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.
Prerequisites:It is recommended but not required that students have a familiarity with data and spreadsheets.
This lab focuses on how to create new reporting tables using SQL JOINS and UNIONs.
This lab focuses on how to query partitioned datasets and how to create your own dataset partitions to improve query performance, which reduces cost.
This lab focuses on how to reverse-engineer the relationships between data tables and the pitfalls to avoid when joining them together.
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.
In this lab you will explore existing datasets with Data Catalog and mine the table and column metadata for insights.