GCP200COMPOSER

Workflow Orchestration with Cloud Composer

Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. Composer enables you to create, schedule, monitor, and manage workflow pipelines that span across clouds and on-premises data centers. In this course, you will learn about Apache Airflow and its implementation via Cloud Composer. You will learn how to provision Composer instances, create and manage Airflow DAGs on Composer, and perform tasks such as testing, debugging, and monitoring of Airflow DAGs.

✓ Formation officielle Google CloudNiveau Intermediate⏱️ 1 jour (7h)

Ce que vous allez apprendre

Explore Apache Airflow and Cloud Composer as workflow orchestration solutions.
Create and manage Airflow DAGs following best practices.
Test and debug Airflow DAGs.
Monitor and observe Airflow DAGs on Cloud Composer.

Prérequis

Completion of "Building Batch Data Pipelines on Google Cloud" or equivalent knowledge of data analytics and engineering on Google Cloud.

Public cible

Customers

Programme de la Formation

3 modules pour maîtriser les fondamentaux

Objectifs

Explore Apache Airflow and Cloud Composer.
Provision Cloud Composer instances.
Explore the Airflow and Composer UIs.

Sujets abordés

→Data Engineer's need for Workflow Orchestration
→Introduction to Apache Airflow
→Cloud Composer
→Environment Setup
→Using the Composer and Airflow

Activités

Lab: Provisioning Cloud Composer

Quality Process

SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach

Teaching Methods Used

Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
Guided Labs — Guided practical exercises on software, hardware, or technical environments.
Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).

Evaluation and Monitoring System

The achievement of training objectives is evaluated at multiple levels to ensure quality:

Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.