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    Modernizing Data Lakes and Data Warehouses with Google Cloud

    Modernizing Data Lakes and Data Warehouses with Google Cloud

    magic_button Data Lake Data Warehouse
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    8 hours Intermediate universal_currency_alt 25 Credits

    The two key 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.

    This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

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    Course Info
    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.
    Prerequisites
    To benefit from this course, participants should have completed “Google Cloud Big Data and Machine Learning Fundamentals” or have equivalent experience. Participant should also have: • Basic proficiency with a common query language such as SQL. • Experience with data modeling and ETL (extract, transform, load) activities. • Experience with developing applications using a common programming language such as Python. • Familiarity with machine learning and/or statistics
    Audience
    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
    Available languages
    English, 日本語, español (Latinoamérica), français, português (Brasil), italiano, and 한국어
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