BigQuery For Data Analysis
Fundamental 12 Steps 9時 40分 37クレジット
Want to learn the core SQL and visualization skills of a Data Analyst? Interested on how to write queries that scale to petabyte-size datasets? Take the BigQuery for Analyst Quest and learn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.
PrerequisitesThis quest assumes basic knowledge of SQL (Structured Query Language) but does provide an optional first lab to review the basic query syntax. No other labs or quests are required as a prerequisite.
このラボでは、基本的な SQL 句について学び、BigQuery と Cloud SQL で実際に構造化クエリを実行します。
In this lab, you learn to use BigQuery to find data, query the data-to-insights public dataset, and write and execute queries.
In this lab, you use BigQuery to troubleshoot common SQL errors, query the data-to-insights public dataset, use the Query Validator, and troubleshoot syntax and logical SQL errors.
In this lab, you learn how to connect Google Data Studio to Google BigQuery data tables, create charts, and explore the relationships between dimensions and measures.
This lab focuses on how to create new permanent reporting tables and logical reviews from an existing ecommerce dataset.
This lab focuses on how to ingest new datasets into tables inside of BigQuery.
This lab focuses on how to reverse-engineer the relationships between data tables and the pitfalls to avoid when joining them together.
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.
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.