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So assuming reusability is given and SpaceX won't scale down, which I believe are true assumtions, cost reduction allowed by this humongous achievement is dependent from demand side and can be very small if there is no increase in launch rate.
Space X has some backlog so some increase is possible. But how big is that? And am I missing something? Maybe raw materials and outsorced parts are really costly?
Or my reasoning is flawed? There have been lots of discussions on this, and pretty much everyone agrees that you need a high launch rate to justify reuse.
The main disagreement is over what exactly that launch rate is and how likely SpaceX is to achieve it. Journalists For Space likes this.
Sorry - my fault, or mobile opera to have something to blame :p Economics of reusability is exactly the thread which I failed to found.
Thanks a lot for pointing me there. And this thread probably should be removed since it's duplicate to keep things clear.
I couldn't do that myself. Posted October 4, Hidden Content Give reaction and reply to this topic to see the hidden content.
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There is a Problem During the implementation of SSAS Tabular model, you usually hit some point you think about, managing some calculation as calculated column.
The content of my measures was defined by quite complicated filtering expressions such as: Using ranges of value of one column.
Values which contains value of account number between 0 and Many different filters in same column. Values which contains value of account number between 0 and or and or … Filtering in more columns Possible Solutions In following sections, I will describe how I managed to handle calculated columns in all layers of the reporting solution and what its impact was.
Outcome of this solution was: Good performance of the queries Solid readability and maintainability Minimal increase of data volume in memory I was fully satisfied with this solution until I implemented incremental processing into the cube.
To sum this up, there is one downside of this solution which can be defined as following: Poor performance in incremental data loading phase ETL Therefore I decided to move calculated columns to lower layer.
Outcome of my implementation can be defined as followed: Minimal impact to ETL duration incremental and initial.
Minimal impact to data volume on SQL layer as we stored fact table as clustered column store index. Same goes for SSAS layer.
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But before this you have to BUY extractors that cost atm ish.