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Before we understand PowerBI, lets learn:

What is BI?

Business intelligence is a technology-driven method which helps you to analyze data and to provide actionable information which helps corporate executives, business managers, and other users to make informed business decisions.

In this Microsoft Power BI tutorial for beginners, you will learn Power BI basics like:

What is Power BI?

Power BI is a Business Intelligence and Data Visualization tool for converting data from various data sources into interactive dashboards and analysis reports. Power BI offers cloud-based services for interactive visualizations with a simple interface for end users to create their own reports and dashboards.

Different Power BI versions like Desktop, Service-based (SaaS), and mobile Power BI apps are used for different platforms. It provides multiple software connectors and services for business intelligence.

In this Power BI training, you will learn all the important concepts of Power BI and develop a foundational understanding of how to use Power BI tool.

Why use Power BI?

Here are the most prominent use of Power BI tool:

Pre-built dashboards and reports for SaaS Solutions

Power BI allows real-time dashboard updates.

Offers Secure and reliable connection to your data sources in the cloud or on-premises

Power BI offers Quick deployment, hybrid configuration, and secure environment.

Allows data exploration using natural language query

Offers feature for dashboard visualization regularly updated with the community.

Types of Power BI tools

Now in this Power BI desktop tutorial, we will learn about types of Power BI tools.

Some Important Power BI tools are:

Power BI Desktop

Power BI desktop is the primary authoring and publishing tool for Power BI. Developers and power users use it to create brand new models and reports from scratch.

Costs: Free

Power BI service

Online Software as a Service (SaaS) where Powe Bl data models, reports, dashboards are hosted. Administration, sharing, collaboration happens in the cloud.

Pro license: $10/users/month

Power BI Data Gateway

Power BI Data Gateway works as the bridge between the Power Bl Service and on-premise data sources like DirectQuery, Import, Live Query. It is Installed by Bl Admin.

Power BI Report Server

It can host paginated reports, KPIs, mobile reports, & Power Bl Desktop reports. It is updated every 4 months and installed/managed by the IT team. The users can modify Power Bl reports other reports created by the development team.

Power BI Mobile Apps

Power BI mobile app is available for iOS, Android, Windows. It can be managed using Microsoft Intune. You can use this tool to view reports and dashboards on the Power Bl Service Report Server.

Data sources for the Power BI

Data source Description

Excel (.xlsx, xlxm) A workbook can have data entered manually or data, which is queried and loaded from external data sources.

Comma Separated Value (.csv) Files are simple text files with rows of data. Every row can contain one or more values, which is separated by a comma.

Power BI Desktop (.pbi) You can use Power BI Desktop to query and load data from external data sources.

Databases in the Cloud It allows you to connect live to Azure SQL Database, Azure SQL Data Warehouse, etc.

Databases on-premises You can connect directly to SQL Server Analysis Services Relational model databases. A Power BI Enterprise Gateway is required.

Key terms used in Power BI

Term Description

Visualization A visual display of information to achieve one or more objective. It offers a single-screen display of information. It alerts users on issues or problems Operational, Performance, Personal, etc.

Datasets A dataset is something which you import or connect to. Datasets can be renamed, refreshed, removes, and explored.

Dashboard The dashboard is a collection which contains zero or more tiles and widgets. It is used to represent a customized view of some subset of the underlying datasets.

A Power BI report is one or multiple pages of visualizations. It can be created from scratch, imported to a dashboard, and created using datasets.

Tile It a single visualization found in a report or on a rectangular dashboard box which contains each visual.

History of Power BI

Power BI was conceptualized by Ruler and Dhers Netz of the SQL server coverage services team at Microsoft.

It was designed by West Chadic George in the year 2010 and named as a Project Crescent.

In 2011, It was bundled with SQL Server Codenamed Mount McKinley.

Microsoft unveiled the first preview to Power BI in September 2014.

The first version of Power BI released on 24 July 2024. It was based on Excel Based Add-ins like Power Query, Pivot, view, and Map.

Architecture of Power BI

Architecture of Power BI

Data Integration:

An organization needs to work with data which comes from different sources which can be in various file formats. The data should be extracted from a different source which can be from different servers or databases. This data is integrated into one standard format in a common staging area.

Data Processing:

In this stage, the integrated data is still not prepared for visualization as the data needs processing. This data is pre-processed. For example, redundant values, missing values will be removed from the data set.

The business rule should be applied to the data when the data is cleaned. You can load that data back to Data Warehouse.

Data Presentation

Once the data is loaded and processed, it can be visualized much better with use of various visualization that Power Bi has to offer. Use of dashboard and report helps one represent data more intuitively. This visual report helps business end users to take business decision based on the insights.

Install and run Power BI Desktop

And select Download free button

Step 2) You will be redirected to a Microsoft store and select Get button.

Step 4) You can see progress status on the screen.

Step 5) Before welcome screen you will ask to register to enter.

Step 6) When you run Power BI Desktop, a Welcome screen is displayed.

Power BI Dashboard

Below figure demonstrates the Power BI Dashboard:

Power Bl Desktop is an enlargement tool used to generate dashboards and reports. Power Bl applications will be accessed by other users through desktop and mobile devices.

There are Power BI components are 5 main components released in the market.

Power Query: It can be used to search, access, and transform public and/ or internal data sources.

Power Pivot: It is used for data modeling for in-memory analytics.

Power View: This component allows you to visualize, analyze, and display data.

Power Map: It brings data to life with interactive geographical visualization.

Power BI Service: It allows you to share data views and workbooks, which you can refresh from on-premises and cloud-based data sources.

Power BI Q&A: It allows you to ask questions and get immediate answers using a natural language query.

Data Management Gateway: It offers periodic data refreshers, view data feeds, expose tables.

Data Catalog: This component allows the user to discover and reuse queries using the Data Catalog. Metadata can be facilitated for search functionality.

What is Dax Function?

DAX is a formula expression language which is called (DAX) which can be used with various visualization tools like Power BI. It is also known as a functional language, where the full code is kept inside a function. DAX programming formulas contain two data types: Numeric and Other.

Each is linked to the other by having common columns. Here is a simple diagram of Power BI Dashboard Example showing the relationships.

There are 3 things in Power BI where you can use


Calculated Columns



Let’s see all these Power BI examples and see how DAX functions works.

Calculated Columns

Calculated column allows you to create new columns based on the given data.

For example, there is no ‘ Final price’ column available in the Items table. Here, the DAX function is used to calculate a new column when only total price & quantity are available.

Price = List_Items[MRP]*List_Items[Qty]

In the data shown in above Power BI example, each row will now have the respective calculated price.


You can perform a calculation using measure without the need to add any data as shown in the below Power BI example. This is very helpful for reports where the price can be displayed, without requiring an entirely new column to store it.


1] Total of the MRP column * Total of Qty column


DAX functions in tabular model return entire tables. For example, In order to generate a list of all the country the organization has clients in, use the function:

cities touched = DISTINCT(Customers[City])

A word on Filters

Filters hide rows that don’t fit given criteria. A calculation after filtering out data will be applicable only on a row which matches those criteria.

Power BI DAX Functions

Some Important DAX functions are:


This DAX function allows you to find the average from a given set of values as shown in the below Power BI example.

Example –

AvgComm = AVERAGE(List_Items[Price])


Helps you to find the maximum from a given set of values.

Example – Find out the highest order.

HighSale = MAX(List_Items[Price])


Helps you to find the minimum set of values.

Example – Allows you to find out the lowest order.

LowestSale = MIN(List_Items[Price])


Count any umerical data.

Example – Count number of ticket issued.

TicketVolume = COUNT(Invoices[Ticket])


This function helps you to join values in calculated columns. You can use ConcatenateX if using in measures.

Example – Concatenate the Item names, and MRPs will give a unique code for all the price points at which each product is sold.

ProMrp = CONCATENATE(List_Items[Item],List_Items[MRP])


The function allows you to calculate the sum from the start of the current Year to the specified date. It performs calculate base on a calendar year, not a financial year.

Example – Calculate Sales totals for the price column.

CumiSales = TOTALYTD(SUM(List_Items[Price]),Invoices[Date])


Returns everything. Ignores filters.

Example – Used with the calculate function above.

Power BI vs. Tableau

Here, are major differences between Power BI vs. Tableau:

Parameters Power BI Tableau

Year of establishment 2013 2003

Application Complete Dashboards for analysis Allow Ad Hoc Analysis

Use by Technical or Non Technical users Only use by Analysts

Support Very limited Full support

Scalability Good Excellent

Infrastructure SaaS Flexible

Who uses Power BI?

Here, are an important professional who uses Power BI tool:

PMO – Project and Portfolio Manager

Business & Data Analyst

Developer & Database Administrator

IT Team, IT Professional

Consumer for End User Report

Data Scientist

Advantages of Power BI

Offers pre-built dashboards and reports for SaaS Solutions

Provide real-time dashboard updates.

Secure and reliable connection to your data sources in the cloud or on-premises

Power BI offers quick deployment, hybrid configuration, and a secure environment.

Data exploration using natural language query.

Feature for dashboard visualization

New features frequently added that are great for excel users.

Extensive database connectivity capabilities Q&A feature publish to the web.

integration with both Python and R coding to use visualizations.

Power Query provides many options related to wrangling and clean the data.

Post publishing the data into Power BI web service can schedule refresh without manual intervention.

Power BI backed by the superpower of with artificial intelligence and machine learning.

Here, are Cons/drawbacks of using Power BI:

Dashboards and reports only shared with users having the same email domains.

Power Bl will not mix imported data, which is accessed from real-time connections.

Power Bi will not accept the file larger than 250MB and zip file which compressed by the data of X-velocity in-memory database.

Power BI can’t accept file size larger than 1 GB.

Dashboards never accept or pass user, account, or other entity parameters.

Very few data sources that permit real-time connections to Power BI reports and dashboard.


BI helps you to analyze data and to provide actionable information which helps corporate executives, business managers to make informed business decisions.

Power BI is a Business intelligence and Data Visualization tool which helps you to convert data from a various data source

Some important Power BI tools are 1) Power BI Desktop 2) Power BI service 3) Power BI Data Gateway 4) Power BI Report Server 5) Power BI Mobile Apps

Excel (.xlsx, xlxm), Comma Separated Value (.csv), Power BI Desktop (.pbi), Databases in the Cloud, Databases on-premises are important data sources used in Power BI.

Visualization, Datasets, Dashboard, Reports, Tile are important terms used in a Power BI.

Power BI was conceptualized by Ruler and Dhers Netz of the SQL server coverage services team at Microsoft.

1) Data Integration 2) Data Processing 3) Data Presentation are important components of Power BI architecture.

Power Query, Power Pivot, Power View, Power Map, Power BI Service, Power BI Q&A, Data Management Gateway, Data Catalog are important elements of Power BI Dashboard.

DAX is a formula expression language which is called (DAX) which can be used with various visualization tools like Power BI.

Power BI offers Complete Dashboards for analysis, while Tableau only allows Ad Hoc Analysis.

Important professional who uses Power BI is PMO – Project and Portfolio Manager, Business & Data Analyst, IT Team, IT Professional, etc.

The biggest drawback for Power Bi will not accept the file larger than 250MB and zip file which compressed by the data of X-velocity in-memory database.

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Comparing Calculated Columns And Dax Measures In Power Bi

In this tutorial, I will cover the two places where you can write your DAX formulas. These two places are the calculated columns and measures. You may watch the full video of this tutorial at the bottom of this blog.

I will go over each one at a time and I’ll start with calculated columns.

A calculated column is an additional column that doesn’t exist in your raw data source.

This means that we need to add it physically to your data table.

To do this, you put some DAX formula logic into a column to create that additional column. This is very similar to working in Excel and you want to add another column with a formula.

In this example, we will use this fact table that contains all the sales that we’re making in our retail stores and we will add the price of the product.

The price actually already exists in the Products Table here, where we have the Original Sales Price and Current Price.

But to show you how to create a calculated column, I’m also going to add this to the Sales Table.

In a lot of these examples, especially with calculated columns, you don’t actually need to create these columns.

If you’re coming from an Excel background, then you might think you have to, but you don’t have to in Power BI. I’m only doing this to show you what a calculated column is.

But later on, I’m going to show you how you can actually use measures to run these calculations versus adding a physical column inside the data table.

So let’s add the Price here just as our first example.

To create a calculated column, open the Modeling ribbon and select New Column.

I’m going to write some pretty simple logic here to get the Price into this column. I’ll call it Sales Price and then use the RELATED function to reference a column name.

In this case, I’m going to reference the Current Price column. That’s going to give me a sales price for every single item that was sold.

The RELATED function is bringing in the price of each individual product.

And then we could write a new column here and call it as Total Revenue. We use the formula:

Total Revenue = Sales[Quantity] * Sales[Sales Price]

These are just some examples of how you can create a calculated column.

You can also create calculated columns in any table in your data model. It doesn’t have to be just the fact table or the sales table. It can be inside of your LOOKUP Tables as well.

For example, we jump to this detailed Dates Table. Think of these columns as the columns that are going to be filters of your DAX measures.

As I look at this table, I see that there is a dimension here that does not exist currently, which I might want to put into some of my visualisations.

To showcase another way of creating a calculated column, I will use the MonthName column.

The MonthName here is the full month, but I only want the first three letters of each month.

So I’m gonna go to New Column in the Modeling ribbon and call this column as Short Month.

I’m gonna use some logic that you might be familiar with from Excel. I’m going to use LEFT, then find my month name, and I’m going to only use the first three letters of that month name.

Now if we go across to the side, we will see the Short Month column, where we only have the first three letters of each month.

I like to call this adding additional dimensions to analysis because we essentially created another filter that we can use throughout any of our analysis that we do from here on out.

If we go back to the data model, you will see that the Short Month column now exists in our Dates Table and it can filter anything that we do down inside this Sales table.

So if we will run a calculation and count up the quantity, we can now filter it by the Short Month.

I would like to reiterate that it is not recommended that you create these columns in here because we can actually create all of these calculations in memory. 

Through creating measures, we can do these internal calculations without having to put them physically inside the table.

That’s a key thing to remember as you learn how to write DAX formula on top of your data tables.

Now let’s talk about measures.

Think about measure as a virtual calculation. It doesn’t actually sit inside your model, but it sits on top of your model.

When you use a measure, it only goes and does a calculation at the time that you use it.

In Excel, every time you run a calculation in the column or in any cell, it recalculates all the time. But in Power BI, a measure only calculates itself when it needs to.

A measure is like a stored calculation procedure that only gets enabled if you use it in a visualization.

So let’s create a simple measure to highlight that point. First, I’m going to select the Sales Table and then select any column in there.

I’ll put in Total Quantity Sold to get the sum of the Quantity column in the Sales table.

Now we have this really simple measure, and it is virtually completing its calculation.

It is also calculating everything in memory. In other words, this is calculating the total items that we have sold throughout the time.

The key thing to remember here is that this measure is just stored inside our model, but it doesn’t actually go and run any calculation, unless we drag it on our report page. Then it will go and run the calculation virtually.

So in this case, this measure is virtually going to the Sales table, going to the Quantity column in that table, and then doing a sum over that entire column.

This is actually called an aggregation measure, which we’ll be going over shortly.

Now I’m going to create a new measure and I’m going to call this as Total Sales. Then I’ll use the iterating function called SUMX, which I’ll explain in another model shortly.

I referenced the Sales table, and come up with this formula:

Total Sales = SUMX( Sales, Sales[Quantity] * RELATED( Products[Current Price] )

If you remember, we didn’t physically put this RELATED current price inside the data table. But in this case, I’m virtually putting it inside the data table by incorporating it in this measure.

Then the iterating function SUMX goes to the Sales table and picks every single row in the table multiplied by the quantity by the related current price.

This Total Sales will now give me a result.

You can also do the formatting in the Modeling tab, where your Data type is at the top.

We went over calculated columns and measures, where you can write your DAX formula.

The key thing with calculated columns is that you are physically putting a column of data into your model. If you do that sometimes on some of your larger tables, those can be very large columns.

It is important to recognize that these calculated columns can take up a lot of memory in your model.

They can make your file size larger, and they can sometimes impact performance depending on how big the table is.

But you can counteract this by using measures effectively to run a lot of these calculations virtually. You will still get the same results that you would get by writing these calculated columns.

I hope that this tutorial makes it a lot clearer for you the two places where you can write your DAX formula in and the considerations when writing DAX formulas.

This will also help you understand how to incorporate DAX into your analysis within Power BI.

Enjoy reviewing this one.


Creating A Pareto Chart In Power Bi – Advanced Dax

I’ll show you how to use a combination of formulas to be able to generate a visualization like this one.

The first thing that I always recommend when working on something similar with this analysis is to turn the visualization into a table. This way, you can really examine the numbers that are needed to generate the said visualization.

For this scenario, you first need to create a cumulative total to get the Total Revenue amount. As you can see, there are no dates and numbers. Therefore, it will be challenging to generate a cumulative total that’s based on text value instead of numbers. I’ll teach you how to do this step by step.

The first thing that I’m going to show you is the calculation for cumulative total without dates.

The technique here is to use the SUMX function as well as an iterator like the FILTER function. The FILTER function goes inside the SUMX. After that, you need to create a virtual table using the SUMMARIZE function.

The virtual table needs to look at all the sales inside the selected date context. After that, it should go through the specific state codes inside the table.

The said part of the formula will create a table exactly like the sample table. The only difference is it’s creating the table virtually for now.

The next part of the formula will create another virtual table for the revenue. But because this logic is inside a filter, it manipulates the table virtually to create a cumulative total. The formula should iterate through every row and part of the total revenue table.

If the revenue of the specific row is greater than or equal to the state revenue, it calculates the revenue amount and brings it into the table.  

After that, you need to add the variable, VAR StateRev, so that it’s going to evaluate the Total Revenue.

That’s how you create a cumulative total even though there’s no ranking based on dates or numbers. The cumulative totals that were created are only based on a text column.

All the cumulative totals in the table are calculated because they all evaluated to true, and then the total revenues are aggregated to the cumulative total.

The next part that I’d like to demonstrate is the creation of the sales table for the pareto chart.

In addition to this, you can create a Pareto chart based on any selection through the Product Category selector.

The next thing that I want to show you is how to combine the previous formulas into another one by simply adding some DAX functions.

To do that, just create a new measure or copy an existing (similar) one, then rename it accordingly. For this example, the new measure is named as Pareto Chart (States).

As you can see, there’s already an existing formula in the copied measure so you only need to add some DAX formulas. In this case, you need to add some variables using the VARIABLE function, which simplifies calculations.

The first variable (VAR StateRey) simply equals to Total Revenue, while the second one (VAR AllSales) uses the CALCULATE and ALLSELECTED functions.

For the logic, add the DIVIDE function before the SUMX, and then add the variable, AllSales on the bottom part. It’s important to have a constant value for the results to return. Thus, you should put zero as the alternative result.

There are times when the results appear in a number format, but the Pareto Chart (States) column should show in percentage like this one below.

Thus, you need to go to Measure tools, and then change the format into a percentage.

Once you go back to the table, you can see the exact results in the table transform into a Pareto chart. That’s the reason why I always recommend having it inside a table first before creating the chart.

You can experiment around the chart and select dynamic data in the Product Category selector. As you can see, the Pareto chart also changes dynamically as per your product categories.

You can also add some colors to your Pareto chart using some data labels to make it more compelling. Just take time to play around the formatting area and set up your Pareto chart accordingly. For instance, you can change the visualization and enable the Line Values like this one below.

There’s so much you can do inside Power BI to make your data stand out and your report more effective and visually appealing.

The key is understanding the right time to use variables in the evaluation. It could either be used at the start of the formula or as an iterating function. 

I hope that you got something out of this tutorial and hopefully you can find a way to utilize the sample formula pattern into your own models.

All the best!


Power Bi Incremental Refresh: What Is It And How To Use It

Power BI has evolved into a valuable tool for businesses working with data, and incremental refresh is a key feature in Power BI that can make your job easier by dynamically updating your data.

Power BI incremental refresh is a process that allows users to refresh data progressively instead of doing a complete data refresh every time. It can significantly speed up data refreshing and reduce the amount of data that has to be transferred between Power BI and the data source.

In this article, we’ll show you how to set up incremental refresh in Power BI like a pro, making your data analysis workflow more agile and allowing you to make informed decisions in real-time with up-to-date data at your fingertips.

Let’s go!

Power BI incremental refresh is a very handy tool, and understanding its fundamentals is essential if you want to leverage it for efficient data refresh processes for your reports and dashboards.

These fundamentals include:

Incremental refresh is a feature in Power BI that allows you to load only a new or updated data model, improving the efficiency of your data refresh process.

By using Power Query date/time parameters with the reserved, case-sensitive names RangeStart and RangeEnd, you can filter table data and dynamically partition it based on incremental range to separate frequently refreshed data from less-frequently refreshed data.

If you are interested in implementing an incremental refresh process, follow these steps:

In the Power BI desktop, select the table with custom filters applied.

In the Incremental Refresh window that opens, you can set various options and define the policy.

The policy you define in the Power BI Desktop will be applied to Power BI Service.

The filter logic used in incremental refresh is based on date ranges. You should consider the following factors when defining your filter logic:

Be mindful of your data source’s historical data retention policies.

Make sure the data source supports query folding and incremental refreshes.

Determine the optimal range for your data table to minimize load time.

Using incremental refresh with dataflows requires some additional considerations:

Firstly, your dataflow must reside in a workspace with Premium capacity.

Secondly, Power Apps per-app or per-user plans are required for an incremental refresh in Power Apps.

Finally, the dataflow must use Azure Data Lake Storage as the destination.

Incremental refresh in Power BI can save you time and improve the efficiency of your data updates.

Now that we’ve covered the fundamentals of Power BI incremental refresh, let’s take a look at a detailed explanation of how to set up incremental refresh in Power BI in the next section. 

Setting up incremental refresh in Power BI is a straightforward process. You start by enabling the feature in the Power Query Editor, then specifying the desired table for refresh, and finally defining your storage and refresh policies.

Below is a detailed guide on how to set up Power BI incremental refresh:

To set up Incremental Refresh in Power BI, you must first create date/time parameters using Power Query. These parameters will help you define the range of data to be incrementally refreshed.

For a successful incremental refresh in your desired incremental range, the following two Power Query date/time parameters must be created with their reserved, case-sensitive names:

RangeStart: This parameter represents the start date of the data range that you want to include in the incremental refresh.

With these parameters created, you can now apply the date range filter to your data set.

Once you have both RangeStart and RangeEnd parameters set up, you can configure the Incremental Refresh policy:

In the Incremental Refresh settings window, turn on the Incrementally refresh this table slider.

Set up the storage policy by defining:

The number of days/years to store data in the Power BI service.

A refresh policy to determine the frequency of incremental refreshes.

By following these steps, you can set up incremental refresh in Power BI and optimize the refresh process for large datasets. It’s that simple!

Now that we have that covered, we look at how you can work with data and filters while using Power BI incremental refresh in the next section.

Knowing how to work with data and filters in Power BI is useful if you have a large dataset that doesn’t change very often but you still want to see the latest data regularly.

Here is how you can do so:

When working with Power BI’s incremental refresh, it’s essential to understand the different filters that work on date/time and the integer data type.

Using date/time parameters with the reserved, case-sensitive names RangeStart and RangeEnd, you can effectively filter table data based on dates.

For example, in a fact table that contains data since 2005, you can set up Incremental Refresh to keep only the last ten years, filtering out data before 2010.

In the case of integer data types, you can set up custom filters to manage incremental refresh. This helps partition and separate the data that needs frequent refreshing from the data that doesn’t require as much updating.

Surrogate keys play a crucial role when working with incremental refresh in Power BI. These unique keys help in managing and controlling your table data.

When you use surrogate keys during the configuration process, you can easily keep track of any changes or updates in your data while ensuring that the primary key is not altered.

This enables efficient and effective incremental refreshes while maintaining the integrity of your data.

Besides date/time and integer data types, you can also create and utilize custom filters in incremental refresh. Custom filters help you better manage and partition the data based on specific needs or criteria.

Here’s a step-by-step guide for creating a custom filter:

Create two parameters of Date/Time data type, with the names of RangeStart and RangeEnd. Set a default value for each parameter.

Implement the custom filter function in your query or data processing steps to filter the data based on your specific requirements.

Using custom filters, you can efficiently organize and manage the data in your Power BI solution, ensuring optimal performance and usability.

Be mindful of the incremental refresh settings to avoid falling into traps or facing issues that might hamper your overall experience.

After understanding how to work with data and filters in Power BI, let’s delve into how you can enhance the performance and efficiency of your reports and dashboards by leveraging incremental refresh in the next section.

There are different ways to improve performance and efficiency while using incremental refresh in Power BI. We will take a look at some of them below.

When you configure incremental refresh in Power BI, your table is automatically partitioned. One partition contains data that needs to be refreshed frequently, while the other partition holds rows that are not changing.

This improves the performance and efficiency of the refresh process. Query folding also plays a part in this process; it combines multiple steps of a query into a single database query, reducing the amount of processing and time required for report updates.

Power BI Premium allows you to work with large datasets more efficiently using storage formats such as Columnstore and Aggregations.

When processing large amounts of data, these storage formats speed up the refresh performance and enable you to interact with your reports faster.

Columnstore: This format enhances the performance of read-intensive queries due to its columnar storage and compression capabilities.

Aggregations: This feature ensures that many queries can be answered by the aggregated tables, reducing the need to access large fact tables and thus improving performance.

Using these storage formats while working with Power BI Premium will make managing large Power BI datasets more efficient.

Monitoring and optimizing resource consumption are crucial for maintaining the performance of your Power BI system. Some ways you can do this are:

Track Memory Usage: Analyze the PeakMemory metric during dataset refresh operations to understand the maximum memory consumed. Monitoring this value helps you identify any bottlenecks and optimize memory usage.

Monitor CPU Usage: Keep an eye on the MashupCPUTime, which indicates the total CPU time consumed by Power Query Engine for all queries. This insight allows you to optimize your queries and minimize consumption.

You can improve your Power BI system’s overall performance and efficiency by implementing the things discussed above and leveraging Power BI features like partitioning, query folding, and large dataset storage formats.

Next, we’ll examine how to configure incremental refresh using different sources.

You can use Power BI Incremental Refresh with different data sources, including SQL Database, DirectQuery and Import Data Modes, and Data Warehouses.

Here are some things to consider when selecting a data source to import into Power BI:

When working with SQL databases as your data source, remember to:

Create Power Query date/time parameters with reserved case-sensitive names: RangeStart and RangeEnd.

Apply filters on the data using these parameters to separate frequently and less frequently refreshed data.

Define an incremental refresh policy in Power BI Desktop before publishing to the Power BI service.

Keep in mind that Incremental refresh is only supported on Power BI Pro, Power BI Embedded datasets, and Premium per-user plan environments.

Power BI offers two data access modes: DirectQuery and Import Data.

Using Incremental Refresh with these modes involves the following:

DirectQuery: In the Direct Query mode, you query data from the data source in real-time, which means no data is imported into your Power BI dataset. You can only use Incremental Refresh with Direct Query if you have a Power BI Premium, Power BI Embedded datasets, or Premium per user plan.

Import Data: In this mode, you import data into your Power BI dataset, and you can then work with the imported data. To use Incremental Refresh with Import Data mode, configure RangeStart and RangeEnd parameters, apply filters, and define an incremental refresh policy.

Using Incremental Refresh with data warehouses is similar to using SQL databases. Follow the same steps to create RangeStart and RangeEnd parameters, apply filters to separate data based on refresh frequency, and define an incremental refresh policy before publishing your dataset to the Power BI service.

Ensure you understand your Power BI environment’s data access modes and limitations for utilizing incremental refresh properly with different data sources.

Properly configured incremental refreshes can improve data storage efficiency and ensure the most up-to-date information is available for your Power BI reports.

These tips and tricks will help you get the most out of your data and make sure that you are using incremental refresh correctly.

When working with Power BI, it’s essential to configure incremental refresh and real-time data efficiently for optimal performance.

Incremental refresh enables you to specifically refresh new or modified data instead of the entire dataset. When paired with real-time data, you can efficiently update your report with the latest information.

To leverage such capabilities, configure either through Power BI Desktop or tools such as the Tabular Model Scripting Language (TMSL) or Tabular Object Model (TOM) via the XMLA endpoint.

Converting Date/Time values to integers makes it easier to work with the data and optimize refresh efficiency. Here are the steps to convert Date/Time to Integer in Power Query:

Open Power Query Editor in Power BI.

Select the column containing the Date/Time values.

Choose Whole Number as the new data type.

This conversion promotes better performance by reducing the strain on data processing during incremental refreshes.

3. Only Refresh Complete Days

It is crucial only to refresh complete days when configuring incremental refresh policies, as it prevents unnecessary data processing and reduces loading times for reports.

To achieve this, follow these steps:

In the Incremental Refresh window, define a filter, such as Date_IS_AFTER = Date.IsAfterOrEqual([Date], DateTimeZone.SwitchZone(DateTime.LocalNow(), -TimeZoneOffset))

Configure the ‘rangeStart‘ and ‘rangeEnd‘ parameters to use only complete days. For example, set the ‘rangeEnd‘ to the previous day’s date.

And there we have it! We’ve unraveled the mystery of Power BI Incremental Refresh. It’s not just some tech jargon but a game-changer that can make handling massive datasets a breeze while saving you some serious time and resources.

So go ahead, give it a spin, and experience how it brings efficiency to your data updates. Remember, in the world of data, staying updated is staying ahead!

If you want to learn more about Power BI, you can watch the video below:

Power Bi Challenges Round 17

In the 16th round of our Power BI Challenges, we looked into something that every organization needs — consultancy time and earnings analysis. This time, we’re going to look into something even deeper; something that affects the whole world.

In our 17th round, we’re going to look into real environmental data that experts around the world are also currently studying.

As we dive deep into this dataset, we’re hopefully contributing to the global push in finding better ways to solve global environmental issues.

We’ve always marveled at how dynamic Power BI can be as a tool, seeing that our Power BI challenges continue to cover various topics. This, we believe, cements that as a fact seeing that we’re going to deal with something that actually impacts the whole world.

Brian is standing in for Haroon, who’s been feeling a bit under the weather. As part of the challenge, participants should include four environmental indicators that will make the most impact on the specific region in focus.

The report that participants will be working on should be focused on three target audiences – regional data scientists, upper-level managers, and policy makers. These end users will have different goals, so it’s critical that each report addresses their unique needs.

Brian also added a few more things to consider to guide participants as they work on their reports.

Deadline for submission is on December 12.

It’s always been fun witnessing the friendly competition in the Enterprise DNA Forum because of the Power BI challenges. But more than that, it’s been inspiring seeing how participants improve from one challenge to the next. This is a strong testament to the fact that these challenges are an effective learning tool that can turn beginners into experts over time.

Winners get amazing prizes, adding even more value to the entire experience.

Winning members get a free membership that they can pass on to someone who they believe will greatly benefit from the resources and events that are offered exclusively to members. We will also choose 3 non-member winners who will be getting complimentary memberships for 1 year.

First-time participant winners take home the best prizes, giving even more reasons for those who are still thinking twice about participating to finally jump in and join the fun. They get to choose one item from this list:

A copy of the Definitive Guide to DAX, 2nd Edition (what we consider to be our “DAX bible”) or any book in the EDNA Forum Recommended List

A copy of SnagIt 2023, a must-have capture and graphics tool that a lot of our Enterprise DNA experts use

A four-month subscription to  chúng tôi , an online source for downloadable and editable icons that can make your Power BI reports stand out

These challenges also serve as a great way for your work to be featured in the Power BI Challenge Showcase.

How do we pick the winners? We focus on the 4 pillars of a great Power BI report:

Data loading and transformation

Data modeling

DAX calculations

Reports and visualizations

As long as you have these 4 areas covered, you’ll have a good chance of emerging as a winner.

Here’s how you can join the challenge.

Download the data set from the forum.

Post the screenshot of your report and explanation in LinkedIn along with the name of the challenge (e.g. Power BI Challenge 17 – Environmental Data Reporting)

Include this in your post: I accepted the #EnterpriseDNAPowerBIChallenge and hyperlink the challenge post from the Forum.

Submit your PBIX files to 

[email protected]

Start working on those reports now and we hope to see your name among the list of winners.

All the best,

Enterprise DNA Team

Power Bi Scheduled Refresh Greyed Out

Power BI Scheduled Refresh Greyed Out [Solved] Top fixes for Power BI greyed scheduled refresh grey out






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In Power BI, the scheduled refresh attribute allows users to refresh the data in their datasets on a recurring schedule. However, the process can be interrupted, causing Power BI Scheduled Refresh to be greyed out.

Readers can check out our guide on how to refresh data in Power BI to assist with a manual refresh in Power BI Desktop.

Why is the scheduled refresh option greyed out in Power BI?

Users can encounter Power BI scheduled refresh greyed-out errors because of issues with the Power BI account or installation. Hence, it prompts Power BI processes to run into issues like being greyed out.

Likewise, other factors can cause the issue. Some are:

Dataset not connected to data source – The dataset not connected to a data source that supports scheduled refresh can cause the problem.

DirectQuery connection does not support scheduled refresh – The issue can occur if the dataset you are trying to refresh depends on a DirectQuery not supported by Scheduled refresh.

Dataset does not support scheduled refresh – If the dataset you are trying to refresh is dependent on a live connection to a data source, it may not support Scheduled refresh. Hence, it can result in an issue.

Scheduled refresh is disabled on other files – The dataset you try to refresh may rely on an Excel workbook or CSV file stored on OneDrive or SharePoint Online. So, the refresh schedule may be disabled for the file type.

However, these reasons can differ on Power BI accounts on different PC.

How do I enable scheduled refresh in Power BI?

Apply the following preliminary checks:

Fix Network congestion issues.

Make sure the dataset is structured correctly.

These will prepare you from encountering errors when following the steps stated below.

1. Make changes from Options and settings

You must have permission to connect to the data source and permission to publish the report to the workspace where you are publishing it. Read about what to do if you don’t have permission to open files in Windows.

2. Create a new schedule refresh plan

Creating a new schedule refresh plan allows you to enable Power BI Schedule Refresh.

You can check how to add a report to a dashboard in Power BI for more details on mastering the use of Power BI.

You may also discover how to bypass Power BI sign-in issues and the best browsers that support Power BI on all platforms on your PC.

Still experiencing issues?

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