1. Write a query that calculates the **total revenue per store** using the data in the `retail_data_updated` table:
Multiply the `quantity` (`INTEGER`) and `unit_price` (`FLOAT`) to calculate revenue per transaction.
   - Group the results by `store_id`;
   - Sum the revenue for each store;
   - Your query should return these columns:
      - `store_id` (identifier for each store);
      - `total_revenue` (sum of quantity × unit_price per store).
2. Write a query that counts how many transactions happened each year and month using the `date` field in the `retail_data_updated` table:
   - Extract the year and month from the `date` field;
   - Group by year and month;
   - Count the number of transactions in each time period;
   - Your query should return these columns:
      - `year_month` (formatted as `YYYY-MM`);
      - `transaction_count` (number of transactions per month).
3. Write a query that compares the number of **online** vs **in-store** transactions in the `retail_data_updated` table:
   - Group the data by the `is_online` column (`BOOLEAN`);
   - Count how many transactions occurred for each value (`TRUE` = online, `FALSE` = in-store);
   - Your query should return these columns:
      - `is_online` (`TRUE` or `FALSE`);
      - `transaction_count` (number of transactions for each group).






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Dive into the fundamentals of the BigQuery platform and transform the way you handle cloud data. Master the skills needed to navigate the console, manage projects, and optimize query costs. Get hands-on experience with BigQuery-specific features like array operations, geographic data, and date functions. Explore advanced optimization techniques and learn the secrets of building machine learning models directly within the warehouse using BigQuery ML.

Gain a solid understanding of the BigQuery environment. distinct from traditional databases. Learn to navigate the console, manage projects and datasets, and analyze costs effectively. Build confidence in setting up your workspace for efficient data analysis.

Explore the essentials of the Query Engine to retrieve data efficiently. Master data selection techniques and learn how to analyze specific product metrics. Discover performance features and query testing methods to ensure your analysis is both fast and accurate.

Take your knowledge to the next level by mastering BigQuery’s unique data types. Learn to handle complex array operations and manipulate date and time functions. Explore geographic features to solve location-based challenges like calculating city distances.

Optimize your workflow by managing large tables and improving query performance. Learn the secrets of effective error handling to troubleshoot issues quickly. Master the skills needed to maintain a high-performance database environment.

Apply your knowledge to real-world scenarios. Discover how to build comprehensive reports and integrate BigQuery with external tools. Transform raw data into actionable business insights through practical application and reporting strategies.

Dive into the fundamentals of BigQuery ML. Discover how to create and execute machine learning models using standard SQL without moving data. Gain hands-on experience with model walkthroughs to predict outcomes directly within the platform.

Challenge: Data Types