Build Data Lakes and Data Warehouses with Google Cloud
In this course, you will learn to differentiate data architectures and implement data lakehouse and pipeline concepts on Google Cloud. You will compare and contrast data lake, data warehouse, and data lakehouse architectures, and evaluate the benefits of the modern lakehouse approach.
Get hands-on experience building a data lakehouse with Cloud Storage, open formats like Apache Iceberg, and BigQuery as the central processing engine. Learn about data governance, security, and advanced analytics patterns for your unified data platform.
What you will learn
- Compare and contrast data lake, data warehouse, and data lakehouse architectures.
- Evaluate the benefits of the lakehouse approach and choose the right architecture.
- Build a data lakehouse with Cloud Storage, open formats, and BigQuery.
- Modernize data warehouses with BigQuery and BigLake.
- Implement data governance and security practices across a unified data platform.
Prerequisites
- Understanding of data engineering principles, including ETL/ELT processes, data modeling, and common data formats (Avro, Parquet, JSON)
- Familiarity with data architecture concepts, specifically Data Warehouses and Data Lakes
- Proficiency in SQL for data querying
- Proficiency in a common programming language (Python recommended)
- Familiarity with core Google Cloud concepts and services
Target audience
- Data Engineers, Data Analysts, Data Architects
Training Program
5 modules to master the fundamentals
Objectives
- Compare and contrast data lake, data warehouse, and data lakehouse architectures
- Evaluate the benefits of the lakehouse approach
Topics covered
- →The classics: Data lakes and data warehouses
- →The modern approach: Data lakehouse
- →Choosing the right architecture
Activities
Quiz
Objectives
- Discuss data storage options, including Cloud Storage for files, open table formats like Apache Iceberg, BigQuery for analytic data, and AlloyDB for operational data
- Understand the role of AlloyDB for operational data use cases
Topics covered
- →Building a data lake foundation
- →Introduction to Apache Iceberg open table format
- →BigQuery as the central processing engine
- →Combining operational data in AlloyDB
- →Combining operational and analytical data with federated queries
- →Real world use case
Activities
Quiz
Lab: Federated Query with BigQuery
Objectives
- Explain why BigQuery is a scalable data warehousing solution on Google Cloud
- Discuss the core concepts of BigQuery
- Understand BigLake's role in creating a unified lakehouse architecture and its integration with BigQuery for external data
- Learn how BigQuery natively interacts with Apache Iceberg tables via BigLake
Topics covered
- →BigQuery fundamentals
- →Partitioning and clustering in BigQuery
- →Introducing BigLake and external tables
Activities
Quiz
Lab: Querying External Data and Iceberg Tables
Objectives
- Implement robust data governance and security practices across the unified data platform, including sensitive data protection and metadata management
- Explore advanced analytics and machine learning directly on lakehouse data
Topics covered
- →Data governance and security in a unified platform
- →Demo: Data Loss Prevention
- →Analytics and machine learning on the lakehouse
- →Real-world lakehouse architectures and migration strategies
Activities
Quiz
Objectives
- Reinforce the core principles of Google Cloud's data platform
Topics covered
- →Review
- →Best practices
Activities
Lab: Getting Started with BigQuery ML
Lab: Vector Search with BigQuery
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