Data Science & Analytics · PPL

Apache Airflow Data Orchestration Certification

A specialist program designed to develop practical Apache Airflow skills for building, scheduling, monitoring, and managing automated data workflows and pipelines.

  • 3 DaysDuration
  • PPLAccredited
  • 3 LanguagesArabic · English · Hindi
  • ₹12,999.00 Per delegate

This course is accredited by PPL

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— The journey

Course Outline

What the programme covers, module by module.

Module 1: Introduction to Apache Airflow

  • Data orchestration concepts
  • Apache Airflow overview
  • Workflow automation
  • Airflow use cases
  • ETL and ELT orchestration
  • Airflow ecosystem

Module 2: Airflow Architecture

  • Scheduler
  • Webserver
  • Metadata database
  • Executor
  • Workers
  • Airflow components

Module 3: Airflow Environment Setup

  • Installation concepts
  • Project structure
  • Configuration files
  • Environment variables
  • Airflow CLI
  • Development environment

Module 4: Understanding DAGs

  • DAG fundamentals
  • DAG structure
  • DAG definitions
  • Task relationships
  • Workflow design
  • DAG lifecycle

Module 5: Building Your First DAG

  • DAG creation
  • Task definition
  • Dependencies
  • Scheduling
  • Start dates
  • DAG execution

Module 6: Airflow Operators

  • Operator concepts
  • PythonOperator
  • BashOperator
  • EmptyOperator
  • SQL operators
  • Custom operators

Module 7: Task Dependencies

  • Upstream tasks
  • Downstream tasks
  • Dependency patterns
  • Branching
  • Trigger rules
  • Workflow sequencing

Module 8: Scheduling & Timetables

  • Scheduling concepts
  • Cron expressions
  • Schedule intervals
  • Data intervals
  • Catchup
  • Backfilling

Module 9: Airflow TaskFlow API

  • TaskFlow concepts
  • Task decorators
  • Python functions
  • Task dependencies
  • Data passing
  • Workflow development

Module 10: XComs & Data Sharing

  • XCom concepts
  • Pushing values
  • Pulling values
  • Task communication
  • Data exchange patterns
  • XCom best practices

Module 11: Sensors

  • Sensor concepts
  • File sensors
  • SQL sensors
  • External task sensors
  • Waiting strategies
  • Sensor optimization

Module 12: Connections, Variables & Configuration

  • Airflow connections
  • Variables
  • Connection management
  • Runtime configuration
  • Environment configuration
  • Secure configuration practices

Module 13: Dynamic Workflows

  • Dynamic task mapping
  • Parameterized DAGs
  • Reusable workflows
  • Dynamic dependencies
  • Scalable task generation
  • Workflow flexibility

Module 14: Error Handling & Retries

  • Task failures
  • Retry configuration
  • Failure callbacks
  • Timeouts
  • Trigger rules
  • Recovery strategies

Module 15: Data Pipeline Orchestration

  • ETL pipelines
  • ELT workflows
  • Data ingestion
  • Transformation workflows
  • Pipeline dependencies
  • Data validation

Module 16: Database & Cloud Integration

  • SQL databases
  • Data warehouses
  • Object storage
  • Cloud services
  • Provider packages
  • Integration patterns

Module 17: Monitoring & Logging

  • Task logs
  • DAG monitoring
  • Workflow status
  • Performance monitoring
  • Alerting concepts
  • Troubleshooting

Module 18: Testing Airflow Workflows

  • DAG validation
  • Task testing
  • Unit testing concepts
  • Integration testing
  • Debugging
  • Pipeline quality checks

Module 19: Airflow Security & Access

  • Authentication concepts
  • Role-based access
  • Secrets management
  • Connection security
  • Environment security
  • Operational best practices

Module 20: Airflow Deployment & Scaling

  • Deployment architecture
  • Executor selection
  • Worker scaling
  • Containerized deployment
  • Kubernetes concepts
  • Production considerations

Module 21: Airflow Optimization & Best Practices

  • DAG optimization
  • Scheduler performance
  • Resource management
  • Idempotent tasks
  • Maintainable workflows
  • Production best practices

Module 22: Practical Data Orchestration Project

  • Pipeline architecture
  • DAG development
  • Task dependencies
  • Data integration
  • Monitoring and retries
  • Production workflow design
— 01.2 · Is it right for you?

Who it's for & what's included

Pick a delivery method to see exactly who it suits and everything you receive.

Who it's for

Classroom

Best for learners who want face-to-face tuition and to network with peers in person.

What's included

Everything you get

  • Live instructor on-site
  • Printed workbook & materials
  • Group exercises & case studies
Who it's for

Online Instructor-Led

Best for learners who want a live instructor and a fixed schedule, without the travel.

What's included

Everything you get

  • Live instructor via video call
  • Digital workbook & resources
  • Session recordings
Who it's for

Self-Paced

Best for self-motivated learners who need maximum flexibility around work and life.

What's included

Everything you get

  • On-demand video lessons
  • Interactive quizzes
  • 24/7 access on any device
— What you will master

Course Objectives

01

Understand Apache Airflow architecture and data orchestration concepts.

02

Design and develop DAGs for automated data workflows.

03

Configure operators, sensors, dependencies, schedules, and trigger rules.

04

Use TaskFlow API and XComs for effective workflow development.

05

Build and orchestrate ETL and ELT data pipelines.

06

Implement monitoring, logging, testing, and failure-handling strategies.

07

Integrate Airflow workflows with databases, warehouses, and cloud platforms.

08

Apply deployment, scaling, security, and optimization practices for production environments.

— Your learning path

Where this fits in your Data Science & Analytics journey

Click any stage to open its detail page. You are at Apache Airflow Data Orchestration Certification.

Start Build Advanced Mastery
— Questions answered

Frequently Asked Questions

What is Apache Airflow?
Apache Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows using programmable workflow definitions.
Who should attend this course?
The course is suitable for data engineers, analytics engineers, ETL developers, data platform professionals, and working professionals responsible for automated data pipelines.
Do I need previous programming experience?
Basic Python and SQL knowledge is recommended because Airflow workflows are commonly defined programmatically and frequently interact with databases and data platforms.
Which topics are covered?
The course covers Airflow architecture, DAGs, operators, scheduling, TaskFlow API, XComs, sensors, dynamic workflows, ETL orchestration, monitoring, testing, security, deployment, and optimization.
What practical skills will I develop?
You will develop skills in designing DAGs, automating data pipelines, managing dependencies, scheduling workflows, handling failures, monitoring pipelines, integrating data platforms, and operating Airflow environments.
— Trusted by learners

What our delegates say

★★★★★

"The structure, the practice exams, the instructor — all top tier. Passed first try."

AS
Aarti SharmaSenior Project Manager · TCS
★★★★★

"Best training I have attended. The content is exactly what modern projects need."

JD
James DonovanProgramme Director · Capgemini
★★★★★

"24/7 support actually means 24/7 — got help on my mock exam at 2am. Worth every dollar."

MO
Maya OkaforPMO Lead · Standard Bank

★ 4.8 / 5 from 12,000+ verified learner reviews on Trustpilot & Google.

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