GCP100DE

Introduction to Data Engineering on Google Cloud

In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.

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

Ce que vous allez apprendre

Understand the role of a data engineer.
Identify data engineering tasks and core components used on Google Cloud.
Understand how to create and deploy data pipelines of varying patterns on Google Cloud.
Identify and utilize various automation techniques on Google Cloud.

Prérequis

Prior Google Cloud experience at the fundamental level using Cloud Shell and accessing products from the Google Cloud console.
Basic proficiency with a common query language such as SQL.
Experience with data modeling and ETL (extract, transform, load) activities.
Experience developing applications using a common programming language such as Python.

Public cible

Data engineers, Database administrators, System administrators

Programme de la Formation

6 modules pour maîtriser les fondamentaux

Objectifs

Explain the role of a data engineer.
Understand the differences between a data source and a data sink.
Explain the different types of data formats.
Explain the storage solution options on Google Cloud.
Learn about the metadata management options on Google Cloud.
Understand how to share datasets with ease using Analytics Hub.
Understand how to load data into BigQuery using the Google Cloud console or the gcloud CLI.

Sujets abordés

→The role of a data engineer
→Data sources versus data sinks
→Data formats
→Storage solution options on Google Cloud
→Metadata management options on Google Cloud
→Sharing datasets using Analytics Hub

Activités

Lab: Loading Data into BigQuery

Quiz

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.