BigQuery for Data Warehousing

Fundamental 6단계 6시간 크레딧 25개

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.

기본 요건:

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

Quest Outline


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.

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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.

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

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.

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

BigQuery에서 JSON, 배열 및 구조체 작업

이 실습에서는 BigQuery 내부의 반구조화된 데이터(수집 JSON, 배열 데이터 유형)를 다루고, 다양한 반구조화된 데이터세트의 로드, 쿼리, 문제해결 및 중첩 해제를 연습합니다.

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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