DAX equiv: Historical Trending visual needed, complex

Really need a DAX equivalent for producing a dataset that can be used for Historical Trending of application outages.

I can solve in SQL but have two issues: I need to do it in DAX because I am pulling direct from CRM.  Second, don't know how to get the pbix visual sorted in chronological order.

 Demo pbix file shared  https://www.dropbox.com/sh/6kgldb1fhfichy4/AAAbXQhUr8VvyD65qTEOvIPFa?dl=0

Above, Napson application was down <1 day, Shapeeze >1day, and Strikeone outage overlapped Shapeeze outage.   

 DDL shows how data is stored in source.

CREATE TABLE Alerts (
AlertID int,
AlertName varchar(100),
ApplicationName varchar(50),
IssueStartTime datetime,
ResolutionTime datetime)

INSERT INTO Alerts (AlertID,AlertName,ApplicationName,IssueStartTime,ResolutionTime)
VALUES 
(160922, 'Can''t login', 'Shapeeze', '2017-05-08 12:00:00.000', '2017-05-23 18:00:00.000'),
(150319, 'Errors Submitting Forms','Shapeeze', '2017-05-23 06:00:00.000', '2017-05-29 13:00:00.000'),
(210522, 'Server down', 'Napson', '2017-05-01 03:00:00.000','2017-05-01 23:00:00.000'),
(120311, 'Customers Being Misrouted', 'Strikeone','2017-05-15 16:00:00.000', '2017-05-19 09:00:00.000')


CREATE TABLE Alert_Application (
AlertID int,
ApplicationID int)

INSERT INTO Alert_Application
VALUES
(160922, 1001),
(150319, 1001),
(210522, 2000),
(120311, 3000)

CREATE TABLE Applications (
ApplicationID int,
AppName varchar(20))

INSERT INTO Applications 
VALUES
(1001, 'Shapeeze'),
(2000, 'Napson'),
(3000, 'StrikeOne');


CREATE TABLE Date

SQL SOLUTION

First build a time table having one row for every hour of the day, for time period I am tracking (in example, it is month of May)

--Time table
;WITH e1(n) AS(
	SELECT * FROM (VALUES(1),(1),(1),(1),(1),(1),(1),(1),(1),(1))x(n)
),
e2(n) AS(
	SELECT e1.n FROM e1, e1 x
),
e4(n) AS(
	SELECT e2.n FROM e2, e2 x
),
e8(n) AS(
	SELECT e4.n FROM e4, e4 x
),
cteTally(n) AS(
	SELECT TOP 6307204 ROW_NUMBER() OVER(ORDER BY (SELECT NULL)) - 1
	FROM e8
),
Test(n) AS(
SELECT DATEADD( hh, n, DATEADD( DAY, -45, GETDATE())) 
FROM cteTally)
select DATEADD(hh,datediff(hh,0,n),0)as cte_start_date
INTO dbo.Time 
FROM Test
WHERE n <= DATEADD( MONTH, 0, GETDATE())

SQL OUTER APPLY the source dataset to time table, pairing *every* row from source with row in time table, regardless of match.  OUTER JOIN does not work here as I need to know both whether the application was out or not out.  Both pieces of information are important for the historical trending graph to work

DECLARE	@FromDate AS DATETIME,
		@ToDate AS DATETIME

	
SET		@FromDate = GETDATE()-45
SET		@ToDate = GETDATE() 

select distinct
Tool
, right('0'+ DATENAME(dd, cte_start_date),2) + '-' + DATENAME(MONTH, cte_start_date) Date
, cte_start_date
, AppDownIs1
from 
(
SELECT 
Apps.[AppName] as Tool
, CrsApp.cte_start_date
, MAX( 
	CASE 
	WHEN CrsApp.cte_start_date BETWEEN A.[issuestarttime] AND [resolutiontime]  THEN 1 
	ELSE 0  
	END  ) as AppDownIs1

FROM
alerts A
JOIN [dbo].[Alert_Application] Appset on A.alertid = Appset.alertid
JOIN [dbo].[Applications] Apps on Appset.applicationid = Apps.applicationid
OUTER APPLY (SELECT t.cte_start_date FROM Time t 
	         WHERE cte_start_date >= @FromDate  AND  cte_start_date < @ToDate) CrsApp    

GROUP BY 
Apps.[AppName], CrsApp.cte_start_date


)t
 order by cte_start_date, Tool

The resulting dataset from this query is many times larger than the original due to pairing of each row in Alerts table with 720 rows in Time table per month (24 hours X 30 days).   This sample only tracks 4 apps over 1 month, but in real life will be tracking hundreds of apps over 1 year.  I worry the dataset will be too huge.

Can this be solved in DAX without SQL staging area?