BigQuery for Data Warehousing

Fundamental 6 Steps 6 hours 25 Credits

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.


It is recommended but not required that students have a familiarity with data and spreadsheets.

Quest Outline


BigQuery: Qwik Start - Komut Satırı

Bu uygulamalı laboratuvarda, herkese açık tabloları nasıl sorgulayacağınız ve örnek verileri Komut Satırı Arayüzünü kullanarak nasıl BigQuery'ye yükleyeceğiniz gösterilmektedir. Google BigQuery ile Anlamlı Analizler Elde Etme ve BigQuery: Qwik Start - Qwiklabs Önizlemesi başlıklı kısa videoları izleyin.

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Creating a Data Warehouse Through Joins and Unions

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

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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.

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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.

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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.

Deutsch English español (Latinoamérica) français 日本語 한국어 português (Brasil)

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.

English español (Latinoamérica) français 日本語 português (Brasil)

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