GCP200CDF

Data Integration with Cloud Data Fusion

This 2-day course introduces learners to Google Cloud's data integration capability using Cloud Data Fusion. In this course, we discuss challenges with data integration and the need for a data integration platform (middleware). We then discuss how Cloud Data Fusion can help to effectively integrate data from a variety of sources and formats and generate insights. We take a look at Cloud Data Fusion's main components and how they work, how to process batch data and real time streaming data with visual pipeline design, rich tracking of metadata and data lineage, and how to deploy data pipelines on various execution engines.

✓ Formation officielle Google CloudNiveau Intermediate⏱️ 2 jours (14h)

Ce que vous allez apprendre

Identify the need of data integration
Understand the capabilities Cloud Data Fusion provides as a data integration platform
Identify use cases for possible implementation with Cloud Data Fusion
List the core components of Cloud Data Fusion
Design and execute batch and real time data processing pipelines
Work with Wrangler to build data transformations
Use connectors to integrate data from various sources and formats
Configure execution environment; Monitor and Troubleshoot pipeline execution
Understand the relationship between metadata and data lineage

Prérequis

Completed "Introduction to Data Engineering"

Public cible

Data Engineer, Data Analysts

Programme de la Formation

9 modules pour maîtriser les fondamentaux

Objectifs

Introduce the course objectives

Sujets abordés

→Course Introduction

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.