Need to Impute Missing Data from Sparsely Populated Table - sql

Question

I am trying to populate a #temp reporting table from an existing sparse table with 2 key elements-- datekeys and prices based on a date range and prices as they change. Here is the data that exists in the price change table:
The date range for a report 2 days ago on a rolling 7 day cycle would include 6/12 - 6/19. The 2 rows in the table have an old price and old price variance from 6/7 out of range for that report, however, the price and variance is needed in order to dub it in under the CashPrice column for all datekeys 20190612 through 20190618. On 6/19 there is a new price change/variance. On that
datekey, the new price/variance values should change.
The set-up data required for reporting would like this:
Here is the code to build the sampling data:
-- T-SQL script to build the sampling tables
-- use for date-related cross-join later
if object_id( N'tempdb..#Numbers', N'U' ) is not null
drop table #Numbers;
create table #Numbers(
n int
);
insert #Numbers( n ) values( 0 ), ( 1 ), ( 2 ), ( 3 ), ( 4 ), ( 5 ), ( 6 ),
( 7 ), ( 8 ), ( 9 ), ( 10 )
-- select * from #Numbers;
-- creating existing sparse price data
if object_id( N'tempdb..#dt', N'U' ) is not null
drop table #dt;
create table #dt(
StoreNumber int
, City char( 3 )
, State char( 2 )
, Type char( 1 )
, ProductKey int
, DateKey int
, CashPrice money
, DateLastPriceChange datetime
, CashPriceVar money
)
insert #dt values
( 1, 'OKC', 'OK', 'D', 144, 20190607, 2.799, '2019-06-07 11:37', -0.1 )
, ( 1, 'OKC', 'OK', 'D', 144, 20190619, 2.699, '2019-06-19 10:40', -0.1 )
-- select * from #dt;
-- creaing temporary working table for reporting
if object_id( N'tempdb..#tt', N'U' ) is not null
drop table #tt;
create table #tt(
StoreNumber int
, City char( 3 )
, State char( 2 )
, Type char( 1 )
, ProductKey int
, DateKey int
-- a couple of extra columns here
, DateKeyDate date
, DataDateKey int
, CashPrice money
, DateLastPriceChange datetime
, CashPriceVar money
)
-- dub in the start date for the report
declare #StartDateKey date = '2019-06-12'
-- populate the temporary working table
insert #tt( StoreNumber, ProductKey, DateKeyDate )
select distinct
dt.StoreNumber
, dt.ProductKey
, dateadd( day, n.n, #StartDateKey )
from #dt dt
cross join #Numbers n
where
n.n <= 7
-- select * from #tt
-- change the added datekeydate to datekey format
update #tt
set DateKey = year( DateKeyDate ) * 10000 + month( DateKeyDate ) * 100 +
day( DateKeyDate )
Here is the code that I have been working on that limps along. It's not ideal and in many cases against the full suite of data, it filters out the dates I cross-joined, so that I am left without the imputed data-- showing only the original known price changes. Please advise.
select
dd.StoreNumber
, dto.City
, dto.State
, dto.Type
, dd.ProductKey
, dd.DateKey
, dto.CashPrice
, dto.DateLastPriceChange
, dto.CashPriceVar
from (
select
tt.StoreNumber
, tt.ProductKey
, tt.DateKey
, max( dt.DateKey ) MaxDateKey
from #tt tt
inner join #dt dt
on dt.StoreNumber = tt.StoreNumber
and dt.ProductKey = tt.ProductKey
and dt.DateKey <= tt.DateKey
group by
tt.StoreNumber
, tt.ProductKey
, tt.DateKey
) dd
inner join #dt dto
on dto.StoreNumber = dd.StoreNumber
and dto.ProductKey = dd.ProductKey
and dto.DateKey = dd.MaxDateKey;
EDIT: Can anyone advise as to whether or not the SQL Server LEAD function might be a better choice--perhaps comparing the value of the current row with the value of the following row?