GCP200DE
Data Engineering on Google Cloud
Get hands-on experience designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hands-on labs to show you how to design data processing systems, build end-to-end data pipelines, and analyze data. This course covers structured, unstructured, and streaming data.
Ce que vous allez apprendre
- Design and build data processing systems on Google Cloud.
- Process batch and streaming data by implementing autoscaling data pipelines on Dataflow.
- Derive business insights from extremely large datasets using BigQuery.
- Leverage unstructured data using Spark and ML APIs on Dataproc.
- Enable instant insights from streaming data.
Prérequis
- Prior Google Cloud experience 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
18 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 and/or the gcloud CLI.
Sujets abordés
- →The role of a data engineer
- →Data sources versus data syncs
- →Data formats
- →Storage solution options on Google Cloud
- →Metadata management options on Google Cloud
- →Share datasets using Analytics Hub
Activités
Lab: Loading Data into BigQuery
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.
Prochaines sessions
9 février 2026
Distanciel • Français
27 avril 2026
Distanciel • Français
29 juin 2026
Distanciel • Français
31 août 2026
Distanciel • Français
26 octobre 2026
Distanciel • Français
14 décembre 2026
Distanciel • Français
Former plusieurs collaborateurs
- Tarifs dégressifs (plusieurs places)
- Session privée ou sur-mesure
- En présentiel ou à distance