@shanebo3239 wrote:
I work for a customer service center where we have customers come into a lobby and are served with tickets (kind of like the old "take a number" system). In our office, we have two big datasets. The first is from our queueing system, and the second is from our cashiering system. These systems are not linked in any way. The queueing system has a table called history and the cashiering system has a table called transactions.
Here is what the queueing system's history table contains:
StartDate | EndDate | CustomerID | DeskID
09-14-2017 12:47pm | 09-14-2017 1:02 PM | 101 | 12
09-14-2017 12:50 pm | 09-14-2017 1:16 PM | 102 | 15
The transactions table will contain something like this:
TransactionDate | AmountPaid | DeskID
09-14-2017 1:01 PM | 45.25 | 12
09-14-2017 1:14 PM | 102.60 | 15
Only one customer can possibly transact one transaction at one DeskID at one time. Therefore, I should be able to join the data in such a way that I can check the date range and the DeskID and determine which transaction belongs to which history entry. That would result in something like this:
history.StartDate | history.EndDate | transaction.TransactionDate | transaction.AmountPaid | DeskID | history.CustomerID
I have this working somewhat in SQL (both of these data sets are on SQL Azure) by doing a JOIN, but it is SLOW, and I'd rather bring the datasets into Power BI natively and then let PowerBI join them. I just don't see a way of doing it.
Thoughts?
@shanebo3239
You could try to implement the similar JOIN logic in DAX when creating a calculated table.
Table =
FILTER (
CROSSJOIN (
SELECTCOLUMNS (
history,
"history.StartDate", history[StartDate],
"history.Enddate", history[EndDate],
"history.CustomerID", history[CustomerID],
"history.DeskID", history[DeskID]
),
transactions
),
[history.DeskID] = transactions[DeskID]
&& transactions[TransactionDate] > [history.StartDate]
&& transactions[TransactionDate] <= [history.Enddate]
)
However, I would concern about the performance when the dataset is huge. I'd still suggest you do the JOIN in Azure database. As to performance aspect, try to create proper index and apply proper where clause to narrow down the date range to shrink the data size.
