BigQuery, Google's fullymanaged and serverless data warehouse, allows users to run complex queries on large datasets efficiently. One of the analytical functions available in BigQuery is the calculation of the median. In this article, we will explore how to compute the median in BigQuery, provide examples, and delve into practical applications of this function.
What is Median?
The median is a statistical measure that represents the middle value of a dataset when it is ordered. In a sorted list, if the number of observations (n) is odd, the median is the middle value. If n is even, the median is calculated as the average of the two middle values. This metric is particularly useful in datasets that may contain outliers, as it provides a more robust central tendency measure than the mean.
The Original Code for the Problem
The common method to calculate median in BigQuery is using the APPROX_QUANTILES
function. Here’s an example of how you might initially set up a query to calculate the median value of a numeric column named value
in a table called your_table
:
SELECT
APPROX_QUANTILES(value, 100)[OFFSET(50)] AS median_value
FROM
your_table;
Explanation of the Code

APPROX_QUANTILES: This function calculates approximate quantiles for a given dataset. The second argument specifies the number of quantiles to compute. In the example, we are computing 100 quantiles, which allows us to extract the 50th quantile (the median).

OFFSET(50): This retrieves the 50th value from the list of calculated quantiles, which corresponds to the median.
Practical Example
Let’s assume you have a dataset of employee salaries and you want to find the median salary. Your table structure may look something like this:
Employee_ID  Salary 

1  50000 
2  70000 
3  60000 
4  80000 
5  75000 
You could use the following query to find the median salary:
SELECT
APPROX_QUANTILES(Salary, 100)[OFFSET(50)] AS median_salary
FROM
employees;
This query would return 70000
, which is the median salary for the employees listed.
Benefits of Using Median
 Robustness: The median is less affected by extreme values (outliers) compared to the mean, making it a preferred measure of central tendency for skewed distributions.
 Interpretability: The median provides a clear representation of the central point in your data, which can be easier to communicate in reports and presentations.
Conclusion
BigQuery offers powerful tools to analyze and compute statistics such as the median. Using the APPROX_QUANTILES
function makes it easy to derive meaningful insights from your data without significant performance overhead. Understanding how to leverage median calculations can help in making informed decisions based on the data you have.
Useful Resources
By incorporating median calculations into your data analysis processes, you can enhance your data interpretation capabilities and make more informed decisions based on the central tendency of your datasets.