Duplicate records upon joining table - sql

Question

I am still very new to SQL and Tableau however I am trying to work myself towards achieving a personal project of mine.
Table A; shows a table which contains the defect quantity per product category and when it was raised
+--------+-------------+--------------+-----------------+
| Issue# | Date_Raised | Category_ID# | Defect_Quantity |
+--------+-------------+--------------+-----------------+
| PCR12 | 11-Jan-2019 | Product#1 | 14 |
| PCR13 | 12-Jan-2019 | Product#1 | 54 |
| PCR14 | 5-Feb-2019 | Product#1 | 5 |
| PCR15 | 5-Feb-2019 | Product#2 | 7 |
| PCR16 | 20-Mar-2019 | Product#1 | 76 |
| PCR17 | 22-Mar-2019 | Product#2 | 5 |
| PCR18 | 25-Mar-2019 | Product#1 | 89 |
+--------+-------------+--------------+-----------------+
Table B; shows the consumption quantity of each product by month
+-------------+--------------+-------------------+
| Date_Raised | Category_ID# | Consumed_Quantity |
+-------------+--------------+-------------------+
| 5-Jan-2019 | Product#1 | 100 |
| 17-Jan-2019 | Product#1 | 200 |
| 5-Feb-2019 | Product#1 | 100 |
| 8-Feb-2019 | Product#2 | 50 |
| 10-Mar-2019 | Product#1 | 100 |
| 12-Mar-2019 | Product#2 | 50 |
+-------------+--------------+-------------------+
END RESULT
I would like to create a table/bar chart in tableau that shows that Defect_Quantity/Consumed_Quantity per month, per Category_ID#, so something like this below;
+----------+-----------+-----------+
| Month | Product#1 | Product#2 |
+----------+-----------+-----------+
| Jan-2019 | 23% | |
| Feb-2019 | 5% | 14% |
| Mar-2019 | 89% | 10% |
+----------+-----------+-----------+
WHAT I HAVE TRIED SO FAR
Unfortunately i have not really done anything, i am struggling to understand how do i get rid of the duplicates upon joining the tables based on Category_ID#.
Appreciate all the help I can receive here.

Answer 1

I can think of doing left joins on both product1 and 2.
select to_char(to_date(Date_Raised,'d-mon-yyyy'),'mon-yyyy')
, (p2.product1 - sum(case when category_id='Product#1' then Defect_Quantity else 0 end))/p2.product1 * 100
, (p2.product2 - sum(case when category_id='Product#2' then Defect_Quantity else 0 end))/p2.product2 * 100
from tableA t1
left join
(select to_char(to_date(Date_Raised,'d-mon-yyyy'),'mon-yyyy') Date_Raised
, sum(Comsumed_Quantity) as product1 tableB
where category_id = 'Product#1'
group by to_char(to_date(Date_Raised,'d-mon-yyyy'),'mon-yyyy')) p1
on p1.Date_Raised = t1.Date_Raised
left join
(select to_char(to_date(Date_Raised,'d-mon-yyyy'),'mon-yyyy') Date_Raised
, sum(Comsumed_Quantity) as product2 tableB
where category_id = 'Product#2'
group by to_char(to_date(Date_Raised,'d-mon-yyyy'),'mon-yyyy')) p2
on p2.Date_Raised = t1.Date_Raised
group by to_char(to_date(Date_Raised,'d-mon-yyyy'),'mon-yyyy')

Answer 2

By using ROW_NUMBER() OVER (PARTITION BY ORDER BY ) as RN, you can remove duplicate rows. As of your end result you should extract month from date and use pivot to achieve.

Answer 3

I would do this as:
select to_char(date_raised, 'YYYY-MM'),
(sum(case when product = 'Product#1' then defect_quantity end) /
sum(case when product = 'Product#1' then consumed_quantity end)
) as product1,
(sum(case when product = 'Product#2' then defect_quantity end) /
sum(case when product = 'Product#2' then consumed_quantity end)
) as product2
from ((select date_raised, product, defect_quantity, 0 as consumed_quantity
from a
) union all
(select date_raised, product, 0 as defect_quantity, consumed_quantity
from b
)
) ab
group by to_char(date_raised, 'YYYY-MM')
order by min(date_raised);
(I changed the date format because I much prefer YYYY-MM, but that is irrelevant to the logic.)
Why do I prefer this method? This will include all months where there is a row in either table. I don't have to worry that some months are inadvertently filtered out, because there are missing production or defects in one month.