Data Pipeline Orchestration With Apache Airflow Training Course: Automate Data Workflows

Introduction

Streamline your data operations with our Data Pipeline Orchestration with Apache Airflow Training Course. This program is designed to equip you with the essential skills to automate and manage complex data workflows using Apache Airflow. In today's data-driven world, the ability to orchestrate and automate data pipelines is crucial for efficient data processing and analysis. Our Airflow training course provides hands-on experience and expert guidance, empowering you to build scalable and reliable data workflows.

This Apache Airflow training delves into the core concepts of data pipeline orchestration, covering topics such as directed acyclic graphs (DAGs), scheduling, and monitoring. You'll gain expertise in using Airflow to design, develop, and deploy robust data pipelines that handle the complexities of modern data environments. Whether you're a data engineer, data architect, or DevOps professional, this Airflow course will empower you to effectively automate and manage complex data workflows.

Target Audience:

• Data Engineers
• Data Architects
• DevOps Engineers
• Big Data Developers
• Data Analysts
• System Administrators
• Anyone needing data workflow automation skills

Course Objectives:

• Understand the fundamentals of data pipeline orchestration with Apache Airflow.
• Master the creation and management of Directed Acyclic Graphs (DAGs).
• Utilize Airflow's scheduling capabilities for automated data workflows.
• Implement data dependencies and task management in Airflow.
• Develop and deploy robust data pipelines using Airflow operators.
• Monitor and troubleshoot Airflow workflows for efficient data processing.
• Integrate Airflow with various data sources and systems.
• Implement data quality checks and validation within Airflow pipelines.
• Automate data transformations and ETL processes with Airflow.
• Deploy and manage Airflow in production environments.
• Implement data security and access control in Airflow workflows.
• Explore advanced Airflow features and plugins.
• Apply real world use cases for Apache Airflow.

Duration

10 Days

Course content

Module 1: Introduction to Apache Airflow

• Fundamentals of data pipeline orchestration.
• Overview of Apache Airflow and its architecture.
• Setting up an Airflow development environment.
• Introduction to Airflow concepts and terminology.
• Best practices for Airflow development.

Module 2: Directed Acyclic Graphs (DAGs)

• Creating and managing DAGs in Airflow.
• Defining tasks and dependencies within DAGs.
• Utilizing Airflow operators for various tasks.
• Implementing branching and conditional logic.
• Best practices for DAG design.

Module 3: Scheduling and Triggering DAGs

• Utilizing Airflow's scheduler for automated workflows.
• Implementing various scheduling strategies (cron, intervals).
• Triggering DAGs based on events and dependencies.
• Handling backfills and missed runs.
• Best practices for scheduling.

Module 4: Airflow Operators and Tasks

• Utilizing various Airflow operators (BashOperator, PythonOperator, etc.).
• Implementing custom operators and hooks.
• Managing task dependencies and execution order.
• Implementing data transformations and ETL processes.
• Best practices for task implementation.

Module 5: Data Dependencies and Task Management

• Managing data dependencies between tasks.
• Implementing data quality checks and validation.
• Handling task retries and error handling.
• Utilizing Airflow's XCom for data sharing.
• Best practices for dependency management.

Module 6: Monitoring and Troubleshooting Airflow Workflows

• Monitoring Airflow workflows using the Airflow UI.
• Analyzing Airflow logs and metrics.
• Troubleshooting common Airflow issues.
• Implementing alerting and notifications.
• Best practices for monitoring.

Module 7: Airflow Integrations and Connections

• Integrating Airflow with various data sources (databases, APIs, etc.).
• Utilizing Airflow connections for secure access.
• Implementing data transfers and data synchronization.
• Integrating Airflow with cloud services (AWS, Azure, GCP).
• Best practices for integrations.

Module 8: Data Quality and Validation in Airflow

• Implementing data quality checks within Airflow pipelines.
• Utilizing data validation operators and hooks.
• Implementing data profiling and data reconciliation.
• Handling data errors and exceptions.
• Best practices for data quality.

Module 9: ETL Automation with Airflow

• Automating ETL processes using Airflow.
• Implementing data transformations and aggregations.
• Utilizing Airflow for data warehousing and data lakes.
• Building end-to-end data pipelines for BI.
• Best practices for ETL automation.

Module 10: Airflow Deployment and Productionization

• Deploying Airflow in production environments.
• Utilizing containerization and orchestration (Docker, Kubernetes).
• Scaling Airflow for high-throughput workflows.
• Managing Airflow configurations and deployments.
• Best practices for production deployment.

Module 11: Data Security and Access Control in Airflow

• Implementing data security in Airflow workflows.
• Utilizing authentication and authorization.
• Implementing data encryption and access control.
• Auditing and compliance in Airflow.
• Best practices for data security.

Module 12: Advanced Airflow Features and Plugins

• Utilizing advanced Airflow features (sub-DAGs, task groups).
• Implementing custom plugins and extensions.
• Utilizing Airflow's API and command-line interface.
• Exploring advanced Airflow configurations.
• Best practices for advanced usage.

Module 13: Airflow in Cloud Environments

• Deploying Airflow on cloud platforms (AWS, Azure, GCP).
• Utilizing cloud-based Airflow services.
• Managing cloud resources for Airflow.
• Best practices for cloud deployment.

Module 14: Airflow and Data Governance

• Implementing data governance policies in Airflow.
• Utilizing metadata management tools.
• Implementing data lineage and data dictionary.
• Best practices for data governance.

Module 15: Future Trends in Airflow and Data Orchestration

• Emerging trends in data pipeline orchestration.
• Utilizing AI and automation in Airflow workflows.
• Implementing real-time data orchestration.
• Best practices for future Airflow.

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.