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Replies posted by Augusto Silva

3 Replies

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Augusto SilvaNew Participant
asked in Metrics and MAQL

Building of an MAQL for Rolling Average

Hi Andress, thank you for your reply.Actually in Office Hours they helped us to undestand how the Average functions work in GoodData.We calculated the Average of the time (hours) via the MAQL below, and divided by 24 (to find the value in days). But this value, Fluxo Tempo Qt Horas was calculated by a Python Scripy, that we ran in Airflow. It calculated the time between a card entered and left a column in our KanBan.So the SLA for one column is the difference between a card entering and leaving it, and the average for that column, is the Average of the SLA. And to select just a time window, we put a filter in the dashboard, to select just thw preferredt ime.  

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2 years ago
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Augusto SilvaNew Participant
asked in Metrics and MAQL

Building of an MAQL for Rolling Average

Hi Iva, Yes, they helped us, and we were able the finish the metric and find the values we were looking for. Thank you! 

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2 years ago
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Augusto SilvaNew Participant
asked in Metrics and MAQL

Building of an MAQL for Rolling Average

Hi Iva, sorry for the late answer, I was in an end-of-year recess. First of all, we cannot alter our original tables, because other reports depend on this tables. We tought about making a series of math operations to achieve a final result.Following I attached some prints to explain what we plan to do:For the present data, we would select just the data of the last 90 days, per example, on 2022-03-01, only the data from 2022-01-01, 02-01 and 03-01 would be selected and summed for the same Id Card, and on 2022-04-01, only the data from 02-01, 03-01 and 04-01 would be selected and summed, taking in consideration the IdCard. This sum is the QtHours for each Id Card.For 2022-03-01, only the data from 2022-01-01 to 2022-03-01 is selected, summed and grouped by Id Card.For 2022-04-01, only the data from 2022-02-01 to 2022-04-01 is selected, summed and grouped by Id Card. In the next step, we would take the average of the Qt Hours for the Reference month. So, for 2022-03-01, we would take the

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2 years ago
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