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Apache Airflow Corporate Training Program for Employees
Give your data engineering teams the skills to build, schedule, and monitor reliable data pipelines with Apache Airflow, through instructor-led corporate training customized to your data stack.
(Virtual / On-site / Off-site)
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Overview
What is Apache Airflow? Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring data workflows as code, using Python-defined DAGs (Directed Acyclic Graphs). For data teams, it turns fragile, manual data pipelines into version-controlled, observable, and reliable workflows that run on schedule and recover from failure.
As organizations move more decisions to data, this program helps your teams orchestrate complex ETL and machine learning pipelines confidently with Apache Airflow. Empower your people with expert-led on-site, off-site, and virtual sessions delivered by Edstellar, a premier corporate training provider serving organizations worldwide. Built around your goals, the program turns Apache Airflow skills into lasting capabilities that lift performance across data engineering, analytics, and platform teams.
Delivered instructor-led and fully customized to your data stack, the training is available worldwide in person and virtually across popular languages, and covers Apache Airflow 2.x and 3.0 including the TaskFlow API, dynamic task mapping, and the Celery and Kubernetes executors. Your organization gains faster and more dependable pipelines, fewer late-night failures, and engineers who can scale orchestration as data volumes grow. Request a tailored proposal to align the curriculum with your tools and use cases.
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- Author maintainable workflows as Python DAGs, with clear task dependencies and reliable scheduling.
- Build robust ETL and ELT pipelines that move and transform data dependably across systems.
- Configure operators, sensors, hooks, and connections to integrate Airflow with your data sources.
- Monitor, troubleshoot, and recover failed tasks using the Airflow UI, logs, and alerting.
- Apply best practices for idempotency, retries, backfills, and dynamic pipeline generation.
- Deploy and scale Airflow securely using executors, environments, and CI/CD for orchestration.
- Apache Airflow Foundations and Architecture
- Core concepts: DAGs, tasks, operators, and the scheduler
- Airflow architecture: webserver, scheduler, metadata database, and executors
- Installing and configuring Airflow for a team environment
- Navigating the Airflow UI and command line interface
- Authoring Workflows with Python DAGs
- Writing DAGs as code with schedules and default arguments
- Setting task dependencies and execution order
- Using the TaskFlow API, variables, templating, and XComs to pass data
- Structuring DAGs for readability and reuse
- Operators, Sensors, Hooks, and Connections
- Common operators (Python, Bash, SQL) and provider packages
- Sensors and triggers for event-driven pipelines
- Hooks and connections to databases, cloud services, and APIs
- Managing secrets and connections securely
- Building Reliable ETL and ELT Pipelines
- Designing idempotent, restartable data pipelines
- Backfills, catchup, and handling late-arriving data
- Branching, dynamic task mapping, and TaskGroups
- Embedding data quality checks within pipelines
- Scheduling, Monitoring, and Troubleshooting
- Scheduling, SLAs, and trigger rules
- Logging, metrics, and alerting for pipeline health
- Diagnosing and recovering failed tasks
- Performance tuning and resource management
- Implementing Advanced Concepts in Airflow
- Executors at scale: Celery and Kubernetes executors
- Deploying Airflow with CI/CD and environment management
- Securing Airflow: roles, role-based access control, and governance
- Integrating Airflow 3.0 with modern data and machine learning stacks
- Data Analytics Managers
- Data Scientists
- ETL Developers
- Data Analysts
- Cloud Engineers
- Software Engineers
- Business Intelligence Developers
- Workflow Coordinators
- Big Data Specialists
- Database Administrators
- Application Developers
- DevOps Engineers
Participants should be comfortable with basic Python programming and core data concepts such as databases and SQL. Familiarity with the command line and your team's data sources is helpful but not required, as the program includes guided setup. Edstellar tailors the starting point to your team's experience, so both engineers new to orchestration and those refreshing their Airflow skills can take part productively.
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64 hours of group training (includes VILT/In-person On-site)
Tailored for SMBs
160 hours of group training (includes VILT/In-person On-site)
Ideal for growing SMBs
Tailor-Made Trainee Licenses with Our Exclusive Training Packages!
400 hours of group training (includes VILT/In-person On-site)
Designed for large corporations
Tailor-Made Trainee Licenses with Our Exclusive Training Packages!
Unlimited duration
Designed for large corporations
Experienced Trainers
Our trainers are drawn from a vetted global network and bring years of industry expertise, keeping every session practical and impactful.
Proven Quality
With a strong global track record, Edstellar is known for quality and engaging delivery.
Industry-Relevant Curriculum
Our programs are built by experts to match the demands of today's industry.
Fully Customizable
Every program can be tailored to your organization's goals.
Comprehensive Support
We provide pre- and post-session support for a complete learning experience.
Global Multi-Location & Multilingual Training Delivery
We deliver in multiple languages to support diverse global teams.
Hear from Organizations We've Trained
Recognition That Motivates Your Team
Frequently Asked Questions
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