Introduction to SQL using Google BigQuery

Explore a real-world case study by loading Wisdom Pets' product, customer, and sales data into BigQuery to analyze product performance and transactions, building practical SQL and BigQuery skills.

Set up your BigQuery environment by creating a new Google Cloud Platform account and enabling the BigQuery sandbox, following the official setup guide to run queries within the free tier.

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.

Master the order by clause in Google BigQuery to sort data by multiple expressions, using ascending or descending order on columns like category name, retail price, and discount percentage.

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.

Learn how to filter data with where clauses using inequalities, between, and in operators, including inclusive endpoints and not between and not in, illustrated with Wisdom Pets products.

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.

Learn to use the count function to count total rows, count non-null values, and count distinct in BigQuery, and combine it with where clauses to filter results.

Explore BigQuery calculation functions to compute min, max, avg, and standard deviation on product data, round results to two decimals, and reveal price distribution across Wisdom Pets products.

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.

Discover how the group by clause splits a data set into groups for aggregation in Google BigQuery. Use having to filter grouped results and apply count, average, and max.

Learn case when expressions in BigQuery to implement if-then-else logic, using else and end, the like operator for fuzzy matching, and group by with between bounds to create price bands.

Explore how to implement conditional counts in BigQuery using the countif function and case when expressions, including group by, sum case when, and pivot techniques.

Explore how to pivot long to wide data in BigQuery using the pivot operator, with CTEs, case when alternatives, and metrics like average rating and product counts.

apply data analytics techniques to a sales data set in bigquery to identify the top three selling products by total amount and analyze sales by transaction type.

Analyze sales in Google BigQuery: identify top three selling products by sum(total_amount) and group by product_id; compute total sales by transaction type and percentage with a cte and window functions.

Explore how to combine tables in BigQuery using cross, inner, left outer, full outer, and right joins, with a focus on inner and left joins for practical data analysis.

Learn how inner join in BigQuery matches records on a join condition, explores one-to-many relationships, and uses aggregation with sum, group by, and order by total sales.

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.

Analyze average transaction size for wholesale and retail by VIP status, and total discounts for wholesale transactions, using table joins to combine data sets in BigQuery.

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.

Discover how window functions in SQL with BigQuery partition data into frames, order within them, and compute moving averages, ranks, and cumulative sums for sales analysis.

Learn to use the rank window function with over and partition by to rank customer transactions, order by total sales, and apply qualify to isolate top results.

Explore cumulative metrics with BigQuery window functions to compute monthly sales totals and cumulative sums by customer, using date trunk and proper ordering in calendar year 2022.

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.

Analyze customer behavior using Wisdom Pets data set for social media campaign, solving most recent purchase and top products with a 30-day moving total spend via window functions in SQL.

Apply window functions in Google BigQuery to identify each customer's top purchased item by total units and compute their last purchase date with a 30-day moving total spend.

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.