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BigQuery for Data Warehousing

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BigQuery for Data Warehousing

6 个小时 Fundamental universal_currency_alt 25 个积分
Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

完成此课程中的所有活动即可赢取一枚徽章。完成挑战任务、游戏和课程即可收集 Qwiklabs 中的徽章。集齐所有徽章,彰显您的技能!

  • 实验

    BigQuery: Qwik Start - Command Line

    This hands-on lab shows you how to query public tables and load sample data into BigQuery using the Command Line Interface. Watch the short videos Get Meaningful Insights with Google BigQuery and BigQuery: Qwik Start - Qwiklabs Preview.

  • 实验

    Creating a Data Warehouse Through Joins and Unions

    This lab focuses on how to create new reporting tables using SQL JOINS and UNIONs.

  • 实验

    Creating Date-Partitioned Tables in BigQuery

    This lab focuses on how to query partitioned datasets and how to create your own dataset partitions to improve query performance, which reduces cost.

  • 实验

    Troubleshooting and Solving Data Join Pitfalls

    This lab focuses on how to reverse-engineer the relationships between data tables and the pitfalls to avoid when joining them together.

  • 实验

    Working with JSON, Arrays, and Structs in BigQuery

    In this lab you will work with semi-structured data (ingesting JSON, Array data types) inside of BigQuery. You will practice loading, querying, troubleshooting, and unnesting various semi-structured datasets.

  • 实验

    Build and Execute MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors

    In this lab you will explore existing datasets with Data Catalog and mine the table and column metadata for insights.

  • info
    Quest Info
    Prerequisites
    It is recommended but not required that students have a familiarity with data and spreadsheets.