Cumulative totals, running sums, and ranking are some of the most common analytics requirements. Whether you are analyzing sales over time, customer growth, or employee salaries, you will often need to calculate metrics that keep growing with every row or compare values across categories.

As a data analyst, you might wonder: Should I calculate these using SQL or Power BI?
The answer lies in understanding SQL Window Functions and Power BI DAX.

SQL Window Functions: Adding Aggregates Without Losing Detail

Normally, SQL aggregates (SUM, AVG, COUNT) require a GROUP BY, which collapses your rows. Window functions solve this problem.

For example, let’s say we have a sales table:

To calculate the cumulative total using SQL:

SELECT 
    Date,
    Sales,
    SUM(Sales) OVER(ORDER BY Date) AS Cumulative_Total
FROM Sales;

Output:

Every row retains its detail, but we also get the running sum.

Other common window functions include:

• RANK() OVER(...) → Ranking rows

• AVG() OVER(...) → Moving averages

• PARTITION BY → Group-wise calculations without GROUP BY

Power BI DAX: Dynamic Measures with Filter Context

In Power BI, we don’t write SQL. Instead, we use DAX measures. DAX is designed to work dynamically with filters and slicers on a dashboard.

For the same cumulative total, we can write:

For the same cumulative total, we can write:

Cumulative Sales = 
CALCULATE(
    SUM(Sales[Amount]),
    FILTER(
        ALL(Sales[Date]),
        Sales[Date] <= MAX(Sales[Date])
    )
)

Now, if the user filters the report by year, region, or product, the measure automatically adjusts. That’s the real power of DAX — dynamic recalculation based on context.

SQL vs DAX: When to Use What

Real Project Insight: Amazon Sales Analytics

In my Amazon Sales Analytics & Forecasting Project, I combined both worlds:

• SQL Window Functions were used to pre-calculate regional totals and growth trends during ETL.

• Power BI DAX was then used to create interactive KPIs and dynamic cumulative sales visuals — so business users could filter by year, category, or payment method in real time.

This hybrid approach ensured performance at scale while keeping the dashboards flexible and interactive.

Conclusion

• SQL Window Functions are great when you want heavy calculations done once at the database level.

• Power BI DAX shines when you need dynamic, interactive dashboards that change with filters and slicers.

• In real-world analytics projects, the best approach often combines both.

If you are a data analyst, mastering both SQL Window Functions and Power BI DAX will make you unstoppable 🚀.

Here’s a glimpse of the Amazon Sales Analytics & Forecasting Dashboard I built:

🔗 GitHub Repository: Amazon Sales Analytics Project

🔗 GitHub Link: https://github.com/Tanu272004

This project combines SQL Window Functions for pre-aggregations and Power BI DAX for interactive KPIs, giving business users both performance and flexibility.

💡 In my next post, I’ll dive deeper into building a Sales Dashboard from scratch using Python, SQL, and Power BI, showing how ETL meets visualization.