Cthulhu

Greg_Deckler, here's an approach using the newer WINDOW functions.

Local Group Index  = 
VAR _GlobalGroup_ = /* All rows with current animal up to current row */  
    WINDOW (
        1, ABS, /* From frirst row */
        0, REL, /* Up to current row */
        ORDERBY ( Cthulhu[Index] ),
        PARTITIONBY ( Cthulhu[Animal] )
    )  
VAR _AddPrevIndex_ = /* Lookup previous index for each row */
    ADDCOLUMNS (
        _GlobalGroup_,
        "@PrevIndex",
	        VAR _PrevRow_ =
                OFFSET ( -1, _GlobalGroup_, ORDERBY ( Cthulhu[Index] ) )
	        VAR _PrevIndex =
                MAXX ( _PrevRow_, Cthulhu[Index] )
	        RETURN
                _PrevIndex
    )
VAR _Skips_ = /* All rows where prev index isn't current index - 1 */
    FILTER ( _AddPrevIndex_, [@PrevIndex] < Cthulhu[Index] - 1  )
    /* Will be empty if there are no such skips */

VAR _LocalGroupStart = /* Latest skip -- blank if _Skips_ is empty */
    MAXX ( _Skips_, Cthulhu[Index] )

VAR _LocalGroup_ = /* All rows since latest skip */
    FILTER ( _AddPrevIndex_, Cthulhu[Index] >= _LocalGroupStart )

VAR _Rank = /* Current row is the last in the local group */
    COUNTROWS ( _LocalGroup_ )

RETURN  
    _Rank

I may be a 3rd guy... Looks like I may be using this work.

I would have preferred a Power Query based solution, as the mix of PQ and using add column or measure is a bit too unpredictable for a beginner like me. I have lots of PQ in a complex table, along with using this, as an add column.

It worked! But devs are buffled as to how it is all works. 

Amazing work - I've just signed up to say thank you!... 

I think I found a cleaner approach:

Local Group Index  = 
VAR _CurrIndex = MAX ( Cthulhu[Index] )
VAR _CurrGroup = MAX ( Cthulhu[Animal] )
VAR _LocalGroupStart =
    CALCULATE (
        MAX ( Cthulhu[Index] ),
        ALLSELECTED ( Cthulhu ),
        Cthulhu[Index] < _CurrIndex,
        Cthulhu[Animal] <> _CurrGroup
    )
VAR _LocalIndex = _CurrIndex - _LocalGroupStart
RETURN
    _LocalIndex

Hahaha, I wish the people using this visual truly know the blood sweat and tears behind it! Thanks for your hard work Greg!

I'm so confused.  I'm a not-very-tech-savvy senior so calculations etc throw me for a loop.  I have a data dump from our timekeeping database. Columns include: Employee, Division, Section, PayGrade, Date, Hours, to name a few. There is 11,000 rows with 200 employees and the dates they worked.  I need to add a column to the dataset or a measure to a table visual to show how many consecutive days they worked (including weekend days) so that I can identify those who worked 7 consecutive days or more (and needs to reset after a 1-day gap).  I watched the video and read whatever I could find. And I'm stumped. I tried to copy the Cthulhu calculation and I got an error.  Any help would be appreciated.   (I've attached the calc pic in PBI with the error and the calc in excel so the bottom of the calc shows)

I may be #4 - remains to be seen.  We're taking bi-weekly vuln scan results to populate a metrics dashboard.  Some CVEs (vulns) have recurred on unique hosts due to using an older VM image that contains the vuln, or occasionally spinning up/shutting down a container image that contains the vuln.  We're tracking those as separate instances (x days between first and last scan vuln is present), and having a way to tag each instance will be helpful.  We have 80+ hosts and the accumulated scans are >300k rows and growing.  Power BI Desktop and PowerShell have been instrumental in making use/sense of the data. Powershell is used to extract the scan info from the xml .nessus file, reduce the data to the desired columns and metric info, then compare the previous scan to the current scan and output a file that contains flags for remediated CVEs.  Those files are used by Power BI to slice, dice & display the data.