![]() ![]() Yes, data model can handle millions of rows of data quite comfortably, but it will depend on your Excel version. csv (about 50 to 100 rows of data to test things). If you have small file that's representative of your. I often deal with log for PBX system with conferencing bridge and these can be quite large.ĭepends. I can do some VBA coding if that is needed.įor Log file. If there are any other options I am open to it. Will Power Query work if I load these files into a Data model (The aim is to use the data on a weekly/monthly basis to create 20 different graphs that show shows either weekly or monthly comparisons).Is there an easier way to do this conversion on a daily basis?.Is is normal for the file sizes to be this big?.I will have to extract and convert these files to Excel files on a daily basis, which is time-consuming since the process is tedious but also the big files take long to open. I'm trying to use Power Query to see if this works easier and quicker as an option at the moment.csv file in Notepad, copy data and paste in clean Excel workbook, then run text-to-column and set delimiter as pipe csv file in Excel and set the delimiter to pipe. csv file in Excel because it reads it as a comma separator and I lose some data on certain rows because 1 of the columns' data has a comma in. I do realize the amount of data could be the problem of the file size? The files contain no formulas, conditional formatting or anything except the converted data, I did check for used range size and there are no extra empty cells after the last row or column. There are roughly 33 million cells of data (roughly 950 000 rows and 35 columns). When I convert them in Excel it's still quite big (150-190MB). csv file sizes are extremely big (300 to 330MB). ![]() The row separator is tab-delimited and the column separator is pipe-delimited (|).Ĭurrently the. I have data files that get extracted from an online system which is saved in. ![]()
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