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    Modernizing Data Lakes and Data Warehouses with Google Cloud (MDLDW)

    Google Cloud
    The two main components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.

    Modernizing Data Lakes and Data Warehouses with Google Cloud (MDLDW) Objectives

    • 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.

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    Key Point of Training Programs

    • Modernizing Data Lakes and Data Warehouses with Google Cloud (MDLDW) Prerequisites

      Who should attend
      This course is intended for developers who are responsible for querying datasets, visualizing query results, and creating reports.

      Specific job roles include:

      Data engineer
      Data analyst
      Database administrators
      Big data architects
      Certifications
      This course is part of the following Certifications:

      Google Cloud Certified Professional Data Engineer
      Prerequisites
      Basic proficiency with a common query language such as SQL.

    • Modernizing Data Lakes and Data Warehouses with Google Cloud (MDLDW) Course Format

      Live Virtual Course

    • Modernizing Data Lakes and Data Warehouses with Google Cloud (MDLDW) Outline

      Module 1 - Introduction to Data Engineering
      Topics:

      The role of a data engineer
      Data engineering challenges
      Introduction to BigQuery
      Data lakes and data warehouses
      Transactional databases versus data warehouses
      Partnering effectively with other data teams
      Managing data access and governance
      Build production-ready pipelines
      Google Cloud customer case study
      Objectives:

      Discuss the role of a data engineer.
      Discuss benefits of doing data engineering in the cloud.
      Discuss challenges of data engineering practice and how building data pipelines in the cloud helps to address these.
      Review and understand the purpose of a data lake versus a data warehouse, and when to use which.
      Module 2 - Building a Data Lake
      Topics:

      Introduction to data lakes
      Data storage and ETL options on Google Cloud
      Building a data lake by using Cloud Storage
      Securing Cloud Storage
      Storing all sorts of data types
      Cloud SQL as your OLTP system
      Objectives:

      Discuss why Cloud Storage is a great option to build a data lake on Google Cloud.
      Explain how to use Cloud SQL for a relational data lake.
      Module 3 - Building a Data Warehouse
      Topics:

      The modern data warehouse
      Introduction to BigQuery
      Getting started with BigQuery
      Loading data into BigQuery
      Exploring schemas
      Schema design
      Nested and repeated fields
      Optimizing with partitioning and clustering
      Objectives:

      Discuss the requirements of a modern warehouse.
      Explain why BigQuery is the scalable data warehousing solution on Google Cloud.
      Discuss the core concepts of BigQuery and review options of loading data into BigQuery.