Hi Folks --
As we continue our efforts to enable our power-users to pull dynamic queries without needing to resort to SQL, we've run into an Analytics request that has me stumped.
So, the request is this: for a Tessitura Season, how many unique customers attended n number of unique production seasons. The output from the SQL query that answers this question is below, along with the SQL. The SiSense conundrum is can we use an aggregate as a row value in order to then aggregate values based on that formula-based metric?
Thanks!
DGomez
select u.season_desc ,u.num_unique_prod_seasons ,count(distinct u.customer_no) as num_unique_customersfrom( select s.description as season_desc ,o.customer_no ,count(distinct p.prod_season_no) as num_unique_prod_seasons from T_ORDER o with (nolock) join T_SUB_LINEITEM sli with (nolock) on o.order_no = sli.order_no join T_PERF p with (nolock) on sli.perf_no = p.perf_no join T_PROD_SEASON ps with (nolock) on p.prod_season_no = ps.prod_season_no join TR_SEASON s with (nolock) on ps.season = s.id join TR_PRICE_TYPE pt with (nolock) on sli.price_type = pt.id join TR_PRICE_TYPE_CATEGORY ptc with (nolock) on pt.price_type_category = ptc.id --select * from TR_PRICE_TYPE_CATEGORY where sli.sli_status in (2,3,12) and ps.season in (269) --select * from TR_SEASON where fyear = 2020 and ptc.id not in (1,4) and exists ( select 1 from T_LIST_CONTENTS lc1 where lc1.customer_no = o.customer_no ) group by s.description, o.customer_no) as ugroup by u.season_desc, u.num_unique_prod_seasonsorder by u.season_desc, u.num_unique_prod_seasons
Try looking around on Sisence's website. I think I saw something in the Data Modeling or Knowledge Base section.
https://support.sisense.com/hc/en-us/sections/115003604948-Data-Modeling-
A word of warning. You are going to find tons of things we cannot currently do in Analytics; but will also find some helpful ideas you can run with.
Thanks for this Neil! I haven't found too much I haven't been able to do in Analytics -- but we're self-hosted. Huge shoutout and credit where credit is due to Chris W. and the entire Tessitura Analytics dev team. They continue to amaze and astound!