Scotty81
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- Sep 20, 2018
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- Excel 2016
The scenario: I've got some small data sources (a few thousand rows) as Excel tables inside a workbook where I'm performing some Power Query math. Specifically, I read the data in PQ from the tables, augment the data with some meta data, aggregate the results, and then load it to a pivot table (regular, not power). But, I also do further calcs, lookups and aggregations, and do this a couple of times, since folks need to see the data at different levels of granularity.
As I've added the layers, model performance has slowed considerably. In particular, when I open PQ and click on the last query in the stack, it takes about 4 minutes to load. Sprinkling Table.Buffer commands in the M code doesn't help; in fact, it slows down the updates. Since I have a lot of machinations to perform, on one hand, it makes sense for me to keep those machinations in PQ. On the other hand, if performance is intolerable, then I'd be forced to implement those machinations in Power Pivot - even though I'm making changes (table merges, and other calcs) at the row level.
I haven't found any Power BI books that address this issue. Are there any recommended sources with some practical and specific advice?
Thanks in advance.
As I've added the layers, model performance has slowed considerably. In particular, when I open PQ and click on the last query in the stack, it takes about 4 minutes to load. Sprinkling Table.Buffer commands in the M code doesn't help; in fact, it slows down the updates. Since I have a lot of machinations to perform, on one hand, it makes sense for me to keep those machinations in PQ. On the other hand, if performance is intolerable, then I'd be forced to implement those machinations in Power Pivot - even though I'm making changes (table merges, and other calcs) at the row level.
I haven't found any Power BI books that address this issue. Are there any recommended sources with some practical and specific advice?
Thanks in advance.