Nairobi, Kenya

254728269396

Apache Airflow For Workflow Orchestration Training

As data ecosystems grow in complexity, managing fragile script executions and unmonitored data pipelines introduces significant operational risk and data reliability issues. Our Apache Airflow for Wor...

img 15 Topics

img 10 Days

Big Data, Predictive Modelling & Data Engineering Trainings.
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ONSITE OR VIRTUAL

Aug 24 - Aug 28
Programme Overview
Training Description

Who Should Attend

This course is ideal for;

  1. Data Engineers
  2. Data Scientists
  3. DevOps Engineers
  4. Data Analysts
  5. System Administrators
  6. Software Developers
  7. Anyone needing Apache Airflow skills
Session Objectives
  • Understand the fundamentals of Apache Airflow for workflow orchestration.
  • Master DAG (Directed Acyclic Graph) creation and management.
  • Utilize Airflow operators for various data processing tasks.
  • Implement task scheduling and dependency management.
  • Design and build complex data workflows with Airflow.
  • Optimize Airflow configurations for performance and reliability.
  • Troubleshoot and address common issues in Airflow deployments.
  • Implement data quality checks and validation in Airflow workflows.
  • Implement data quality checks and validation in Airflow workflows.
  • Integrate Airflow with various data storage and processing systems.
  • Understand how to handle large datasets and distributed processing with Airflow.
  • Explore advanced Airflow features (e.g., custom operators, sub-DAGs).
  • Apply real world use cases for Apache Airflow in data engineering.
  • Leverage Airflow's ecosystem for efficient workflow management.
About the Course

As data ecosystems grow in complexity, managing fragile script executions and unmonitored data pipelines introduces significant operational risk and data reliability issues. Our Apache Airflow for Workflow Orchestration Training Course solves this challenge by equipping technical teams with the skills needed to build, automate, and maintain resilient enterprise data pipelines. Guided by senior data engineering specialists, you will master the creation of Directed Acyclic Graphs (DAGs) in Python, implement custom operators, manage task dependencies, and configure robust scheduling frameworks. By combining hands-on pipeline construction with proactive monitoring, backfilling strategies, and alert management, this program prepares Data Engineers, Data Scientists, and DevOps Engineers to streamline complex processing workflows and ensure continuous data delivery across modern data environments.

Curriculum & Topics

15 Topics | 10 Days

  • play Subtopic 1.1: Fundamentals of Apache Airflow for workflow orchestration.

  • play Subtopic 1.2: Overview of DAGs, operators, and task scheduling.

  • play Subtopic 1.3: Setting up an Airflow development environment.

  • play Subtopic 1.4: Introduction to Airflow architecture and components.

  • play Subtopic 1.5: Best practices for Airflow.

  • play Subtopic 2.1: Mastering DAG (Directed Acyclic Graph) creation and management.

  • play Subtopic 2.2: Utilizing Python for DAG definition.

  • play Subtopic 2.3: Designing and building complex DAGs with dependencies.

  • play Subtopic 2.4: Optimizing DAGs for workflow efficiency.

  • play Subtopic 2.5: Best practices for DAG creation.

  • play Subtopic 3.1: Utilizing Airflow operators for various data processing tasks.

  • play Subtopic 3.2: Implementing operators for data ingestion, transformation, and loading.

  • play Subtopic 3.3: Designing and building custom operators.

  • play Subtopic 3.4: Optimizing operators for specific data processing needs.

  • play Subtopic 3.5: Best practices for Airflow operators.

  • play Subtopic 4.1: Implementing task scheduling and dependency management.

  • play Subtopic 4.2: Utilizing Airflow schedulers and triggers.

  • play Subtopic 4.3: Designing and building scheduled workflows.

  • play Subtopic 4.4: Optimizing task dependencies for workflow reliability.

  • play Subtopic 4.5: Best practices for scheduling.

  • play Subtopic 5.1: Designing and building complex data workflows with Airflow.

  • play Subtopic 5.2: Implementing branching and looping in workflows.

  • play Subtopic 5.3: Utilizing sub-DAGs and external task dependencies.

  • play Subtopic 5.4: Optimizing workflows for specific data pipelines.

  • play Subtopic 5.5: Best practices for complex workflows.

  • play Subtopic 6.1: Optimizing Airflow configurations for performance and reliability.

  • play Subtopic 6.2: Utilizing Airflow configuration parameters.

  • play Subtopic 6.3: Implementing resource management and scaling.

  • play Subtopic 6.4: Designing efficient Airflow deployments.

  • play Subtopic 6.5: Best practices for configuration optimization.

  • play Subtopic 7.1: Debugging common issues in Airflow deployments.

  • play Subtopic 7.2: Analyzing Airflow logs and error messages.

  • play Subtopic 7.3: Utilizing troubleshooting techniques for problem resolution.

  • play Subtopic 7.4: Resolving common deployment errors.

  • play Subtopic 7.5: Best practices for troubleshooting.

  • play Subtopic 8.1: Implementing data quality checks and validation in Airflow workflows.

  • play Subtopic 8.2: Utilizing Airflow sensors and checks.

  • play Subtopic 8.3: Designing and building data quality workflows.

  • play Subtopic 8.4: Optimizing validation for data integrity.

  • play Subtopic 8.5: Best practices for data quality.

  • play Subtopic 9.1: Integrating Airflow with various data storage and processing systems.

  • play Subtopic 9.2: Utilizing Airflow hooks and connections.

  • play Subtopic 9.3: Implementing data integration with external databases and APIs.

  • play Subtopic 9.4: Optimizing integration for data retrieval and processing.

  • play Subtopic 9.5: Best practices for integration.

  • play Subtopic 10.1: Understanding how to handle large datasets and distributed processing with Airflow.

  • play Subtopic 10.2: Utilizing Airflow with distributed computing frameworks.

  • play Subtopic 10.3: Implementing data partitioning and parallel processing.

  • play Subtopic 10.4: Designing scalable data processing workflows.

  • play Subtopic 10.5: Best practices for large datasets.

  • play Subtopic 11.1: Exploring advanced Airflow features (custom operators, sub-DAGs).

  • play Subtopic 11.2: Utilizing custom operators for specialized tasks.

  • play Subtopic 11.3: Implementing sub-DAGs for modular workflows.

  • play Subtopic 11.4: Designing and building advanced Airflow solutions.

  • play Subtopic 11.5: Optimizing advanced techniques for specific applications.

  • play Subtopic 11.6: Best practices for advanced features.

  • play Subtopic 12.1: Implementing Airflow for ETL/ELT pipelines.

  • play Subtopic 12.2: Utilizing Airflow for machine learning workflows.

  • play Subtopic 12.3: Implementing Airflow for data warehousing automation.

  • play Subtopic 12.4: Utilizing Airflow for log processing and data analysis.

  • play Subtopic 12.5: Best practices for real-world applications.

  • play Subtopic 13.1: Utilizing Airflow tools and frameworks (Airflow UI, Airflow CLI).

  • play Subtopic 13.2: Implementing Airflow workflows with specific tools.

  • play Subtopic 13.3: Designing and building automated deployment workflows.

  • play Subtopic 13.4: Optimizing tool usage for efficient development.

  • play Subtopic 13.5: Best practices for tool implementation.

  • play Subtopic 14.1: Implementing workflow monitoring and logging in Airflow.

  • play Subtopic 14.2: Utilizing Airflow monitoring tools and metrics.

  • play Subtopic 14.3: Designing and building performance dashboards.

  • play Subtopic 14.4: Optimizing monitoring for real-time insights.

  • play Subtopic 14.5: Best practices for monitoring.

  • play Subtopic 15.1: Emerging trends in Airflow orchestration.

  • play Subtopic 15.2: Utilizing AI for workflow automation.

  • play Subtopic 15.3: Implementing Airflow in cloud-native environments.

  • play Subtopic 15.4: Best practices for future applications,

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$ 2,000

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This Programme Includes

Certificate of completion

Training manual

Reference materials

10 o'clock tea

Lunch

4 o'clock tea

Course Highlights

  • icon 10 Days Intensive Training

  • icon 15 Core Learning Topics

  • icon 10 Days Professional Sessions

  • icon Training Expert-led Delivery

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