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Shoebur Rahman구매 담당자, Giz Bangladesh
강의 설명
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.
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커리큘럼
강의 개요
1
Course introduction
Introduce the learner to the topics that will be covered in the course and the skills they will learn.
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.
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.
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.
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.
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
7
Course Summary
Summarize the architectural and operational capabilities of the BigQuery-centric data lakehouse, covering governance, advanced analytics, and machine learning
R
Build Data Lakes and Data Warehouses on Google Cloud
강의
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