Query to reorganize dates - sql

Question

I need to do a transformation of a Postgres database table and I don't know where to start.
This is the table:
| Customer Code | Activity | Start Date |
|:---------------:|:--------:|:----------:|
| 100 | A | 01/05/2017 |
| 100 | A | 19/07/2017 |
| 100 | B | 18/09/2017 |
| 100 | C | 07/12/2017 |
| 101 | A | 11/02/2018 |
| 101 | B | 02/04/2018 |
| 101 | B | 14/06/2018 |
| 100 | A | 13/07/2018 |
| 100 | B | 14/08/2018 |
Customers can perform activities A, B and C, always in that order.
To carry out activity B he/she has to carry out activity A. To carry out C, he/she has to carry out activity A, then to B.
An activity or cycle can be performed more than once by the same customer.
I need to reorganize the table in this way, placing the beginning and end of each step:
| Customer Code | Activity | Start Date | End Date |
|:---------------:|:--------:|:----------:|:----------:|
| 100 | A | 01/05/2017 | 18/09/2017 |
| 100 | B | 18/09/2017 | 07/12/2017 |
| 100 | C | 07/12/2017 | 13/07/2018 |
| 101 | A | 11/02/2018 | 02/04/2018 |
| 101 | B | 02/04/2018 | |
| 100 | A | 13/07/2018 | 14/08/2018 |
| 100 | B | 14/08/2018 | |

Answer 1

Here is approach at this gaps-and-islands problem:
select
customer_code,
activity,
start_date,
case when (activity, lead(activity) over(partition by customer_code order by start_date))
in (('A', 'B'), ('B', 'C'), ('C', 'A'))
then lead(start_date) over(partition by customer_code order by start_date)
end end_date
from (
select
t.*,
lead(activity) over(partition by customer_code order by start_date) lead_activity
from mytable t
) t
where activity is distinct from lead_activity
The query starts by removing consecutive rows that have the same customer_code and activity. Then, we use conditional logic to bring in the start_date of the next row when the activty is in sequence.
Demo on DB Fiddle:
customer_code | activity | start_date | end_date
------------: | :------- | :--------- | :---------
100 | A | 2017-07-19 | 2017-09-18
100 | B | 2017-09-18 | 2017-12-07
100 | C | 2017-12-07 | 2018-07-13
100 | A | 2018-07-13 | 2018-08-14
100 | B | 2018-08-14 | null
101 | A | 2018-02-11 | 2018-06-14
101 | B | 2018-06-14 | null