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.
What you will learn
- 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.
Prerequisites
- 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.
Target audience
- Data engineers, Database administrators, System administrators
Training Program
6 modules to master the fundamentals
Objectives
- 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.
Topics covered
- →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
Activities
Lab: Loading Data into BigQuery
Quiz
Objectives
- Explain the baseline Google Cloud data replication and migration architecture.
- Understand the options and use cases for the gcloud command-line tool.
- Explain the functionality and use cases for Storage Transfer Service.
- Explain the functionality and use cases for Transfer Appliance.
- Understand the features and deployment of Datastream.
Topics covered
- →Replication and migration architecture
- →The gcloud command-line tool
- →Moving datasets
- →Datastream
Activities
Lab: Datastream: PostgreSQL Replication to BigQuery (optional for ILT)
Quiz
Objectives
- Explain the baseline extract and load architecture diagram.
- Understand the options of the bq command-line tool.
- Explain the functionality and use cases for BigQuery Data Transfer Service.
- Explain the functionality and use cases for BigLake as a non-extract-load pattern.
Topics covered
- →Extract and load architecture
- →The bq command-line tool
- →BigQuery Data Transfer Service
- →BigLake
Activities
Lab: BigLake: Qwik Start
Quiz
Objectives
- Explain the baseline extract, load, and transform architecture diagram.
- Understand a common ELT pipeline on Google Cloud.
- Learn about BigQuery's SQL scripting and scheduling capabilities.
- Explain the functionality and use cases for Dataform.
Topics covered
- →Extract, load, and transform (ELT) architecture
- →SQL scripting and scheduling with BigQuery
- →Dataform
Activities
Lab: Create and Execute a SQL Workflow in Dataform
Quiz
Objectives
- Explain the baseline extract, transform, and load architecture diagram.
- Learn about the GUI tools on Google Cloud used for ETL data pipelines.
- Explain batch data processing using Dataproc.
- Learn how to use Dataproc Serverless for Spark for ETL.
- Explain streaming data processing options.
- Explain the role Bigtable plays in data pipelines.
Topics covered
- →Extract, transform, and load (ETL) architecture
- →Google Cloud GUI tools for ETL data pipelines
- →Batch data processing using Dataproc
- →Streaming data processing options
- →Bigtable and data pipelines
Activities
Lab: Use Dataproc Serverless for Spark to Load BigQuery (optional for ILT)
Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
Quiz
Objectives
- Explain the automation patterns and options available for pipelines.
- Learn about Cloud Scheduler and Workflows.
- Learn about Cloud Composer.
- Learn about Cloud Run functions.
- Explain the functionality and automation use cases for Eventarc.
Topics covered
- →Automation patterns and options for pipelines
- →Cloud Scheduler and Workflows
- →Cloud Composer
- →Cloud Run Functions
- →Eventarc
Activities
Lab: Use Cloud Run Functions to Load BigQuery (optional for ILT)
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.
Upcoming sessions
February 5, 2026
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April 21, 2026
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June 25, 2026
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August 27, 2026
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October 22, 2026
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December 10, 2026
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January 26, 2026
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March 31, 2026
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May 29, 2026
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July 31, 2026
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September 30, 2026
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November 27, 2026
Distanciel • Français
March 31, 2026
Distanciel • Français
May 29, 2026
Distanciel • Français
July 31, 2026
Distanciel • Français
September 30, 2026
Distanciel • Français
November 27, 2026
Distanciel • Français
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote