Introduction to SQL using Google BigQuery

Load Wisdom Pets CSV data into a BigQuery dataset by uploading products, customers, and sales files; auto-detect schemas and keep table names lowercase.

Learn how to query data in BigQuery using the select statement, including select star and selecting specific columns to optimize costs, plus using the resource viewer.

Explore the where clause and conditional logic in BigQuery, using equals, not equals, and string comparisons with proper single quotes. Filter, sort, and identify the most expensive product by category.

Explore fuzzy string matching in BigQuery with the like operator, wildcards, and case handling via upper and lower functions, including like any for in-like queries.

Resolve three sql challenges to extract top-rated products with id, name, retail price, category; assess dog supplement products' average price and rating; add wholesale price and discount, sorted by discount.

Learn sql queries in Google BigQuery to list products with id, name, price, category, order by price, calculate average price and rating for dog supplements using delimiters and rounding.

Understand left join behavior in BigQuery, preserving left records and returning nulls for nonmatches, and compare it with inner join using a Wisdom Pets retail and wholesale analysis.

Explore solving sql queries in Google BigQuery by calculating average transaction size by retail and wholesale with vip status, and computing total wholesale discounts using joins, cte, and per-unit discount.

Explore moving average calculations in BigQuery with window functions, range frames, and the unix date function to compute 7 and 28 day averages on Wisdom Pets daily sales by type.

Apply Google BigQuery techniques and SQL concepts learned in this course to analyze data, drive business and technology outcomes, and continue your data journey with confidence.