Can we create Dynamic Date table in mapping Data Flow? - powerbi

Question

I have a query in Power BI that takes two parameter: Start Date and End Date.
Whenever I pass these Dates it return a table of Date that contain few columns created according to this range of date such as Date, QuarterofYear, Year, MonthName......etc.
Can we create a mapping data flow in ADF that takes two parameter as input and return a calculated table according to provided dates?
Is there any function that return the range of dates?

Answer 1

For your request: "I want that I pass two date Start Date and End Date in ADF Mapping Data Flow , and Data flow will Create a column such as "Date" that contain that number of Date rows. Is there any function for this? Exam. Start Date=20-01-2019, End Date=20-01-2020 Then Date Column Values should be: 20-01-2019 21-01-2019 ......... ......... 20-02-2020", according the Data Factory documents and my experience, the answer is no, we can't achieve it in Data Flow.

Answer 2

There is a solution to this, but it is a bit tricky.
TL;DR
The general data flow looks like this:
We need a dummy source with exactly one row which contains whatever.
Then we derive a column where we use the mapLoop() expression to create an array of all the dates we want to get rows for.
Finally, we need to flatten the array column which will result in one row per array entry and thus one row per date.
Walkthrough
Source dummy
Each dataflow needs a source and we need exactly one row to make our dataflow work. To achieve this I've created a dataset called empty of type CSV in my data lake which has this content:
empty
""
This is our source definition:
And its result looks like this:
Derived column days
This is where the magic happens!
We create a new column dates which is an array of all the dates we want to have in our date table:
In this scenario we want a date table starting on 2019-01-01 and reaching one year into the future. The full expression looks like this:
mapLoop(
addDays(currentDate(), 365) - toDate(2019-01-01),
addDays(toDate(2019-01-01), #index)
)
This is what happens here:
the mapLoop() function builds an array of elements. You specify the number of elements you want to have and the lambda expression to calculate each of the elements. For example, mapIndex([1, 2, 3, 4], #item + 2 + #index) results in [4, 6, 8, 10]
addDays(currentDate(), 365) - toDate('2019-01-01') is the number of days between our start (2019-01-01) and end date (1 year in the future from now) and thus the number of dates we want to have in our resulting array.
addDays(toDate(2019-01-01), #index) calculates each array item by adding #index days to our start date. This is executed for the number of days we've calculated before and #index is the array position. Thus, the first element of the array will be 2019-01-01 + 1, the second 2019-01-01 + 2 and so on.
Our stream now has these columns:
Flatten
Finally, you need a flatten transformation which will expand each item in your array to its dedicated row. We can also dismiss the useless empty column in this step:
And this finally results in what we wanted to achieve:
References
Data transformation expressions in mapping data flow