It's not really clear the difference between the table as an argument in ALL and a column. Also, why ALL(productColor) didn't change the measure but All(product name) did? Isn't it removing ALL the filters regardless of the column?
Shashank you should share datasets and pbix file along with your video in description box for practical purposes it will be helpful for us for practices.
I have a question that is, I have a table visual with 5 fields and I have same in the slicer now where ever the client select any two fields it have to give the difference how can we get any idea that u can share
Hi Bro. Your video's are helped me a lot. I have a question which asked me in an interview. How to cache the data / pre-calculating data to optimize speed ?
In Power BI, you can optimize performance and speed by caching data through various methods. Here are some strategies you can use to cache or pre-calculate data in Power BI: DirectQuery vs. Import Mode: Power BI offers two main data storage modes: Import and DirectQuery. Import mode loads data into Power BI's internal data model, allowing for faster performance as data is cached in memory. DirectQuery mode queries data directly from the data source, which can be slower but ensures real-time data access. You can choose the appropriate mode based on your data size, refresh frequency, and performance requirements. Data Refresh Scheduling: Schedule regular data refreshes to ensure that your dataset stays up-to-date while also caching the data for faster access. Power BI allows you to set up automatic refresh schedules for datasets, ensuring that the data is refreshed at specified intervals. Incremental Data Refresh: Implement incremental data refresh to update only the new or changed data since the last refresh. This helps reduce data refresh time and optimizes performance, especially for large datasets. Data Aggregation: Pre-aggregate or summarize data where possible to reduce the volume of data loaded into memory. Aggregating data at a higher level of granularity can improve query performance and reduce memory usage. Optimize DAX Measures: Write efficient DAX measures that minimize the computational load on the data model. Avoid using complex or nested calculations that can slow down query performance. Use Calculated Columns Sparingly: Limit the use of calculated columns, especially if they involve heavy calculations or string manipulations. Calculated columns can increase the size of the data model and impact performance. Partitioning: Partition large tables into smaller segments based on date ranges or other criteria. Partitioning helps improve query performance by allowing Power BI to query only relevant segments of data. Optimize Data Model Relationships: Create optimized relationships between tables in your data model to improve query performance. Use appropriate cardinality and cross-filter direction settings based on your data relationships. Use Aggregations: Set up aggregations to store pre-calculated summary data at different levels of granularity. Aggregations help accelerate query performance, especially for large datasets. Data Compression: Ensure that your data is stored in a compressed format to reduce storage requirements and improve query performance. Power BI automatically compresses data where possible. By implementing these strategies, you can effectively cache data and optimize performance in Power BI, resulting in faster report rendering and improved user experience.
Asked in EY Product Name Category AbcBike Bike Bike123 Bike DEF Accessories LFG Accessories Bike Bike Need to create this in Power Bi category calculated column My solution Category = Switch ( TRUE(), CONTAINSSTRING(‘Table[Product Name],”Bike”),”Bike”, CONTAINSTRING(‘Table[Product name ],”Accessories “),”Acceesories”, “Accessories “ )
Crystal clear ♥️ Thanks man for the wonderful explanation.
Glad it was helpful!
Very nice explanation.
Thank you very much.
So basically, AllExcepts is the opposite of All
It's not really clear the difference between the table as an argument in ALL and a column. Also, why ALL(productColor) didn't change the measure but All(product name) did? Isn't it removing ALL the filters regardless of the column?
Really superb. Clear explanation with a simple example. Thanks Shashank for sharing this video with us.😊
Glad you liked it.
Shashank you should share datasets and pbix file along with your video in description box for practical purposes it will be helpful for us for practices.
Nice video ,my doubts are cleared only by your videos .thanks a lot .
Glad to hear that.
Good Content always 🙂
Thank you 😊
Thank you for sharing this Important Interview Question with us Sir.
You are looking very nice in Black Shirt.👌
Dhanyawad 🙏
What an explanation sir❤❤ ,just wow
I have a question that is, I have a table visual with 5 fields and I have same in the slicer now where ever the client select any two fields it have to give the difference how can we get any idea that u can share
Didn't get your question
@@learnwidgiggs I have 5 feilds in my slice if my client select any two of them it have to give the difference of them
Hi Bro. Your video's are helped me a lot. I have a question which asked me in an interview. How to cache the data / pre-calculating data to optimize speed ?
File-options-setting-global- clear cache
In Power BI, you can optimize performance and speed by caching data through various methods. Here are some strategies you can use to cache or pre-calculate data in Power BI:
DirectQuery vs. Import Mode: Power BI offers two main data storage modes: Import and DirectQuery. Import mode loads data into Power BI's internal data model, allowing for faster performance as data is cached in memory. DirectQuery mode queries data directly from the data source, which can be slower but ensures real-time data access. You can choose the appropriate mode based on your data size, refresh frequency, and performance requirements.
Data Refresh Scheduling: Schedule regular data refreshes to ensure that your dataset stays up-to-date while also caching the data for faster access. Power BI allows you to set up automatic refresh schedules for datasets, ensuring that the data is refreshed at specified intervals.
Incremental Data Refresh: Implement incremental data refresh to update only the new or changed data since the last refresh. This helps reduce data refresh time and optimizes performance, especially for large datasets.
Data Aggregation: Pre-aggregate or summarize data where possible to reduce the volume of data loaded into memory. Aggregating data at a higher level of granularity can improve query performance and reduce memory usage.
Optimize DAX Measures: Write efficient DAX measures that minimize the computational load on the data model. Avoid using complex or nested calculations that can slow down query performance.
Use Calculated Columns Sparingly: Limit the use of calculated columns, especially if they involve heavy calculations or string manipulations. Calculated columns can increase the size of the data model and impact performance.
Partitioning: Partition large tables into smaller segments based on date ranges or other criteria. Partitioning helps improve query performance by allowing Power BI to query only relevant segments of data.
Optimize Data Model Relationships: Create optimized relationships between tables in your data model to improve query performance. Use appropriate cardinality and cross-filter direction settings based on your data relationships.
Use Aggregations: Set up aggregations to store pre-calculated summary data at different levels of granularity. Aggregations help accelerate query performance, especially for large datasets.
Data Compression: Ensure that your data is stored in a compressed format to reduce storage requirements and improve query performance. Power BI automatically compresses data where possible.
By implementing these strategies, you can effectively cache data and optimize performance in Power BI, resulting in faster report rendering and improved user experience.
Many Thanks for all videos!! Good work,keep it up🙏🙂👌
Most welcome 😊
Hi, I don't know why but I am getting confused alot in all these 3 all filters
Hi there! Is it true that we need to sign in to access all the features provided in power BI?
make videos about data modeling and give the examples also.
Okay..noted
can you please share the .pbix file (or the link) ?
Thank you for making this video based on my request
Welcome😊
Very clearly explained. Thanks a lot.
Nicely explained 👌
Glad to hear this!
Is it necessary to take pl 300 exam to become data analyst
No
Bro on wednesday my interview is their plz suggest me the important questions from python and power bi
@bangalore?
@@Engineering4675 hyderabad
From Power BI side, just revise all the videos from this channel...you are good to go.
Thanks for the videos
Welcome 😊
Nice explanation
Hello Shashank i want to know the answer of one question which was asked in one of my recent interview, where should i share my question with you ?
You can share on my mail id.
(mail id present in the about section of the channel)
Table a
Column1
12
15
35
65
Approved
Pending
67
98
Pending
Approved
This is the data how to do sum here?
First convert the non numeric values to null using conditional statement. Then sum the latest column which is recently made.
@@ananyabhattacharyya2362 not working
in modelling create a calculated column ValueType =
IF(
ISNUMBER(TableName[Values]),
"Numeric",
"Text"
) then sum the numeric
Live class they provide
Helpful information
Glad you found it useful
helpful video
Hi Shashank, yesterday i make a comment here, however it's deleted. I think you don't want to reply me.
Sorry I didn't see any message of yours. Maybe RUclips would have deleted it on its own if founded it as spam.
😂
paid promotion na , kitna diya aapko ???????
Asked in EY
Product Name Category
AbcBike Bike
Bike123 Bike
DEF Accessories
LFG Accessories
Bike Bike
Need to create this in Power Bi category calculated column
My solution
Category =
Switch (
TRUE(),
CONTAINSSTRING(‘Table[Product Name],”Bike”),”Bike”,
CONTAINSTRING(‘Table[Product name ],”Accessories “),”Acceesories”,
“Accessories “
)