Technologies / Programming

Apache Airflow - data flow orchestration

Advanced training in dataflow orchestration using Apache Airflow. The program focuses on the design, implementation and management of complex data processing pipelines. Participants, through hands-on workshops, will learn techniques for automating ETL processes, task monitoring and error handling. The training combines theory with intensive hands-on exercises, enabling real-world experience.

Issues

Apache Airflow architecture
Design and implementation of DAGs
Operators and sensors
Task management and dependencies
Scheduling mechanisms
Monitoring and error handling
Scaling and high availability
Versioning and testing
Continuous Integration/Deployment
Security and access control
Integration with external systems
Performance optimization

This training is part of the path:

Data Engineer

Benefits

Thorough knowledge of Apache Airflow architecture and mechanisms
Practical experience in designing scalable data pipelines
Ability to create advanced DAGs and custom components
Ability to effectively monitor and troubleshoot data flows
Knowledge of ETL process automation best practices
Expertise in optimizing and managing the performance of data flows

Who is this training for?

Data and ETL engineers

Python developers specializing in data engineering

Big Data systems administrators

DevOps specialists

Data pipeline solution architects

Data analysts with programming experience

MLOps engineers

Prerequisites

Knowledge of the Python language at an intermediate level
Experience working with data processing systems
Basic knowledge of ETL processes
Knowledge of DevOps concepts

Training program

Setting up the production environment
Scaling up and managing resources
Integration with external systems
Design of DAGs and operators
Advanced design patterns of DAGs

Implementation of hooks and sensors
Managing dependencies between tasks
Monitoring and error handling
Performance monitoring strategies
Implementation of retry and timeout mechanisms
Logging and auditing operations

Optimization and best practices
Performance optimization techniques

Versioning and testing of DAGs
Continuous Deployment for Airflow

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

What are the prerequisites for this training?

For Apache Airflow - data flow orchestration we recommend: Knowledge of the Python language at an intermediate level; Experience working with data processing systems; Basic knowledge of ETL processes.

What is the format and duration of this training?

The training lasts 3 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Data and ETL engineers; Python developers specializing in data engineering; Big Data systems administrators.