Can I convert SQL queries to DAX formulas automatically?

Yes, the translator converts common SQL patterns to equivalent DAX syntax, handling joins, aggregations, and filtering. However, complex SQL may need manual adjustment due to fundamental differences between SQL and DAX paradigms.

What types of DAX calculations can I generate?

The tool typically covers time intelligence functions, KPI calculations, running totals, year-over-year comparisons, ranking functions, and complex business calculations commonly needed in Power BI and Analysis Services.

Does the tool provide DAX performance optimization tips?

Yes, many tools include best practice recommendations for writing efficient DAX, avoiding common performance pitfalls, and optimizing calculations for large datasets and complex data models.

Can I validate DAX syntax before using in Power BI?

Most tools provide syntax validation and error checking to ensure your DAX formulas are correctly formatted before implementing them in Power BI reports or Analysis Services models.

Is this suitable for both beginners and advanced users?

Yes, beginners benefit from the template library and SQL translation, while advanced users appreciate the complex calculation generators and optimization suggestions for sophisticated business intelligence scenarios.

DAX Formula Genera - Business Intelligence and Power BI Tool

Time Intelligence

Year to Date (YTD)

Calculate year-to-date totals

YTD Sales = TOTALYTD([Total Sales], 'Date'[Date])

Quarter to Date (QTD)

Calculate quarter-to-date totals

QTD Sales = TOTALQTD([Total Sales], 'Date'[Date])

Month to Date (MTD)

Calculate month-to-date totals

MTD Sales = TOTALMTD([Total Sales], 'Date'[Date])

Previous Year

Compare with previous year

Previous Year = CALCULATE([Total Sales], PREVIOUSYEAR('Date'[Date]))

Same Period Last Year

Same period in previous year

SPLY = CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))

Previous Month

Previous month comparison

Previous Month = CALCULATE([Total Sales], PREVIOUSMONTH('Date'[Date]))

Last N Days

Rolling period calculation

Last 30 Days = CALCULATE([Total Sales], DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -30, DAY))

Parallel Period

Compare with parallel time period

Same Period Last Year = CALCULATE([Total Sales], PARALLELPERIOD('Date'[Date], -12, MONTH))

Aggregations & Statistics

Sum with Filter

Sum with specific conditions

Total Sales = SUMX(Sales, Sales[Quantity] * Sales[Price])

Average

Calculate average values

Avg Order = AVERAGEX(Sales, Sales[OrderValue])

Distinct Count

Count unique values

Unique Customers = DISTINCTCOUNT(Sales[CustomerID])

Count Rows

Count number of rows

Order Count = COUNTROWS(Sales)

Count Non-Blank

Count non-blank values

Non-Empty Orders = COUNTA(Sales[OrderID])

Median

Calculate median value

Median Sales = MEDIAN(Sales[SalesAmount])

Percentile

Calculate percentile values

90th Percentile = PERCENTILE.INC(Sales[SalesAmount], 0.9)

Standard Deviation

Calculate standard deviation

Sales StdDev = STDEV.S(Sales[SalesAmount])

Filters & Context

Calculate with Filter

Modify filter context

Sales 2023 = CALCULATE([Total Sales], 'Date'[Year] = 2023)

Remove All Filters

Ignore current filters

Total All Products = CALCULATE([Total Sales], ALL(Product))

All Selected

Remove filters but keep selections

% of Selected = DIVIDE([Total Sales], CALCULATE([Total Sales], ALLSELECTED()))

All Except

Remove all filters except specified columns

Sales by Category = CALCULATE([Total Sales], ALLEXCEPT(Sales, Product[Category]))

Keep Filters

Add filter without removing existing ones

High Value Sales = CALCULATE([Total Sales], KEEPFILTERS(Sales[Amount] > 1000))

Top N

Get top N records

Top 10 Products = TOPN(10, VALUES(Product[Name]), [Total Sales], DESC)

Filter Table

Filter rows based on condition

Filtered Sales = SUMX(FILTER(Sales, Sales[Amount] > 100), Sales[Amount])

Get Values

Get unique values from column

Product List = CONCATENATEX(VALUES(Product[Name]), Product[Name], ", ")

KPI & Performance Metrics

Growth Percentage

Calculate percentage growth

Growth % = DIVIDE([Current Period] - [Previous Period], [Previous Period], 0) * 100

Year over Year %

Year over year growth percentage

YoY Growth = DIVIDE([Total Sales] - [Previous Year Sales], [Previous Year Sales], 0)

Running Total

Cumulative sum

Running Total = CALCULATE([Total Sales], FILTER(ALL('Date'), 'Date'[Date] <= MAX('Date'[Date])))

Ranking

Rank values

Product Rank = RANKX(ALL(Product[Name]), [Total Sales], , DESC)

Market Share

Calculate market share percentage

Market Share = DIVIDE([Total Sales], CALCULATE([Total Sales], ALL(Product)), 0)

Moving Average

N-period moving average

3-Month MA = AVERAGEX(DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -3, MONTH), [Total Sales])

Variance

Calculate variance from target

Sales Variance = [Actual Sales] - [Target Sales]

Penetration Rate

Calculate penetration percentage

Customer Penetration = DIVIDE(DISTINCTCOUNT(Sales[CustomerID]), DISTINCTCOUNT(Customer[CustomerID]), 0)

Table Functions

Summarize

Create summary table

Summary Table = SUMMARIZE(Sales, Product[Category], "Total", SUM(Sales[Amount]))

Summarize Columns

Advanced table summarization

Advanced Summary = SUMMARIZECOLUMNS(Product[Category], "Sales", [Total Sales], "Orders", [Total Orders])

Add Columns

Add calculated columns to table

Extended Table = ADDCOLUMNS(Product, "Sales Rank", RANKX(ALL(Product), [Total Sales], , DESC))

Select Columns

Select specific columns from table

Product Info = SELECTCOLUMNS(Product, "Name", Product[ProductName], "Price", Product[UnitPrice])

Cross Join

Cartesian product of tables

All Combinations = CROSSJOIN(VALUES(Product[Category]), VALUES('Date'[Year]))

Left Outer Join

Left join between tables

Joined Table = NATURALLEFTOUTERJOIN(Customer, Sales)

Union

Combine tables with same structure

Combined Data = UNION(Sales2022, Sales2023)

Intersect

Common rows between tables

Common Customers = INTERSECT(VALUES(Sales[CustomerID]), VALUES(Returns[CustomerID]))

Text & String Functions

Concatenate

Join text strings

Full Name = Customer[FirstName] & " " & Customer[LastName]

Concatenate X

Concatenate values from table

Product List = CONCATENATEX(VALUES(Product[Name]), Product[Name], ", ")

Left

Extract leftmost characters

Product Code = LEFT(Product[ProductName], 3)

Right

Extract rightmost characters

File Extension = RIGHT(Document[FileName], 3)

Mid

Extract characters from middle

Month Code = MID(Product[ProductCode], 4, 2)

Length

Get string length

Name Length = LEN(Customer[CustomerName])

Upper Case

Convert to uppercase

Product Name Upper = UPPER(Product[ProductName])

Substitute

Replace text in string

Clean Name = SUBSTITUTE(Product[ProductName], "_", " ")

Logical & Conditional

IF Statement

Basic conditional logic

Sales Category = IF([Total Sales] > 1000, "High", "Low")

Switch Statement

Multiple condition evaluation

Quarter = SWITCH(MONTH('Date'[Date]), 1,2,3, "Q1", 4,5,6, "Q2", 7,8,9, "Q3", "Q4")

IF Error

Handle errors gracefully

Safe Division = IFERROR(DIVIDE([Sales], [Units]), 0)

Is Blank

Check for blank values

Has Description = IF(ISBLANK(Product[Description]), "No", "Yes")

AND Logic

Multiple conditions must be true

Premium Customer = IF(AND([Total Sales] > 5000, [Orders] > 10), "Premium", "Standard")

OR Logic

Any condition can be true

Active Customer = IF(OR([Recent Order] = TRUE, [Last Contact] < 30), "Active", "Inactive")

NOT Logic

Negate condition

Not Returned = IF(NOT(ISBLANK(Sales[OrderID])), [Total Sales], 0)

Has One Value

Check if column has single value in context

Single Product = IF(HASONEVALUE(Product[ProductName]), VALUES(Product[ProductName]), "Multiple")

DAX Formula Generator & SQL Translator

Generated DAX Formula

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