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The Economics of Reusability topic is old, last posted in It's a good topic to review for context, but probably since it's locked, discussion should continue here.
Just ignore it, I say!!! Just hopefully not at the point of impact. Since she seemed to be talking about now, I'd assume that she was talking about the present launch rate and manifest.
This should be a good price point to start the discussion. Note: Some say that SpaceX isn't making a profit, and others say they are losing money on each launch, so profit can be thought of as positive, zero, or negative.
The key issue has always been elasticity of demand, and the key issues for elasticity of demand are risk tolerance and time.
I think SpaceX may need to fill its manifest gaps with home grown payloads while waiting for the satellite manufacturers to catch up.
Hopefully that satellite internet thing works out. I'm just going to add a thought. Just because a stage it deemed "reusable" and launched it may not be "reliable" One needs to factor this into the cost structure estimates It is all well and good to quote those things that made it past your confirmation bias that other people wrote, but this is a discussion board damnit!
Let us know what you think! And why! Caldari State. Ren Nakami. Nipple Facee Corporation. Nipple Facee. The John Galt Express.
Jane Galt. I Corpa. Native Freshfood. Minmatar Republic. Post Comment. No Comments. 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.
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