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.
What you will learn
- 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.
Prerequisites
- 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
Target audience
- Data engineering teams
Training Program
2 modules to master the fundamentals
Topics covered
- →Data warehousing
- →Pipelines
- →Data quality
Topics covered
- →Address a real-world use case using Google Cloud tools
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.
Upcoming sessions
No date suits you?
We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.
Register for a custom dateTrain multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote