Here’s an expanded and robust article outline for the series “DAX for Beginners: Understanding Programming Concepts in DAX,” incorporating programming and SQL concepts. This first article will serve as an introduction to DAX and fundamental programming principles applied within this context.

DAX for Beginners: Understanding Programming Concepts in DAX – Part 1

Introduction
DAX (Data Analysis Expressions) is a powerful programming language used in Power BI, Excel, and SQL Server Analysis Services. It facilitates advanced calculations and analytics on your data. This is the first part of a 10-article series that will cover key programming concepts applied to DAX, explained step by step with practical examples. We will also integrate programming concepts and SQL principles to enhance understanding.

In this article, we will introduce fundamental programming concepts such as variable assignments, conditional logic, and iteration using DAX. Each example will use a different sample table with 100 rows, covering various themes to keep the content dynamic and engaging. By the end of this series, you’ll not only be proficient in DAX but will also understand how programming and SQL concepts relate to it.

Section 1: Understanding DAX and Its Purpose

DAX is designed to work with relational data and perform calculations on data models. It shares similarities with Excel formulas but is more robust and capable of handling complex data models. Here are some fundamental programming concepts to understand as we dive into DAX:

Key Programming Concepts

• Variables: Just like in traditional programming languages (such as Python or Java), variables in DAX allow you to store values and reuse them throughout your calculations.
• Conditional Logic: This involves executing different actions based on certain conditions. In programming, this is often handled with if statements or switch cases, similar to what you will find in SQL with the CASE statement.
• Iteration: Iteration allows you to perform operations over a set of data, akin to loops in programming languages. In DAX, functions like GENERATESERIES and ADDCOLUMNS can simulate this behavior.

Section 2: Example 1 – Variable Assignment (Appliances)

For this example, let’s create a table of 100 household appliances and their prices. We will assign a 15% discount to each appliance using variables. This example illustrates variable assignment, a concept that exists in both programming languages and SQL through the use of temporary variables in SQL procedures.

SampleTable_Appliances = 
ADDCOLUMNS(
    GENERATESERIES(1, 100, 1),
    "Appliance", SWITCH(
        TRUE(),
        [Value] <= 20, "Washing Machine",
        [Value] <= 40, "Refrigerator",
        [Value] <= 60, "Microwave",
        [Value] <= 80, "Air Conditioner",
        "Vacuum Cleaner"
    ),
    "Price", SWITCH(
        TRUE(),
        [Value] <= 20, 300 + RAND() * 200,
        [Value] <= 40, 500 + RAND() * 300,
        [Value] <= 60, 100 + RAND() * 50,
        [Value] <= 80, 700 + RAND() * 400,
        150 + RAND() * 100
    )
)

Example1 = 
ADDCOLUMNS(
    SampleTable_Appliances,
    "DiscountedPrice", 
    VAR Discount = 0.15
    VAR OriginalPrice = [Price]
    VAR DiscountedPrice = OriginalPrice * (1 - Discount)
    RETURN DiscountedPrice
)

Explanation

• A sample table of 100 household appliances is generated.
• Each appliance has a random price.
• A 15% discount is applied using variable assignment.

Section 3: Example 2 – Conditional Logic (IF) (Books)

In this example, we create a table of 100 books with different prices. Using conditional logic, we categorize each book as “Cheap” or “Expensive.” This mirrors the use of IF conditions in programming and the CASE statement in SQL.

SampleTable_Books = 
ADDCOLUMNS(
    GENERATESERIES(1, 100, 1),
    "BookTitle", SWITCH(
        TRUE(),
        [Value] <= 25, "Fiction",
        [Value] <= 50, "Non-Fiction",
        [Value] <= 75, "Science",
        "Fantasy"
    ),
    "Price", SWITCH(
        TRUE(),
        [Value] <= 25, 10 + RAND() * 5,
        [Value] <= 50, 20 + RAND() * 10,
        [Value] <= 75, 30 + RAND() * 15,
        40 + RAND() * 20
    )
)

Example2 = 
ADDCOLUMNS(
    SampleTable_Books,
    "Category", 
    IF([Price] < 20, "Cheap", "Expensive")
)

Explanation

• A table of books is generated with random prices.
• Books are categorized as “Cheap” if their price is below $20; otherwise, they are categorized as “Expensive.”

Section 4: Example 3 – Iteration Simulation with GENERATESERIES (Smartphones)

This example creates a list of 100 smartphones and calculates the square of each phone’s price to demonstrate how iteration can be simulated with GENERATESERIES. This concept relates closely to FOR loops in programming and the way SQL can handle sets of data.

SampleTable_Phones = 
ADDCOLUMNS(
    GENERATESERIES(1, 100, 1),
    "Smartphone", SWITCH(
        TRUE(),
        [Value] <= 25, "iPhone",
        [Value] <= 50, "Samsung",
        [Value] <= 75, "Huawei",
        "OnePlus"
    ),
    "Price", SWITCH(
        TRUE(),
        [Value] <= 25, 700 + RAND() * 300,
        [Value] <= 50, 600 + RAND() * 200,
        [Value] <= 75, 500 + RAND() * 150,
        400 + RAND() * 100
    )
)

Example3 = 
ADDCOLUMNS(
    SampleTable_Phones,
    "PriceSquared", [Price] * [Price]
)

Explanation

• A table of smartphones is generated.
• The square of each smartphone’s price is calculated, demonstrating how iteration can be applied in DAX.

Section 5: Example 4 – Using SWITCH for Multiple Conditions (Sports Equipment)

Here, we have a table of sports equipment. We adjust the price of each item based on the equipment category using the SWITCH function, showcasing how we can handle multiple conditions in DAX, similar to SWITCH or CASE statements in programming and SQL.

SampleTable_Sports = 
ADDCOLUMNS(
    GENERATESERIES(1, 100, 1),
    "Equipment", SWITCH(
        TRUE(),
        [Value] <= 25, "Tennis Racket",
        [Value] <= 50, "Football",
        [Value] <= 75, "Basketball",
        "Running Shoes"
    ),
    "Price", SWITCH(
        TRUE(),
        [Value] <= 25, 50 + RAND() * 20,
        [Value] <= 50, 30 + RAND() * 10,
        [Value] <= 75, 25 + RAND() * 15,
        100 + RAND() * 50
    )
)

Example4 = 
ADDCOLUMNS(
    SampleTable_Sports,
    "AdjustedPrice", 
    SWITCH(
        TRUE(),
        [Equipment] = "Tennis Racket", [Price] - 5,
        [Equipment] = "Football", [Price] - 3,
        [Equipment] = "Basketball", [Price] - 2,
        [Equipment] = "Running Shoes", [Price] - 10
    )
)

Explanation

• A table of sports equipment with random prices is generated.
• The price is adjusted differently for each type of equipment, demonstrating how to manage multiple conditions effectively.

Section 6: Example 5 – Sales Tax Calculation (Laptops)

Let’s calculate the sales tax for a table of laptops, assuming a 12% tax rate applied to each laptop’s price. This example shows how calculations can be performed dynamically, similar to computed columns in SQL.

SampleTable_Laptops = 
ADDCOLUMNS(
    GENERATESERIES(1, 100, 1),
    "LaptopBrand", SWITCH(
        TRUE(),
        [Value] <= 25, "Dell",
        [Value] <= 50, "HP",
        [Value] <= 75, "Lenovo",
        "Apple"
    ),
    "Price", SWITCH(
        TRUE(),
        [Value] <= 25, 800 + RAND() * 200,
        [Value] <= 50, 900 + RAND() * 250,
        [Value] <= 75, 1000 + RAND() * 300,
        1500 + RAND() * 500
    )
)

Example5 = 
ADDCOLUMNS(
    SampleTable_Laptops,
    "SalesTax", 
    VAR TaxRate = 0.12
    RETURN [Price] * TaxRate
)

Explanation

• A table of laptops is generated with random prices.
• A 12% sales tax is calculated for each laptop, illustrating how DAX can perform dynamic calculations.

Conclusion

In this article, we have explored fundamental programming concepts such as variable assignment, conditional logic, iteration, and handling multiple conditions using