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Hey gregpetrossian, hopefully this is still going ahead (Unfortunately I may not be able to make it though as the time zone is a little out of whack for me)
Great overview of the options available in optimising our reports. I have two separate questions:
- When running Performance Analyzer in Power BI, do we need to first save the report on a blank page, reopen and then navigate to the page we want to analyse? Or is the "Refresh visuals" action good enough to analyse the relative performance of the visuals on the page?
- What are your top tips for new Power BI users for creating good performing DAX queries (especially if they have limited DAX knowledge)?
Also vote for Multi-row cards to have a vertical/horizontal orientation option (Microsoft Idea · Multi Row Card)
Thanks so much for your post Matthew! Hopefully timing works out and yes we are still on for tomorrow.
Hi gregpetrossian,
I have a sort of discussion type question regarding the Table vs. the Matrix visual. How much data is too much? And more importantly--how do you handle it when your audience wants you to fill either one with so many columns that you're scrolling right forever? I know how I handle it but would love to hear from others.
Another question for Marco. How much does Premium Capacity 'hide' badly performing reports versus 'shared' capacity?
Good day!
We would like to reduce the refresh time for one of our datasets. We're observing a 90 minute refresh duration using import mode for a dataset containing a fact table with ~250M rows. We're using premium capacity workspace (P1 SKU) with large dataset storage enabled. The data is being sourced from azsql (gen 5, 8 vcores) table with clustered column store index on fact table. Azsql CPU/data IO seems to stay under 20% utilization during the refresh. We've confirmed that a native query is being executed to refresh the fact table. We haven't pursued incremental refresh as the source table is trunc/load on daily frequency.
The fact table itself has fairly simple data types.
CREATE TABLE [dbo].[Fact_Table](
[c1] [int] NOT NULL,
[c2] [int] NOT NULL,
[c3] [date] NOT NULL,
[c4] [date] NOT NULL,
[c5] [varchar](1) NULL,
[c6] [int] NULL,
[c7] [date] NOT NULL,
[c8] [float] NULL,
[c9] [float] NULL,
[c10] [float] NULL,
[c11] [int] NULL,
[c12] [int] NULL,
[c13] [int] NULL,
[c14] [int] NULL,
[c15] [int] NULL,
[c16] [int] NULL,
[c17] [float] NULL,
[c18] [int] NULL,
[c19] [int] NULL,
[c20] [int] NULL,
[c21] [int] NULL,
[c22] [int] NULL,
[c23] [int] NULL,
[c24] [int] NULL,
[c25] [float] NULL
)Do you consider this scenario to be appropriate for import or have we reached a threshold where we should be considering DQ? We experimented some with DQ but encountered sluggish report performance and DAX limitations.
Any advice or troubleshooting suggestions would be greatly appreciated!
Thanks,
MK
Not performance related, but I have a requirement to apply dynamic column grouping to aggregated data and I'm wondering if there's a solution using DAX.
The underlying dataset is a table that contains STORE NUMBER, REGION, VOLUME, SALES, FORECAST.
STORE NUMBER REGION VOLUME SALES FORECAST 1 A HIGH 100 120 2 A LOW 20 20 3 B LOW 30 40 4 C HIGH 120 180 5 C HIGH 130 140 The initial presentation of the data will be as follows. The SALES and FORECAST data will be summed across the entire dataset with a count of total stores.
STORE COUNT SALES FORECAST 5 400 500 The users would then like to be apply dynamic grouping of the aggregated dataset without filtering out any of the underlying dataset. In this example, they'd want the options of grouping by data by (1) REGION, (2) VOLUME, or (3) REGION and VOLUME.
1. Group by REGION
STORE COUNT REGION SALES FORECAST 2 A 120 140 1 B 30 40 2 C 250 320 2. Group by VOLUME
STORE COUNT VOLUME SALES FORECAST 3 HIGH 350 440 2 LOW 50 60 3. Group by both REGION and VOLUME
STORE COUNT REGION VOLUME SALES FORECAST 1 A HIGH 100 120 1 A LOW 20 20 1 B LOW 30 40 2 C HIGH 250 320 Two potential solutions that have been presented for this are (1) bookmarks as buttons and (2) utilizing personalized visuals. Regarding #1, in the actual use-case we're dealing with 8 dynamic categorical columns where any combination could be selected, so creating that many different bookmarks doesn't seem feasible. Regarding #2, personalized visuals using a perspective will create the desired result, but we're concerned about the user experience of configuring the visual (and would also like to lock down the visualization type).
Is there a more graceful way to solve this problem using DAX and a slicer to dynamically choose the columns used to group the data without filtering data?
