GCP200DATAPLEX

Managing a Data Mesh with Dataplex

Dataplex is an intelligent data fabric that enables organizations to centrally discover, manage, monitor, and govern their data across data lakes, data warehouses, and data marts. You can use Dataplex to build a data mesh architecture to decentralize data ownership among domain data owners. In this course, you will learn how to discover, manage, monitor, and govern your data across data lakes, data warehouses, and data marts through guided lectures and independent exercises using sample data.

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

Ce que vous allez apprendre

Identify the importance of a modern data platform
Configure and set up Dataplex
Secure data lakes, zones, and assets
Implement tagging for resources and use tags to search for assets
Process data using Dataplex tasks
Design, execute and report on data quality processes

Prérequis

Completion of the "Modernizing Data Lakes and Data Warehouses with Google Cloud" and "Building Batch Data Pipelines on Google Cloud" courses in the "Data Engineer" learning path or equivalent experience using Google Cloud.

Public cible

Customers

Programme de la Formation

7 modules pour maîtriser les fondamentaux

Objectifs

Identify the importance of a modern data platform
Explain the role of Dataplex on Google Cloud

Sujets abordés

→Modern Data Platforms and Data-Oriented Design
→Pillars of Data Governance
→What is Dataplex?
→Dataplex Capabilities
→Dataplex compared with other products on Google Cloud

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.