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    <p>I'm trying to turn a huge spreadsheet of data into a database to make data analysis easier, but I'm running into problems with too many columns. I've tried my best to learn about normalization, but I'm having a hard time applying it to this use case.</p> <h2>Scenario</h2> <p>We are performing N <em>independent</em> measurements on rectangular blocks. Measurements include:</p> <ul> <li>Length (or Measurement 0)</li> <li>Width (or Measurement 1)</li> <li>Height (or Measurement 2)</li> <li>Mass (or Measurement 3)</li> <li>Color (or Measurement 4)</li> <li>...</li> <li>Measurement N</li> </ul> <p>There are over 7000 measurements (complicated blocks)! The measurements have limits. If a block fails one or more measurements, all measurements are repeated to verify. If it fails again, the block is deemed a failure.</p> <p>The blocks are serialized and there are thousands of them.</p> <h2>Data Source</h2> <p>A huge spreadsheet (table). The fields are: Block Number, Length, Width, Height, Mass, Color, ..., Measurement N. Each row represents one test run or execution of all measurements. Since we have a retest policy, there may be multiple rows with results from the same block.</p> <h2>Help!</h2> <p>This source table seems like an intuitive format, but doesn't seem like the best format for a database. At first I tried to put it in an SQLite database and ran into the 2000 column limit. Yes I could recompile SQLite with more columns or use another database engine, but this sounds like more of a database design issue. Do you have a better design idea?</p> <p>P.S. Sorry so long, but thanks for reading!</p>
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