Data Engineering Solutions Lab
The Data Engineering Solutions Lab (DSL) is a 10-Day, focused, immersive learning experience that rapidly upskills your team on Google Cloud data engineering principles and best practices. The program combines five days of expert-led training sessions on key data engineering concepts (e.g., data warehousing, pipelines, data quality) with hands-on labs and a five-day collaborative capstone project. The capstone challenges participants to address a real-world use case using Google Cloud tools.
Ce que vous allez apprendre
- Accelerate data engineering adoption: Master essential skills and best practices quickly, enabling faster implementation and value realization.
- Boost data-driven decision making: Equip your team to confidently leverage Google Cloud's powerful data tools and services for deeper insights and informed actions.
- Shorten the path to ML success: Build a strong data engineering foundation to prepare your organization for successful machine learning implementation.
Prérequis
- Completion of 'Modernizing Data Lakes and Data Warehouses with Google Cloud' or equivalent Google Cloud experience
- Completion of 'Building Batch Data Pipelines on Google Cloud' or equivalent Google Cloud experience
- Completion of 'Building Resilient Streaming Analytics Systems on Google Cloud' or equivalent Google Cloud experience
Public cible
- Data engineering teams
Programme de la Formation
2 modules pour maîtriser les fondamentaux
Sujets abordés
- →Data warehousing
- →Pipelines
- →Data quality
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
- 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).
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
Aucune date ne vous convient ?
Nous organisons régulièrement de nouvelles sessions. Contactez-nous pour connaître les prochaines dates disponibles ou pour organiser une session à la date de votre choix.
S'inscrire à une date personnaliséeFormer plusieurs collaborateurs
- Tarifs dégressifs (plusieurs places)
- Session privée ou sur-mesure
- En présentiel ou à distance