Ranking gym members by efficiency

Now that you've created the calories_per_minute function, it's time to create the dataset for your development team. This is really going to put their app over the top!

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Data Types and Functions in Snowflake

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Instructions

Using the calories_per_minute function, determine the workout efficiency for each individual gym member using the visits table.
Alias the resulting column as workout_efficiency.
Sort the result set by workout_efficiency.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

SELECT
    user_id,
    gym_id,
    checkin_time,

	-- Determine workout efficiency for each user using the check in/out
    -- time, as well as the number of calories burned
    ___(
        ___, 
        ___, 
        ___
    ) AS ___  -- Alias the results as workout_efficiency

FROM CORE_GYM.visits

-- Order the results by workout_efficiency
___ ___ ___ DESC;

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Cet exercice fait partie du cours

Data Types and Functions in Snowflake

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Before you can change the world using Snowflake, you need to know the type of data you'll be working with. You'll start with text data, learning to use it in queries and define columns, plus discover how to retrieve column details from any table. Then you'll master numeric data types and date/time data, understanding precision, scale, and how to convert strings to timestamps.

Exercise 1: Text data types Exercise 2: Is that text data?Exercise 3: Working out in the big city Exercise 4: Defining text data Exercise 5: Numeric data types Exercise 6: Go with the FLOAT Exercise 7: Creating a retail table Exercise 8: Datetime data types Exercise 9: Building datetime values Exercise 10: Which timezone?Exercise 11: When did you check in?

Now that you know your Snowflake data inside-and-out, it's time to manipulate and transform it. You'll begin with text data manipulation using TRIM, SPLIT, and CONCAT, then master numeric operations like arithmetic and summary measures with SUM and AVG. You'll explore timestamp transformations by extracting components like year or hours, calculating intervals between dates, and create your own custom Snowflake functions (UDFs) to implement personalized logic.

Exercise 1: Manipulating text data Exercise 2: Managing display length Exercise 3: Transforming text data Exercise 4: Motivating member marketing Exercise 5: Numeric calculations Exercise 6: Comparison operators Exercise 7: Adjusting membership prices Exercise 8: What burns more calories?Exercise 9: Manipulating datetime data Exercise 10: Breaking down a timestamp Exercise 11: How long were you at the gym?Exercise 12: Making appointments Exercise 13: User-defined functions in Snowflake Exercise 14: Creating your own Snowflake function Exercise 15: Ranking gym members by efficiency

Exercice en cours

In this final chapter, you'll crank the heat up with Snowflake's more advanced functionality. You'll start with VARIANT data types (semi-structured data), learning to parse top-level and nested data using dot-notation and bracket-notation, then master common table expressions (CTEs) by building and using multiple CTEs. Finally, you'll wrap up by creating pivoted tables using the PIVOT keyword to transform data into an easy-to-digest format, ready for reports and analysis.

Exercise 1: Flattening Semi-Structured Data Exercise 2: Retrieving nested data Exercise 3: Working with personal info Exercise 4: Building a gym member description Exercise 5: Multiple common table expressions Exercise 6: Identifying high-performers Exercise 7: Comparing senior workouts Exercise 8: Pivoting Data Exercise 9: Evaluating workout length Exercise 10: Is CrossFit a popular workout?Exercise 11: Which workouts burn the most calories?Exercise 12: Congratulations!