🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
GCP100DA

Introduction to Data Analytics on Google Cloud

This course is an introduction to data analytics on Google Cloud. It is designed for learners who have no prior experience with data analytics or Google Cloud. The course covers the basics of data analysis, including collection, storage, exploration, visualization, and sharing. It also introduces learners to Google Cloud's data analytics tools and services. Through video lectures, demos, quizzes, and hands-on labs, the course demonstrates how to go from raw data to impactful visualizations and dashboards.

Google Cloud
✓ Official training Google CloudLevel Fundamentals⏱️ 1 day (7h)

What you will learn

  • Describe the data analytics workflow on Google Cloud and summarize the different types of analytics.
  • Identify Google Cloud data analytics products and describe how each is used to work with data.
  • Describe data sources, data structures, and data storage options in Google Cloud.
  • Use BigQuery, Looker, and Looker Studio to answer data questions and influence business decisions.

Prerequisites

  • Basic familiarity with SQL
  • Basic understanding of data concepts such as data types (relational, non-relational) and storage (data lakes, data warehouses)

Target audience

  • Data analysts

Training Program

4 modules to master the fundamentals

Objectives
  • Detail and describe the data analytics workflow on Google Cloud.
  • Compare and contrast data sources and storage methods available in Google Cloud.
  • Compare how different data types can be used for data analytics.
Topics covered
  • →Data analytics workflow
  • →Data sources
  • →Storage methods
  • →Google Cloud data analytics products
  • →Data types
Activities

Quiz

Objectives
  • Describe BigQuery and the BigQuery solution architecture.
  • Derive insights from data by using BigQuery.
  • Use the BigQuery user interface to run basic queries.
Topics covered
  • →BigQuery services, capabilities, and organization
  • →Data storage
  • →Basic SQL
  • →Answering data-driven questions
Activities

Lab 1: BigQuery Qwik Start: Console

Lab 2: Introduction to SQL for BigQuery and Cloud SQL

Lab 3: BigLake: Qwik Start

Lab 4: Analyze data with Gemini Assistance

Quiz

Objectives
  • Manipulate a Looker Explore to answer data-driven questions.
  • Create a situation-appropriate visualization to highlight the answer for a data-driven question.
  • Choose between Looker and Looker Studio for data visualization and sharing.
  • Share visualizations with others.
Topics covered
  • →Looker data exploration terms and concepts
  • →Looks and dashboards
  • →Visualizations
  • →Report sharing
  • →Looker Studio
Activities

Lab 1: Looker Data Explorer—Qwik Start

Lab 2: Looker Data Studio—Qwik Start

Quiz

Objectives
  • Find resources for additional learning and support.
Topics covered
  • →Topic review
  • →Slides

Related Trainings

Google Cloud

Data Integration with Cloud Data Fusion

This 2-day course introduces learners to Google Cloud's data integration capability using Cloud Data Fusion. In this course, we discuss challenges with data integration and the need for a data integration platform (middleware). We then discuss how Cloud Data Fusion can help to effectively integrate data from a variety of sources and formats and generate insights. We take a look at Cloud Data Fusion's main components and how they work, how to process batch data and real time streaming data with visual pipeline design, rich tracking of metadata and data lineage, and how to deploy data pipelines on various execution engines.

2 d
Intermediate
Google Cloud

Managing a Data Mesh with Dataplex

Dataplex is an intelligent data fabric that enables organizations to centrally discover, manage, monitor, and govern their data across data lakes, data warehouses, and data marts. You can use Dataplex to build a data mesh architecture to decentralize data ownership among domain data owners. In this course, you will learn how to discover, manage, monitor, and govern your data across data lakes, data warehouses, and data marts through guided lectures and independent exercises using sample data.

2 d
Intermediate
View all trainings →

Upcoming sessions

June 3, 2026
Distanciel • Français
Register
July 7, 2026
Distanciel • Français
Register
August 19, 2026
Distanciel • Français
Register
September 11, 2026
Distanciel • Français
Register
October 19, 2026
Distanciel • Français
Register
November 13, 2026
Distanciel • Français
Register
December 16, 2026
Distanciel • Français
Register

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.

790€ excl. VAT

per learner

ReserveQuote