Training Google Cloud

Training goals

code: G-BDLDWGC | version: 3.0

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.

 

What you'll learn

  • Differentiate between data lakes and data warehouses.
  • Explore use-cases for each type of storage and the available data lake and warehouse solutions on Google Cloud.
  • Discuss the role of a data engineer and the benefits of a successful data pipeline to business operations.
  • Examine why data engineering should be done in a cloud environment.

 

Audience

This course is designed for Data Engineers, Data Analysts and Data Architects.

 

 

Conspect Show list

  • Introduction to Modern Data Engineering on Google Cloud
    • Topics
      • The classics: Data lakes and data warehouses
      • The modern approach: Data lake house
      • Choosing the right architecture
    • Objectives
      • Compare and contrast data lake, data warehouse, and data lake house architectures
      • Evaluate the benefits of the lake house approach
    • Activities
      • Quiz
  • Building a data lake house with Cloud Storage, open formats, and BigQuery
    • Topics
      • 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
    • 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.
    • Activities
      • Quiz
      • Lab: Federated Query with BigQuery
  • Modernizing Data Warehouses with BigQuery and BigLake
    • Topics
      • BigQuery fundamentals
      • Partitioning and clustering in BigQuery
      • Introducing BigLake and external tables
    • 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.
    • Activities
      • Quiz
      • Lab: Querying External Data and Iceberg Tables
  • Advanced lakehouse patterns and data governance
    • Topics
      • 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
    • 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.
    • Activities
      • Quiz
  • Labs and best practices
    • Topics
      • Review
      • Best practices
    • Objectives
      • Reinforce the core principles of Google Cloud's data platform
    • Activities
      • Lab: Getting Started with BigQuery ML
      • Lab: Vector Search with BigQuery
Download conspect training as PDF

Additional information

Prerequisites
  • Basic understanding of the principles and activities associated with data engineering
  • Familiarity with SQL or data management principles
  • Familiarity with data warehouse or data lake architecture
Difficulty level
Duration 1 day
Certificate

The participants will obtain certificates signed by Google Cloud (course completion).
Trainer

Authorized Google Cloud Trainer

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