What are some common use cases for window functions in data analysis and reporting?

RANK(), DENSE_RANK(), and ROW_NUMBER() are SQL window functions used for ranking and ordering data within partitions: - RANK() assigns ranks to rows, with gaps for ties. Tied rows get the same rank, and the next rank skips the number of tied rows. - DENSE_RANK() assigns ranks without gaps for ties. Tied rows get the same rank, and the next rank is the next integer. - ROW_NUMBER() assigns a unique sequential integer to each row, starting from 1 for the first row in each partition.

if you want to rank or order records by group, you can use PARTITION BY clause. Example: select top 3 high score students each faculty CREATE TABLE studentScore( faculty NVARCHAR(128), studentName NVARCHAR(128), score int ) INSERT INTO studentScore ( faculty, studentName, score ) VALUES ('Math','Alex', 7), ('Math','Vu', 4), ('Math','Hung', 6), ('Math','Duy', 3), ('IT','Thomas', 6), ('IT','John', 3), ('IT','David', 5), ('IT','Dao', 8) SELECT faculty, no, studentName, score FROM ( SELECT faculty, ROW_NUMBER() OVER(PARTITION BY faculty ORDER BY score DESC) no, studentName, score FROM studentScore ) v WHERE no <= 3 ORDER BY faculty, no

Use Case: Assign ranks to rows within a partition of a result set, such as ranking salespeople based on their sales figures. Example: RANK(), DENSE_RANK(), and ROW_NUMBER() functions can be used to assign ranks, handle ties, and provide sequential row numbers. Personal Experience: I often use ROW_NUMBER() to eliminate duplicate entries while keeping the most recent record based on a timestamp.

Window functions in data analysis and reporting offer powerful tools for ranking, sorting, and partitioning data. Use RANK, DENSE_RANK, or ROW_NUMBER to assign rankings within categories, like product sales by category. NTILE is valuable for dividing rows into equal-sized groups, such as customer income quartiles. These functions excel at identifying top performers, outliers, and trends. By leveraging window functions, you can transform raw data into meaningful insights, enabling more sophisticated analysis and decision-making. Master these tools to elevate your data storytelling and provide actionable business intelligence.

A Fun??es de Janela (Window Function) que eu utilizo todo santo dia é a ROW_NUMBER() Casos de uso: - Ordenar registros (ex. Eventos do GA4 dentro de uma Sess?o) - Marcar registros duplicados; ou - Selecionar o último registro (QUALIFY ROW_NUMBER().... = 1)

When you need to calculate a cumulative total for a specific purpose, you can use the SUM() OVER (PARTITION BY) function to achieve this. For example, to calculate the cumulative number of employees in each department within a company, you could use the following query: SELECT department_id, COUNT(*) AS emp_of_dept, SUM(COUNT(*)) OVER (PARTITION BY department_id ORDER BY department_id) FROM employees GROUP BY department_id ORDER BY department_id;

Use clause SUM() OVER to calculate sum over each row Example: DECLARE @transaction TABLE ( [Date] DATETIME2, [Amount] DECIMAL(18,5) ) DECLARE @BeginingBalance DECIMAL(18,5) = 100.0 INSERT INTO @transaction([Date], [Amount]) VALUES ('2024-01-01',100), ('2024-01-02',-50), ('2024-01-03',20) SELECT [Date], [Amount], @BeginingBalance + SUM([Amount]) OVER(ORDER BY [Date]) Balance FROM @transaction ORDER BY [Date] The result should be: Date | Amount | Balance --------------------------------------- 2024-01-01 | 100 | 200 2024-01-02 | -50 | 150 2024-01-03 | 20 | 170

Another common use of window functions is to calculate cumulative or running totals over a partition of data. You can use functions like SUM, AVG, or COUNT with a window frame to define the range of rows included in the calculation, such as the current row and all preceding rows, which is useful for tracking cumulative sales or totals. Additionally, FIRST_VALUE and LAST_VALUE functions retrieve the first or last value within a partition, such as determining the opening or closing balance of an account. These window functions provide powerful ways to analyze growth, changes, and distribution of data over time or across categories, offering deeper insights into trends and patterns.

Use Case: Calculate cumulative totals or running totals, such as cumulative sales up to a certain date. Example: The SUM() function with OVER() clause helps in computing running totals. Personal Experience: In financial reports, I frequently use running totals to show cumulative revenue over time.

O Total acumulado por si só eu acabo n?o utilizando muito, porém calcular o share % de um registro em execu??o é fantástico e muito útil. Como? - SUM(coluna) / SUM(coluna) OVER(parti??o_se_existir) AS share_percentual

In SQL server use syntax AVG() OVER (ORDER BY [Column] ROWS BETWEEN 7 PRECEDING AND 7 FOLLOWING) to calculate avg previous 7 days and the next 7 days. That is helpful when you calculate moving average over each row, special in market trend statistic - UNBOUNDED PRECEDING – All rows before the current row. - n PRECEDING – n rows before the current row. - CURRENT ROW – Just the current row. - n FOLLOWING – n rows after the current row. - UNBOUNDED FOLLOWING – All rows after the current row.

Use Case: Compute moving averages or percentages to smooth out data and observe trends over time. Example: Functions like AVG() over a specified window frame can calculate moving averages. Personal Experience: I utilize moving averages to analyze stock prices and identify trends in financial data.

Use Case: Access previous or next row data within the same result set, useful for comparing current values with past or future values. Example: LAG() and LEAD() functions provide access to prior or subsequent row data. Personal Experience: These functions are particularly useful in time series analysis to calculate day-over-day changes or year-over-year growth.

By using the Partition by (column_1, column_2) option in analytic functions, you can partition the values for the function you're calculating (e.g., MIN, MAX, etc.). For example, the query to get the maximum salary paid in each department would be: SELECT department_id, MAX(salary) OVER (PARTITION BY department_id) as max_salary FROM employees;

Use Case: Calculate statistics within partitions of a dataset, such as calculating average sales by region. Example: Partitioning data using the PARTITION BY clause in functions like AVG(), COUNT(), SUM(), etc. Personal Experience: I often partition sales data by product category to analyze performance within each category.

Performance Considerations: Window functions can be resource-intensive; optimizing queries and indexing appropriately can mitigate performance issues. Complex Calculations: Combining multiple window functions can perform complex calculations in a single query, reducing the need for multiple steps. Understanding Window Frames: Defining window frames precisely (e.g., ROWS BETWEEN, RANGE BETWEEN) is crucial for accurate calculations. Practical Applications: In business intelligence dashboards, window functions are invaluable for creating insightful and dynamic visualizations.