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Friday 20 September 2024

Mathematical functions in BigQuery in Google Cloud Platform (GCP)

Mathematical functions in BigQuery are used to perform arithmetic operations, rounding, and more complex calculations. They are essential for data analysis, where numeric values need to be manipulated, transformed, or rounded according to specific requirements.


ROUND()

   - Explanation: The ROUND() function rounds a numeric value to a specified precision. By default, it rounds to the nearest integer, but you can specify the number of decimal places to round to.

   - Usage:

     - ROUND(numeric_value, [precision]): Rounds numeric_value to the specified precision. If no precision is specified, it rounds to the nearest integer.

   - Example:     

     SELECT ROUND(123.456) AS rounded_value;

     This query rounds the number 123.456 to the nearest integer, resulting in 123.

     SELECT ROUND(123.456, 2) AS rounded_value;

     This query rounds the number 123.456 to two decimal places, resulting in 123.46.

   - Practical Use:

     The ROUND() function is useful in financial calculations, where rounding to a specific number of decimal places is essential for displaying results in currency format:

     SELECT ROUND(order_total, 2) AS rounded_total

     FROM orders;

     This query rounds the order totals to two decimal places, making it suitable for displaying prices.


ABS()

   - Explanation: The ABS() function returns the absolute value of a number, which means it removes the sign of a number and returns the non-negative version of it. This function is used to ensure that only positive values are considered.

   - Usage:

     - ABS(numeric_value): Returns the absolute value of the numeric_value.

   - Example:

     SELECT ABS(-123.456) AS absolute_value;

     This query returns 123.456, which is the absolute value of -123.456.

   - Practical Use:

     The ABS() function is useful in scenarios where only the magnitude of a number matters, such as calculating differences between two numbers:

     SELECT ABS(sales_target - actual_sales) AS variance

     FROM sales;

     This query calculates the absolute difference between the sales target and actual sales, ignoring whether the result is positive or negative.


FLOOR() / CEIL()

   - Explanation: The FLOOR() function rounds a number down to the nearest integer, while the CEIL() function rounds a number up to the nearest integer. These functions are used when you need to round a number consistently in one direction.

   - Usage:

     - FLOOR(numeric_value): Rounds the numeric_value down to the nearest integer.

     - CEIL(numeric_value): Rounds the numeric_value up to the nearest integer.

   - Example:

     SELECT FLOOR(123.789) AS floor_value;

     This query rounds the number 123.789 down to 123.

     SELECT CEIL(123.789) AS ceil_value;

     This query rounds the number 123.789 up to 124.

   - Practical Use:

     These functions are useful when dealing with ranges or when rounding is necessary in a specific direction. For example, when allocating resources and you need to ensure there are enough resources by rounding up:

     SELECT CEIL(total_hours / hours_per_worker) AS workers_required

     FROM project;

     This query calculates the number of workers required, rounding up to ensure the project is adequately staffed.

     Similarly, FLOOR() is helpful for calculating the maximum number of full items that fit into a given quantity:

     SELECT FLOOR(total_boxes / items_per_box) AS full_boxes

     FROM inventory;

     This query calculates how many full boxes can be formed from the total number of items.

Summary of Use Cases

Mathematical functions in BigQuery are vital for performing precise numeric operations:

- ROUND() is essential for rounding numbers to a specific precision, often used in financial calculations.

- ABS() is used to ensure non-negative values when analyzing magnitude or differences.

- FLOOR() and CEIL() provide control over rounding, with applications in allocation, distribution, and capacity calculations.


These functions are foundational for transforming and working with numeric data in BigQuery.

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