Build Data Lakes and Warehouses on Google Cloud
Course 1492
1 DAY COURSE

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.

Build Data Lakes and Warehouses on Google Cloud Benefits

• Important Course Information:

  • Differentiate between a data lake, data warehouse, data lakehouse
  • Explain the data lakehouse concept and how it addresses the limitations of traditional data lakes and data warehouses
  • Identify various data sources that BigQuery can query
  • Recognize the capabilities of using BigQuery to create and access AI models

  Prerequisites:

  To benefit from this course participants should have familiarity, training, or experience with the principles and activities associated with data engineering, data warehouse or data lake architecture, SQL query language, or data management principles.

Google Data Lakes and Warehouses Course Outline

Learning Objectives

Module 01

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

Module 02

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

Module 03

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  04

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

Module 05

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

Module 06

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 07

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