Dealing with required duplicates in table records - sql

Question

Here's the situation. My team forecasts sales and revenue numbers at a monthly resolution but would like all reporting to be at a daily resolution. So what I am doing is ingesting these numbers and dividing the monthly targets by number of days and saving it in a table.
So I start of with something like this:
| date | forecasted_units | forecasted_revenue |
|---------|------------------|--------------------|
| 2020-01 | 372 | 9300 |
| 2020-02 | 435 | 9280 |
...
My target table now looks like this:
| date | forecasted_units | forecasted_revenue |
|------------|------------------|--------------------|
| 2020-01-01 | 12 | 300 |
| 2020-01-02 | 12 | 300 |
| 2020-01-03 | 12 | 300 |
...
| date | forecasted_units | forecasted_revenue |
|------------|------------------|--------------------|
| 2020-02-01 | 15 | 320 |
| 2020-02-02 | 15 | 320 |
| 2020-02-03 | 15 | 320 |
...
Now my table is quite a lot wider than the one above and all of them have duplicate records. As you can see there's a lot of data redundancy. Now my question is, Is there a more efficient method to save the same resolution of data in one table.
My immediate thought is to reshape the table to include a start date and end date to look like this:
| start_date | end_date | forecasted_units | forecasted_revenue |
|------------|------------|------------------|--------------------|
| 2020-01-01 | 2020-01-31 | 12 | 300 |
| 2020-02-01 | 2020-02-29 | 15 | 320 |
But that would offload all the computation to the instance generating all the reports because it would have to generate the data for each day in between the start and end date.
Is there a better way to do this?

Answer 1

Unfortunately, Redshift does not support handy Postgres function generate_series(), which would have largely simplified the task here.
Typical alternative solutions would involve a calendar table - basically, a table that enumerates all possible dates. If you have a table with a sufficient number of rows, you can generate such dataset on the fly with row_number() and dateadd():
select dateadd(day, row_number() over(order by 1) - 1, '2020-01-01') dt
from my_large_table;
You can store the results in another table (using the create table ... as select ... syntax), or use the query result directly. In both cases, you would then join it with your actual table. To count the number of days in the month, we use a window count:
select
d.dt,
t.forecasted_unit / count(*) over(partition by t.date) forecasted_units,
t.forecasted_revenue / count(*) over(partition by t.date) forecasted_revenue
from (
select dateadd(day, row_number() over(order by 1) - 1, '2020-01-01') dt
from my_large_table
) d
inner join mytable t on t.date = date_trunc('month', d.dt)