This time we’re going bigger than ever. Fabric, Power BI, SQL, AI and more. We're covering it all. You won't want to miss it.
Learn moreDid you hear? There's a new SQL AI Developer certification (DP-800). Start preparing now and be one of the first to get certified. Register now
How to convert the below sql statement into DAX to calculate Average Lag? Thanks
SELECT avg(isnull(datediff(day, b.ClaimSubmitDate, t.PostDate),0)) as AvgLag
FROM [dbo].[Bill] b join [dbo].[Transactions] t
on b.BillID= t.BillID
WHERE t.[TransactionTypeID] = 2
Hi @JulietZhu
There are two approches.
1.
create a calculated column
related = LOOKUPVALUE(b[ClaimSubmitDate],b[BillID],t[BillI])
Then create a measure
Measure =
VAR Table1 =
NATURALINNERJOIN ( b, t )
VAR datediff =
IF (
ISBLANK ( DATEDIFF ( MAX ( [related] ), MAX ( [PostDate] ), DAY ) ),
0,
DATEDIFF ( MAX ( [related] ), MAX ( [PostDate] ), DAY )
)
RETURN
CALCULATE (
AVERAGE ( 'Table'[datediff] ),
FILTER ( ALL ( 'Table' ), [TransactionTypeID] = 2 )
)
2. create a new table
Table = NATURALINNERJOIN(b,t)
Then in the new table
create calculated columns
datediff =
IF (
ISBLANK ( DATEDIFF ( [ClaimSubmitDate], [PostDate], DAY ) ),
0,
DATEDIFF ( [ClaimSubmitDate], [PostDate], DAY )
)
average = CALCULATE(AVERAGE('Table'[datediff]),FILTER(ALL('Table'),[TransactionTypeID]=2))
Best Regards
maggie
Honestly, any question that starts with "I want to convert this SQL into DAX" generally never go well when all you present is the SQL. Sample data and expected result will get you there quicker. Please see this post regarding How to Get Your Question Answered Quickly: https://community.powerbi.com/t5/Community-Blog/How-to-Get-Your-Question-Answered-Quickly/ba-p/38490
Check out the April 2026 Power BI update to learn about new features.
Sign up to receive a private message when registration opens and key events begin.
If you have recently started exploring Fabric, we'd love to hear how it's going. Your feedback can help with product improvements.
| User | Count |
|---|---|
| 34 | |
| 31 | |
| 25 | |
| 20 | |
| 16 |
| User | Count |
|---|---|
| 61 | |
| 49 | |
| 28 | |
| 23 | |
| 23 |