GCP400DSL

Data 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.

Google Cloud
✓ Official training Google CloudLevel Advanced⏱️ 10 days (70h)

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

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Upcoming sessions

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We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.

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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.

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