T-SQL Query Won't execute when converted to Spark.SQL

Transact-SQL
1

Hello Community,

The following T-SQL executes with no problems in MS SQL Server

SELECT     MakeName, SUM(Cost) AS TotalCost
FROM       Data.Make AS MK INNER JOIN Data.Model AS MD 
           ON MK.MakeID = MD.MakeID
INNER JOIN Data.Stock AS ST ON ST.ModelID = MD.ModelID
WHERE      DateBought BETWEEN 
           CAST(YEAR(DATEADD(m, -1, GETDATE())) AS CHAR(4)) 
           + RIGHT('0' + CAST(MONTH(DATEADD(m, -1, GETDATE())) 
           AS VARCHAR(2)),2) + '01'
           AND EOMONTH(DATEADD(m, -1, GETDATE()))
GROUP BY   MakeName

I have converted the code to work with Spark.SQL as follows:

SELECT
  Make.MakeName
 ,SUM(SalesDetails.SalePrice) AS TotalCost
FROM Make
INNER JOIN Model
  ON Make.MakeID = Model.MakeID
INNER JOIN Stock
  ON Model.ModelID = Stock.ModelID
INNER JOIN SalesDetails
  ON Stock.StockCode = SalesDetails.StockID
INNER JOIN Sales
  ON SalesDetails.SalesID = Sales.SalesID
WHERE Stock.DateBought BETWEEN cast(year(add_months(current_date(),-1)) as CHAR(4)), add_months(cast(current_date() as CHAR(2)),-1) AND last_day(add_months(current_date(),-1))
GROUP BY MakeName

However, I'm getting the following error:

Error in SQL statement: ParseException:
mismatched input 'FROM' expecting (line 4, pos 0)

== SQL ==
SELECT
Make.MakeName
,SUM(SalesDetails.SalePrice) AS TotalCost
FROM Make
^^^
INNER JOIN Model
ON Make.MakeID = Model.MakeID
INNER JOIN Stock
ON Model.ModelID = Stock.ModelID
INNER JOIN SalesDetails
ON Stock.StockCode = SalesDetails.StockID
INNER JOIN Sales
ON SalesDetails.SalesID = Sales.SalesID
WHERE Stock.DateBought BETWEEN cast(year(add_months(current_date(),-1)) as CHAR(4)), add_months(cast(current_date() as CHAR(2)),-1) AND last_day(add_months(current_date(),-1))
GROUP BY MakeName

Now, before I get screamed out from someone from this group, I know this question should be targetted at someone from a spark.sql forum, but I can't get any help from the group.

I was just wondering if someone could just take a look at the error and figure out where I might be going wrong .. you may not need to be an expert in Spark.SQL

Any insights will be appreciated.