflow across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), integrating cloud storage, data warehouses, databases, and real-time data processing technologies. The training emphasizes scalable data pipeline orchestration, cloud infrastructure optimization, DevOps automation, Infrastructure as Code, containerization, and secure enterprise workflow management. 

Participants will develop advanced competencies in cloud-native Apache Airflow deployment, monitoring, troubleshooting, performance optimization, and production workflow management. Through practical exercises and international case studies, learners will explore Amazon Managed Workflows for Apache Airflow (MWAA), Google Cloud Composer, and Azure-based orchestration environments. The course also covers Kubernetes integration, Docker containers, CI/CD pipelines, cloud security, data governance, and cost optimization. By the end of the training, participants will be equipped to implement resilient, scalable, automated, and highly available data orchestration solutions supporting modern analytics, artificial intelligence, machine learning, and enterprise digital transformation initiatives. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Understand Apache Airflow architecture and cloud-native workflow orchestration.
2. Develop advanced Directed Acyclic Graphs (DAGs).
3. Deploy Apache Airflow across major cloud platforms.
4. Configure Amazon Managed Workflows for Apache Airflow.
5. Implement Google Cloud Composer environments.
6. Explore Microsoft Azure integration capabilities.
7. Automate enterprise data engineering workflows.
8. Integrate cloud databases and storage services.
9. Implement Kubernetes and Docker orchestration.
10. Configure CI/CD pipelines for workflow deployment.
11. Apply cloud security and access management.
12. Monitor, troubleshoot, and optimize workflows.
13. Implement scalable and highly available orchestration solutions.

Organizational Benefits
 

1. Improved enterprise data pipeline automation and productivity.
2. Reduced operational costs through optimized cloud resources.
3. Enhanced scalability of enterprise data infrastructure.
4. Improved reliability of automated data workflows.
5. Faster deployment of cloud-based analytics solutions.
6. Strengthened data security and governance.
7. Improved collaboration between data engineering teams.
8. Reduced manual intervention in data processing.
9. Better integration across enterprise cloud environments.
10. Enhanced organizational digital transformation capabilities.

Target Audiences
 

1. Data engineers and cloud data engineers.
2. Cloud architects and solutions architects.
3. DevOps and site reliability engineers.
4. Data scientists and machine learning engineers.
5. Database and systems administrators.
6. Business intelligence and analytics professionals.
7. IT infrastructure and cloud operations managers.
8. Enterprise technology consultants and software developers.

Course Duration: 10 days

Course Modules

Module 1: Introduction to Apache Airflow and Cloud Orchestration
 

• Apache Airflow architecture and core components.
• Cloud-native workflow orchestration principles.
• Understanding schedulers, workers, and executors.
• DAG development and task dependencies.
• Global case study: Airbnb's adoption of workflow orchestration.

Module 2: Advanced Apache Airflow Architecture
 

• Airflow metadata database and webserver.
• Executors, schedulers, and distributed processing.
• Task lifecycle and execution management.
• Airflow configuration and environment variables.
• Case study: Enterprise-scale distributed data orchestration.

Module 3: Amazon Web Services Integration
 

• AWS cloud infrastructure fundamentals.
• Amazon Managed Workflows for Apache Airflow.
• Integration with Amazon S3 and AWS Glue.
• IAM roles and cloud resource permissions.
• Case study: AWS-based financial data pipeline automation.

Module 4: Google Cloud Platform Integration
 

• Google Cloud Composer architecture.
• Cloud Storage and BigQuery integration.
• Service accounts and identity management.
• Cloud-native DAG deployment.
• Case study: Google Cloud analytics pipeline implementation.

Module 5: Microsoft Azure Integration
 

• Azure cloud infrastructure and services.
• Azure Data Lake Storage integration.
• Azure Kubernetes Service connectivity.
• Authentication and access management.
• Case study: Enterprise Azure data orchestration.

Module 6: Cloud-Based DAG Development
 

• Advanced DAG design and implementation.
• Dynamic task mapping and task groups.
• Sensors, operators, and hooks.
• Scheduling and dependency management.
• Case study: Global e-commerce data processing workflows.

Module 7: Cloud Storage and Database Integration
 

• Connecting cloud databases and data warehouses.
• AWS S3, Azure Blob, and Google Cloud Storage.
• PostgreSQL and MySQL integration.
• Data extraction and loading automation.
• Case study: Multicloud enterprise data warehouse integration.

Module 8: Docker and Kubernetes Deployment
 

• Containerizing Apache Airflow applications.
• Docker Compose configuration.
• KubernetesExecutor implementation.
• Helm charts and Kubernetes deployments.
• Case study: Scalable Kubernetes-based workflow infrastructure.

Module 9: CI/CD and Infrastructure as Code
 

• Automated Airflow deployment pipelines.
• Git integration and version control.
• Terraform for cloud infrastructure provisioning.
• Continuous integration and continuous deployment.
• Case study: Automated cloud infrastructure deployment.

Module 10: Cloud Security and Governance
 

• Cloud identity and access management.
• Secrets management and credential protection.
• Role-based access control.
• Encryption and secure connections.
• Case study: Financial-sector cloud data security implementation.

Module 11: Monitoring and Performance Optimization
 

• Airflow logs and execution monitoring.
• Cloud monitoring and alerting systems.
• Scheduler and worker optimization.
• Troubleshooting failed DAG executions.
• Case study: High-volume cloud pipeline performance optimization.

Module 12: Event-Driven and Real-Time Workflows
 

• Event-driven workflow architecture.
• Integration with Apache Kafka.
• Cloud messaging and notification services.
• Streaming data pipeline orchestration.
• Case study: Real-time financial transaction processing.

Module 13: Machine Learning and AI Workflow Orchestration
 

• Machine learning pipeline automation.
• Integration with cloud AI services.
• Model training and deployment workflows.
• Automated model monitoring.
• Case study: Google Cloud-based machine learning orchestration.

Module 14: High Availability and Disaster Recovery
 

• Airflow high-availability architecture.
• Backup and recovery strategies.
• Cloud failover and resilience.
• Disaster recovery planning and testing.
• Case study: Enterprise banking workflow continuity.

Module 15: Advanced Cloud Project and Implementation
 

• Designing an enterprise cloud orchestration solution.
• Deploying production-ready Apache Airflow.
• Integrating storage, databases, and analytics.
• Implementing monitoring, security, and automation.
• Case study: Multicloud enterprise data engineering project.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations of cloud-based Apache Airflow deployment.
• Hands-on laboratory exercises and guided configuration.
• Real-world international case studies and industry examples.
• Group assignments and collaborative cloud engineering projects.
• Practical assessments, troubleshooting exercises, and knowledge evaluation.
• Expert-led discussions on emerging cloud orchestration technologies.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.