SQL for Data Analysis with Google BigQuery

Explore Google BigQuery, a serverless data warehouse for analyzing large data in the cloud, with fast queries and free quotas, plus integration with Google Sheets, Analytics, and Ads.

Sign up for Google BigQuery, a cloud-based data warehouse, and explore the free plan with ten gigabytes of storage and one terabyte of queries per month, plus $300 credit.

Explore the Google BigQuery interface, focusing on the left menu and BigQuery Studio to write SQL queries for analysis, manage projects, data sets, and tables, and run and save results.

Explore five tables in a fictional e-commerce data set—customers, categories, products, items, and orders—learn relationships via primary and foreign keys, and how to upload to BigQuery.

Upload and organize data in Google BigQuery by creating a data set, uploading CSV tables, auto-detecting schemas, and preparing multiple tables for SQL queries.

Explore SQL for data analysis in Google BigQuery. Learn SQL, operators, functions, and left, inner, right, and full joins, and recognize SQL as a declarative language with results as tables.

Explore basic SQL queries in Google BigQuery by writing select statements, selecting specific columns, using string literals and arithmetic, and referring to tables with and without project identifiers using backticks.

Explore the distinct keyword in SQL with Google BigQuery to retrieve unique values from columns, apply it to different tables, and count distinct values for insights into data variety.

Explore arithmetic and comparison operators, including equals, not equals, between, like, and in, and use logical operators with is null, is true, and is false to filter data in BigQuery.

Explore arithmetic operations, comparison operators, the between operator, the like operator, the in operator, and handling null values in SQL with Google BigQuery.

Explore how aliases define temporary names for columns and tables using the as keyword to simplify code, boost readability, and clarify joins.

Master aliases in SQL with Google BigQuery by renaming columns and tables to improve readability, especially in complex joins.

Apply the where clause to restrict data with conditions such as last 30 days or approved sales, reducing rows while respecting the command order (where after select, before group by).

Explore the where clause in SQL with Google BigQuery to filter data using and, or, and parentheses, with examples filtering by quantity and state from e-commerce items and customers tables.

Explore conditional commands in SQL, including case, if, and coalesce, to classify sales as high, medium, or low and handle nulls in Google BigQuery.

Explore conditional expressions in SQL with Google BigQuery, using case statements, the if function, and coalesce to label categories, handle multiple conditions and null values in queries.

Learn how to group rows with group by to create summary results using aggregate functions, and how to sort those results with order by in ascending or descending order.

Group and sort data in Google BigQuery using group by and order by to summarize by status and day, leveraging distinct, column positions, and aggregate counts.

Learn how SQL aggregate functions count, max, min, sum, and avg summarize large data sets, optionally with distinct, and apply these concepts in practical SQL queries for data analysis.

Explore count, max, and min functions in SQL using Google BigQuery to summarize data, group orders by date and year, and analyze price ranges and user registrations.

Explore sum and avg aggregation in Google BigQuery with practical examples, including monthly revenue and average ticket, using group by, round, format date, and conditional logic.

Explore sql joins—inner, left, right, and full—and how the on clause relates rows across multiple tables to produce a unified result set for relational data analysis.

Introduce the logins, purchases, and users tables and guide uploading them to BigQuery, including creating the Table Relationships data set and using semicolon-delimited data with a header skip.

Learn how inner joins relate tables using from, join, and on clauses, illustrated by matching users to purchases via a shared user ID.

Explore inner join in SQL with Google BigQuery to merge related data from logins and purchases. Use aliases to improve readability, and learn that join and inner join are interchangeable.

Describe how left join uses a from clause and on clause to return all records from the left table and matching rows from the right, with nulls when no match.

Master left joins in sql with Google BigQuery, returning rows from the left table and matching data from the right, with nulls on no match, and using aliases for readability.

Explore the right join and right outer join, using from and on clauses to connect table A to B, returning all B records with matched A and nulls when unmatched.

Learn how to use the right join to return all purchases with matching login data, using aliases A and C, and filter for purchases without logins where logins.user_id is null.

Explore full join (full outer join) by joining orders and logins on user ID to return all records from both tables, with nulls where there are no matches.

Explore the full join operation in SQL with Google BigQuery to merge logins and purchases. Identify unmatched records with where clauses, handle nulls with coalesce, and perform double full joins.

Practice retrieving, filtering, and aggregating data with SQL on BigQuery public data sets, including look e-commerce, baseball, and bike share examples.