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An JDBC datasource implementation.

There is a newer version: 2.0.0_preview_973

select
   s_store_name
  ,s_company_id
  ,s_street_number
  ,s_street_name
  ,s_street_type
  ,s_suite_number
  ,s_city
  ,s_county
  ,s_state
  ,s_zip
  ,sum(case when (sr_returned_date_sk - ss_sold_date_sk <= 30 ) then 1 else 0 end)  as "30_days"
  ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 30) and 
                 (sr_returned_date_sk - ss_sold_date_sk <= 60) then 1 else 0 end )  as "31_60_days"
  ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 60) and 
                 (sr_returned_date_sk - ss_sold_date_sk <= 90) then 1 else 0 end)  as "61_90_days"
  ,sum(case when (sr_returned_date_sk - ss_sold_date_sk > 90) and
                 (sr_returned_date_sk - ss_sold_date_sk <= 120) then 1 else 0 end)  as "91_120_days"
  ,sum(case when (sr_returned_date_sk - ss_sold_date_sk  > 120) then 1 else 0 end)  as above120_days
from
   store_sales
  ,store_returns
  ,store
  ,date_dim d1
  ,date_dim d2
where
    d2.d_year = 2001
and d2.d_moy  = 8
and ss_ticket_number = sr_ticket_number
and ss_item_sk = sr_item_sk
and ss_sold_date_sk   = d1.d_date_sk
and sr_returned_date_sk   = d2.d_date_sk
and ss_customer_sk = sr_customer_sk
and ss_store_sk = s_store_sk
group by
   s_store_name
  ,s_company_id
  ,s_street_number
  ,s_street_name
  ,s_street_type
  ,s_suite_number
  ,s_city
  ,s_county
  ,s_state
  ,s_zip
order by s_store_name
        ,s_company_id
        ,s_street_number
        ,s_street_name
        ,s_street_type
        ,s_suite_number
        ,s_city
        ,s_county
        ,s_state
        ,s_zip
 limit 100