I had a trouble visualizing my output in Power BI. I have imported more than 1500 CSV files but every time I visualize it, it only shows 10 CSV files that are randomly selected among the 1500 CSV files.
How can I see all 1500 CSV visualization at once rather than just 10 CSSV?
If it's not possible then I wanted to know how is 10 CSV files selected out of 1500 CSV files? Is there any calculation involved or is it just a random selection that Power BI dose on its own?
In image4 I would like to know how the calculation is di=one for Average, Sum, Medium, and Maximum.
I have attached screenshots for reference. I tried using various filters but none has given me the desired output. In Image4 you can see that I can select the Sum Average Minimum Maximum and other filters....but neither worked.
Power BI has a limit in the number of datapoints it can print on a graph.
since january you have more options with the high density line charts.
you can read more about it here:
high density sampling
Related
I'm trying to figure out a solution to my problem. Basically we get a monthly report with about 3000 records and there's a bunch of reporting that is done on that, and there are calculations based on various columns. e.g.
Date
Total usage
Recommended reduction
Product
01.01.2022
1000
500
A
01.01.2022
1300
70
B
01.01.2022
2000
900
C
...
...
...
At the end of it Power BI kindly sums up the columns which is great, but now what I am trying to do is take the sum of these columns and store them in a summary table so that it would be something like this so that I could use it for a time series visual
Month
Sum Total Usage
Sum Recommended Reduction
January
59720
12040
February
81020
20580
...
...
...
I have no idea how to go about doing this. Is this the right way to go ? Or is there a way to create a visual without having to create a summary table ? I'm at a bit of a loss, so any suggestions would be really appreciated.
You don't need any DAX calculations for that. Simply pull your data onto the fields of a line chart visual like shown below. Note that you have to drill-down from Year to Month to actually see the lines.
I am working on a Power BI report and I have this matrix:
In which I have Amount and Rate as values. What I need is to hide all the Rate values for each RATING and keep only the calculated Total Rate in this table.
I tried to Exclude the Rate column, but it just excludes the whole column instead(if I exclude the rate for the column "A-1" it excludes entire "A-1" column).
Is there any way I can achieve what I need?
Thank you in advance! :D
Good Idea would be to keep the calculations in underlying dataset/ table/ dataframe
Only fetch already calculated Amount in PBI.
Rates would not appear in your report, as you only pull up the calculated & desired column in PBI.
Also BI would be light weight.
I have 2 columns one containing order place time and another containing order delivered time and I have created a custom column where we calculate the delivery time in (dd:hh:mm:ss) format now I wanted to calculate average delivery time (i.e average of delivery time in (dd:hh:mm:ss) format).
can any one please suggest me any dax code for the same.
I'm using a calculated column that is an average. The problem is, the average is above the range of possible values, which should be impossible. I made a calculated column that calculates the average star rating (out of a range of 1-5) and the value on a visual is coming up as 6, which shouldn't be possible, even if all the values were 5 stars, which it isn't. So there must be an outlier causing the average to be above the range of possible values, but it isn't in the original data source which Power BI pulls from. The original data source shows me a value of 4.1 as an average, which is within the expected range. But Power BI's dataset has introduced an outlier or (data is missing) that caused the average to become a 6.
I can elaborate on the dax below, but what I want to try to do is pull the dataset down from power bi to figure out why it's calculating its average that way. Looking at the source data, the average is 4.1 and there are no outliers in the source data. So, it's not the source data that's the problem. Basically, I want to find the outlier that's causing the average rating to differ in Power BI.
Avg Rating = IF(SUM(data[Total Reviews]) = 0, BLANK(), SUM(data[Monthly Stars])/SUM(data[Total Reviews]))
Here's a screencap that shows the two
relevant columns
Notice that I had to manually calculate (aka eyeball the columns and type into a calculator then calculate manually) these two columns, which came out to ~4.6. I'm trying to download this dataset to explore it in further detail without having to eyeball the dataset, as the source doesn't show this discrepancy.
To get to the data you have a number of options.
Create a new report in Power BI Desktop, and then use the connect to PBI Dataset option to access that data, in for example, a table. You can create your own report based on the dataset in the service as well.
Access that data via Analyze in Excel, which should allow you to access the data in a pivot table using Excel
Use the Export data from the visual option, using this you can download 30,000 rows into a csv, or 150,000 in to xlsx formats
Please note, that these options may not be available to you if you do not have the right permissions in the workspace, or options have been turned off in the Power BI Admin tenancy settings.
I want to import a 500 GB dataset into Power BI, but Power BI is limited 1 GB. How can I get the data into Power BI?
Thanks.
For 500GB I'd definitely recommend Direct Query mode (as Joe recommends) or a live connection to a SSAS cube. In these scenarios, the data model is hosted in a separate location (such as a database server) and Power BI sends its queries to that location and displays the returned results.
However, I'll add that the 1GB limit is the limit after compression. (Meaning you can fit more than 1GB of uncompressed data into the advertised 1GB dataset limit.)
While it would be incredibly difficult to reduce a 500GB dataset to 1GB (even with compression), there are things you can do once you understand how the compression works in Power BI.
In Power BI, compression is done by columns, not rows. So a column that has 800 million rows with identical values can see significant compression. Likewise, a column with a different value in every row cannot be compressed much at all.
Therefore:
Do not import columns you do not absolutely need for analysis (particularly identity columns, GUIDs, free-form text fields, or binary data such as images)
Look at columns with a high degree of variability and see if you can also eliminate them.
Reduce the variability of a column where possible. E.g. if you only need a date & not a time, do not import the time. If you only need the whole number, do not import 7 decimal places.
Bring in less rows. If you cannot eliminate high-variability columns, then importing 1 year of data instead of 17 (for example) will also reduce the data model size.
Marco Russo & the SQLBI team have a number of good resources for further optimizing the size of a data model (SSAS tabular, Power Pivot & Power BI all use the same underlying modelling engine). For example: Optimizing Multi-Billion Row Tables in Tabular
If possible given your source data, you could use Direct Query mode. The 1 GB limit does not apply to Direct Query. There are some limitations to Direct Query mode, so check the documentation to make sure that it will meet your needs.
Some documentation can be found here.
1) make Aggregation on data on sql side __reduce size
2) import only useful column____________reduce size