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Build Data Lakes and Data Warehouses on Google Cloud

IntermediateSkill Level

4.9+

Updated 06/2026

Build modern data lakehouses on Google Cloud using BigQuery, Cloud Storage, Apache Iceberg, BigLake, federated queries, and data governance tools.

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3 hr 48 min

52 Exercises

2,800 XP

Statement of Accomplishment

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Course Description

While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

Prerequisites

There are no prerequisites for this course

1

Course introduction

Introduce the learner to the topics that will be covered in the course and the skills they will learn.

Course Introduction

2

Introduction to Modern Data Engineering on Google Cloud

This module introduces the foundational concepts of data lakes and data warehouses, setting the stage for modern architectures on Google Cloud.

Module 1: Introduction to modern data engineering on Google Cloud

The classics: Data lakes and data warehouses

The modern approach: Data lakehouse

Choosing the right architecture

Data warehouses, lakes, and lakehouses concepts — Question 1

Data warehouses, lakes, and lakehouses concepts — Question 2

Data warehouses, lakes, and lakehouses concepts — Question 3

Data warehouses, lakes, and lakehouses concepts — Question 4

3

Building a Data Lakehouse with Cloud Storage, Open Formats, and BigQuery

This module details the concept of a lakehouse and introduces the Google Cloud products most commonly used to build a modern data lakehouse using open-source formats.

Module 2: Building a data lakehouse with Cloud Storage, open formats, and BigQuery

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

Data lakehouse features and benefits — Question 1

Data lakehouse features and benefits — Question 2

Data lakehouse features and benefits — Question 3

Data lakehouse features and benefits — Question 4

Federated query with BigQuery

4

Modernizing Data Warehouses with BigQuery and Lakehouse

This module explores BigQuery as the cornerstone of a modern data warehouse and introduces Lakehouse for unifying access across the data lake and warehouse.

Module 3: Modernizing data warehouses with BigQuery and Lakehouse

BigQuery fundamentals

Partitioning and clustering in BigQuery

Introducing Lakehouse and external tables

Querying external data and Iceberg tables

BigQuery and Lakehouse — Question 1

BigQuery and Lakehouse — Question 2

BigQuery and Lakehouse — Question 3

BigQuery and Lakehouse — Question 4

BigQuery and Lakehouse — Question 5

BigQuery and Lakehouse — Question 6

BigQuery and Lakehouse — Question 7

BigQuery and Lakehouse — Question 8

5

Advanced Lakehouse Patterns and Data Governance

This module focuses on advanced architectural patterns for the lakehouse, including data processing, orchestration, and comprehensive data governance across BigQuery, Cloud Storage, and Lakehouse.

Module 4: Advanced lakehouse patterns and data governance

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

Security and ML in the lakehouse — Question 1

Security and ML in the lakehouse — Question 2

Security and ML in the lakehouse — Question 3

Security and ML in the lakehouse — Question 4

Security and ML in the lakehouse — Question 5

Security and ML in the lakehouse — Question 6

Security and ML in the lakehouse — Question 7

Security and ML in the lakehouse — Question 8

6

Labs and Best Practices

This module provides labs to deepen skills in the tools and technologies used by a lakehouse on Google Cloud and an overview of best practices, common mistakes, and future trends

Module 5: Labs and best practices

Getting Started with BigQuery ML

Vector search with BigQuery

Review and best practices

7

Course Summary

Summarize the architectural and operational capabilities of the BigQuery-centric data lakehouse, covering governance, advanced analytics, and machine learning

Summary and next steps

Build Data Lakes and Data Warehouses on Google Cloud

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