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There is still a lot of confusion about the difference between SUM vs SUMX in Power BI. This is key knowledge that users have to master because both functions can be used across different scenarios, but there are cases where one is more efficient than the other. You may watch the full video of this tutorial at the bottom of this blog.

I’m going to focus on one example here that would show the distinction between the two. But before I jump into that example, it is important to understand the difference between an aggregating function and an iterating function.

When it comes to DAX, there are two types of calculation engines – the aggregators and iterators.

Aggregating functions include SUM, AVERAGE, MIN, MAX and COUNT. Iterators, on the other hand, are functions that have an X at the end, like SUMX.

Iterating functions go through every single row of a table to add logic to each of these rows.

Aggregating functions look at the entire column left over after the context is placed in a formula. From there, a single aggregation is done for the entire column at a single time.

How is SUM used as an aggregator?

In this example, I’m going to compute for the Total Revenue in the sample data given.

The context is always important here. In this case, each specific date is the context of each specific result.

If I dig deeper into this table, it will show that there is a direct relationship flowing from the Date going into the Sales table.

Then if I look at the data working underneath this model, this is how everything fits together.

So the relationship is linked to the Order Date column here. Once specific dates from this column are filtered, the corresponding results are shown under the Revenue column.

From there, the SUM would just do one big calculation of the filtered results.

Now, I’m going to use SUMX on the same sample data so that you can see the difference. I can actually calculate for that Revenue without touching the Revenue column.

When the SUMX function is used, it will always ask for a table. Note that either a physical table or a virtual table can be used here.

To come up with the Revenue, I’m going to choose the Sales table. Then, I’ll place an expression, which can be a measure or a specific column from that table into this formula so that it can start running logic on every row. The expression, as explained here, returns the sum of an expression evaluated for each row of the table.

Since the sample data includes the Order Quantity, I’m going to use that here to get the Total. I’m also going to use the Unit Price.

Once I drag that formula into the report, the results are exactly the same.

Of course, they’re both showing the same results because they are both deriving data from the same two columns – the Order Quantity and the Unit Price.

Why use the SUMX if it yields the same result as the SUM anyway?

With the SUMX, the logic is applied not just to an entire column, but to every single row within that column. In fact, I could delete the Revenue column and still be able to retrieve specific results.

So imagine that logic being applied at every row. It multiplies the Order Quantity and Unit Price for the 1st row then saves that into the memory. It does the same thing to the 2nd row and all the other rows after that, saving each individual result.

This means that at the end, what’s being used to calculate the SUMX is not the physical data on the table, but the results saved in the memory.

Hopefully I was able to explain the main difference between SUM vs SUMX in Power BI, especially to those who are still getting the hang of what Power BI can really do.

SUMX will also be useful in cases where you have thousands to millions of rows. As long as the tables and columns referenced in your measures are there, using iterating functions would make the process more efficient.

All the best,

Sam

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Difference Between Hadoop Vs Redshift

Difference between Hadoop and Redshift

Hadoop is an open-source framework developed by Apache Software Foundation with its main benefits of scalability, reliability, and distributed computing. Data processing, Storage, Access, and Security are several types of features available in the Hadoop Ecosystem. HDFS has a high throughput which means being able to handle large amounts of data with parallel processing capability. Redshift is a cloud hosting web service developed by the Amazon Web Services unit within chúng tôi Inc., Out of the existing services provided by Amazon. It is used to design a large-scale data warehouse in the cloud. Redshift is a petabyte-scale data warehouse service that is fully managed and cost-effective to operate on large datasets.

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Hadoop, Data Science, Statistics & others

Hadoop HDFS has high fault tolerance capability and was designed to run on low-cost hardware systems. Hadoop can handle a minimum type size of TeraBytes to GigaBytes of files within its system. HDFS is master-slave architecture consisting of Name Nodes and Data Nodes where the Name Node contains metadata and Data Node contains real data to be processed or operated.

RedShift uses different data loading techniques such as BI (Business Intelligence) reporting, analytical tools, and data mining. Redshift provides a console to create and manage Amazon Redshift clusters. The core component of the Redshift Data Warehouse is a cluster.

Image Source: Apache.org

RedShift Architecture:

Below is the top 10 comparisons between Hadoop and Redshift are as follows.

Key Differences Between Hadoop vs Redshift

Below is the Key Differences between  Hadoop vs  Redshift are as Follows

1. The Hadoop HDFS (Hadoop Distributed File System) Architecture is having Name Nodes and Data Nodes, whereas Redshift has Leader Node and Compute Nodes where Compute nodes will be partitioned as Slices.

2. Hadoop provides a command-line interface to interact with file systems whereas RedShift has a Management console to interact with Amazon storage services such as S3, DynamoDB etc.,

3. The database operations are to be configured by developers. Redshift automates the database operations by parsing the execution plans.

5. In terms of Hadoop architectural design, network, storage, security, and performance have been considered primary elements whereas in Redshift these elements can be easily and flexibly configured using Amazon cloud management console.

6. Hadoop is a File System architecture based on Java Application Programming Interfaces (API) whereas Redshift is based on a Relational model Database Management System (RDBMS).

8. Most of the existing companies are still using Hadoop whereas new customers are choosing RedShift.

9. In terms of, performance Hadoop always lacks behind and Redshift always wins over in the case of query execution on large volumes of data.

10. Hadoop uses Map Reduce programming model for running jobs. Amazon Redshift uses Amazon’s Elastic Map Reduce.

11. Hadoop uses Map Reduce programming model for running jobs. Amazon Redshift uses Amazon’s Elastic Map Reduce.

12. Hadoop is preferable to run batch jobs daily that becomes cheaper whereas Redshift comes out cheaper in the case of Online Analytical Processing (OLAP) technology that exists behind many Business Intelligence tools.

14. In terms of Data Loading too, Hadoop has been behind Redshift in terms of hours taken by the system to load data from the storage into its file processing system.

15. Hadoop can be used for low-cost storage, data archiving, data lakes, data warehousing and data analytics whereas Redshift comes under Data warehouse capabilities causing to limiting the multi-purpose usage.

16. Hadoop platform provides support to various external vendors and its own Apache projects such as Storm, Spark, Kafka, Solr, etc., and on the other side Redshift has limited integration support with its only Amazon products

Hadoop vs Redshift Comparison Table

BASIS FOR

COMPARISON

Availability Open Source Framework by Apache Projects Priced Services provided by Amazon

Implementation Provided by Hortonworks and Cloudera providers etc., Developed and provided by Amazon

Performance Hadoop MapReduce jobs are slower Redshift performs more faster than Hadoop cluster

Scalability Limitations in scalability Easily be down/upsized as per requirement

Pricing Costs \$ 200 per month to run queries The price depends on the region of the server and is cheaper than Hadoop

Eg: \$20/month

Speed Faster but slower compared to Redshift 10 times faster than Hadoop

Query Speed Takes 1491 seconds to run 1.2TB of data 155 seconds to run 1.2TB data

Data Integration Flexible with the local file system and any database Can load data from Amazon S3 or DynamoDB only

Data Format All data formats are supported Strict in data formats such as CSV file formats

Ease of Use Complex and trickier to handle administration activities Automated backup and data warehouse administration

Conclusion

The final statement to conclude the big winner in this comparison is Redshift that wins in terms of ease of operations, maintenance, and productivity whereas Hadoop lacks in terms of performance scalability and the services cost with the only benefit of easy integration with third-party tools and products. Redshift has been recently evolving with tremendous growth and acceptance by many customers and clients due to its high availability and less cost of operations compared to Hadoop makes it more and more popular. But, till now most of the existing Fortune 1000 companies have been using Hadoop platforms in its architectures to manage the customer data.

In most the cases RedShift has been the best choice to consider for the business purposes by any client or customer in order to handle the large and sensitive data of any financial institutions or public information with more data integrity and security.

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Small Multiples Chart In Power Bi: An Overview

In this tutorial, we’ll talk about the small multiples chart, which is a new preview feature introduced by Microsoft. This is also one of the best features for visualization in Power BI. We’ll also be discussing some of its limitations when it comes to visualization.

A small-multiples chart is a data visualization that consists of a series of similar graphs or charts arranged in a grid. It uses multiple views to show different partitions of a dataset. It is often used to compare the entirety of the data. For scenarios with a wide range of data presentations, small multiples are the best design solution.

This is what a small multiples chart looks like.

In order to apply the changes and use this feature, you need to restart the Power BI application.

The Small multiple visual is only available in column charts, bar charts, line charts, and area charts.  It’s not available in pie charts and any other charts.

First, let’s use a line chart and resize it as shown in the image.

Let’s utilize the Total Defects measure, and place it into the Values field.

For this example, we’ll analyze the total defects by vendor. Therefore, we need to add the Vendor to the Axis field.

Right now, you can see that it’s just a descending line chart.

To enable the small multiples chart visual feature, we need to bring in some data over the Small multiples field. So, let’s place the Vendor into the Small multiples field.

Then, within the Axis field, let’s change the Vendor to Date.

As you can see, we now have small multiple visuals in our visualization.

Let’s turn off the Title and the Background under the Formatting tab.

Then, change the color to yellow under the Data colors.

Right now, we can’t see the title on our small multiple visual because its color is dark. So, let’s change the title color to white. Just change the Font color under the Small multiple title.

Then, let’s change the font size of the title.

Let’s also change the alignment of the title horizontally and vertically by using the Alignment (for horizontal) and Position (for vertical) settings. The best title alignment I found for line charts is positioning them at the bottom. So, let’s change the Position to bottom.

The output should now look like this.

The most important section for a small multiple visual is the Grid layout. The Grid layout setting sets the amount of small multiple visuals that we can display on our rows and columns.

For example, if we increase the Rows and Columns to 6, it’ll display 6 items for rows and columns as well.

However, the downside of this is that we can only increase the rows and columns up to 6.

To make this look better, let’s just use 4 rows.

As a result, we can now see the lines more clearly. Let’s then remove these categories.

To do that, just turn off the Title for both X and Y Axis.

Then, turn off the X and Y axis as well.

We can also hide or display the grid lines by enabling or disabling them on the Y and X axis. For this example, let’s leave this turned on as it defines the borders around the visual and makes it look better.

Note that we currently don’t have conditional formatting for line charts. But you can certainly try doing it by using bar or column charts.

The small multiples visual can also handle secondary values. For example, let’s add another measure in the Secondary values field.

As you can see, it can handle secondary data which makes it a great feature for visualization.

We can also change the color for the second measure (Total Downtime (Hrs)).

Another cool feature is that we can analyze our data by using column charts.

And this is how it looks like if we convert our line visual to a bar chart.

For bar charts, it would be better to change the Axis to Month & Year instead of Date.

Just remember that if we want to make it look better, we can play with the various settings that are available in Power BI. For example, we can reduce the Rows and Columns on the Grid layout to make the bar chart look better.

For the bar height, we can just edit the Inner padding.

Don’t forget to check the other settings in the X and Y axis as well.

As for the sorting, we can only sort them by categories and not by values.

We can also use the area chart for our small multiples visual as shown in the image below.

To sum up, we’ve seen how the small multiples chart allows the viewer to focus on changes in the data rather than on changes in graphical visualization. We’ve also discussed the limitation of this visual when it comes to sorting and the limited number of options for rows and columns. Hopefully, they can make it better in the future.

It’s a relatively new feature introduced by Microsoft Power BI. You can play around with the different visuals that you can use with the small multiples chart.

Until next time,

Mudassir

Cnn Vs Ann Vs Rnn: Exploring The Difference In Neural Networks

Neither all models can be applied to one problem, nor all problems can be addressed with one neural network.

If you have come across machines recognising your face and voice among millions of images and wonder how is that possible, it is all to the credit of neural networks and deep learning. Neural networks are the algorithms that leverage the unique character of the human mind which thinks of possibilities. This characteristic is nothing but fuzzy logic, invented by Lotfi Zadeh. It resembles human reasoning and has inspired AI researchers to develop neural network algorithms. While machine learning algorithms take decisions according to the data they are fed, neural networks are designed to follow a path to decide for themselves. Researchers develop hundreds of algorithms in a day with different characteristics and performance capabilities and most of which build on existing models to predict and build real-world models. Neither all models can be applied to one problem, nor all problems can be addressed with one neural network.

Types of Neural Networks:

Artificial Neural Network (ANN):

Even though it is a layered algorithm, the chances of gradual corruption are low. Rather it occurs over a long period so that you have enough time to correct blunders. Unlike other networks, it stores information over the entire network leaving very little scope for disruption of the entire system because of a few missing pieces of information. This very characteristic makes ANN more fault-tolerant than others. They are popular for their multitasking capabilities, for they use a layered system where information is stored at every node, thereby developing an ability to generate outcomes by comparing the event with previous ones. Despite its numerous benefits, it is pretty much difficult to design an ANN for it takes a copious amount of data and a lot more trials to zero in on the right architecture. And also, ANN cannot identify sequential data required for sequential data processing.

Widely used for its computer vision applications, it comes with three layers viz. convolutional layer, pooling layer, and fully-connected layer. Computer vision, which is applied in image identification anchors on CNN networks. The complexity of algorithms increases with each layer. They analyze the input through a series of filters known as kernels. They are like matrices that move over the input data, used to extract features from the images. As the input images are processed, the links between neurons develop as kernels in the layers. For example, to process an image, kernels go through sequential layers and change accordingly in the process of identifying colors, shapes, and eventually, the overall image.

CNN algorithms have shot to fame after visual technology became the main source of information dissemination. The tasks which humans used to do earlier, now are made easy with AI-enabled tools developed for facial recognition, image recognition, handwritten character analysis, X-ray analysis, etc. CNN algorithms are still nascent and they do have issues working with variable data. It has been reported that CNN algorithms are not up to the mark when it comes to processing hidden objects in images, processing titled or rotated images. Training CNN algorithms requires good GPU (Graphical Processing Units), the lack of which might slow down the project.

Recurrent Neural Networks (RNN):

Voice recognition and natural language processing are the two linchpins of the RNN network. Be it voice search with Apple’s Siri, Google Translate, or Picasa’s face detection technology, it is all possible because of RNN algorithms. Contrary to feed-forward networks, RNN networks leverage memory. While for traditional neural networks inputs and outputs are assumed to be independent, the RNN network depends on previous outputs within the sequence. RTT Networks use a backpropagation technique that is slightly different from that used by other networks, which is specific to the complete sequence of data.

RNN is known for its double data processing capability, i.e., it processes data belonging to the present and the immediate past, therefore, developing memory and awareness of context through an in-depth understanding of sequences. These algorithms can be designed to process several inputs and outputs simultaneously mapping out one-to-one, one-to-many, many-to-one, and many-to-many datasets. Notwithstanding the benefits RNN has to offer, it comes with significant hurdles in the process of development. They take a lot of time to train RNN algorithms and are not so easy to develop or implement. Because of the way the layers are arranged in the RNN model, the sequence gets rather long resulting in exploding or null weights, leading to a gradient problem. To make an RNN model work, it is necessary to stack up the sequences, and hence it is impossible to pair this model with another one.

No wonder, neural networks are fast becoming indispensable for their versatility in providing solutions for different business problems. McKinsey estimates that deep learning and neural networks have the potential to spin a market of around \$3.5 trillion to \$5.8 trillion in different domains. The only problem at hand should be to identify the right neural network. Hope this article has thrown some light as to how a specific network works.

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Showcase Qoq Sales Using Time Intelligence In Power Bi

In this tutorial, we’re going to cover how to calculate quarter on quarter sales differences using time intelligence in Power BI. You may watch the full video of this tutorial at the bottom of this blog.

We’re not just going to do it at a granular level- we are going to try and analyze trends based on quarter on quarter sales.

Sometimes when you are looking at something from a very granular level, your visualizations on a whole will become very busy.

If you can smooth out the results that you’re looking at, it enables you to produce a much more compelling visualization which shows something more meaningful than a busy chart, which shows every adjustment or change in your result through time.

It’s a two-fold example that I will run through here. Not only are we going to run through how to visualize time calculations around different time periods, one quarter versus another quarter, we’ll also be analyzing the difference.

I want to show you how to create Quarter on Quarter Sales or how you can compare one quarter’s results to another quarter.

Then I will also show how to keep it dynamic, and how you can utilize the data model to discover the difference between the two quarters.

This is an example from a recent workshop that I ran by way of the Enterprise DNA webinar series. What we’re trying to do here is to analyze how our sales have fared on any one quarter and then compare it to a prior period.

To come up with these insights, I first grabbed my Dates field and turned it into a filter (right), and then grabbed the Date column and turned it into a table (left).

If we calculate the total of anything (e.g. Total Sales, Total Profits, Total Costs, etc.), these are what I call core calculations. These calculations are very easy to do because they are just simple sums or simple aggregations.

First, I’m going to drag the Total Sales into the table.

Now, if we want to compare on a quarter to quarter basis, we need to use time intelligence calculations. My favorite time intelligence calculation is the DATEADD function so I highly recommend familiarizing yourself with how to use the DATEADD function inside the CALCULATE function as you can see in this formula:

In this calculation, we referenced the initial core calculation, which is our Total Sales. We used the DATEADD function so we can jump back to any time period.

Since we wanted to do a quarter-on-quarter sales, all we had to do inside of DATEADD is to specify that we want to jump back one quarter.

This is my favorite function to use when it comes to time intelligence in Power BI because of all the variability and flexibility that you can put in this formula.

In this case, we’re just going to look at it from a quarterly perspective. Once I finish writing down this formula, I’ll drag it into the table.

You can see the Total Sales is being calculated from the current context, which means we’re calculating for whatever the particular day is.

However, the Sales LQ is calculating 1 quarter or 3 months ago from this day.

What’s so great about this calculation is how reusable it is. I’ll copy and paste the table I just made, grab my Quarter & Year measure, and drag it into the second table I have created.

Now, we are getting the true Quarter on Quarter calculations, and the timeframe or window we’re looking at is being determined by the filter we have in place.

We can drill into any grouping of quarters and make a comparison of our Total Sales and our Sales Last Quarter.

We can also work out what the changes are by creating a new measure. The formula I’ve used is to deduct the Sales LQ from the Total Sales.

I’ve subtracted the time intelligence calculation we created using DATEADD from our initial core calculation. This gave me the absolute Quarter on Quarter Sales Change.

There’s so many different ways that you can you can utilize these techniques. We’ve honed in on quarter on quarter here, but you can do your calculations for month on month or year on year.

If you’re just starting out with time intelligence in Power BI, this is a really good technique to practice and get you going. You’ll understand how context and measure branching works, and how to use time intelligence calculations. Once you implement them well, you can ultimately create Power BI reports that look compelling and showcase really good insight.

For many more time related insights that you can discover and illustrate with Power BI, check out this detailed course module at Enterprise DNA Online.

Time Intelligence Calculations

I hope you enjoy this tutorial as much as I have.

Sam

Embedded Bi Expectations Vs. Reality

Embedded BI promises to offer a wealth of benefits, including interactive dashboards and pixel-perfect reports. You expect the technology to help you efficiently monitor the data. You don’t want to miss any information on critical changes. Also, you expect it to be very easy to integrate into your application. You don’t want to face any complexity. But does the Why should you use Embedded BI? What is the best Embedded BI tool of 2023?

Embedded BI promises to offer a wealth of benefits, including interactive dashboards and pixel-perfect reports. You expect the technology to help you efficiently monitor the data. You don’t want to miss any information on critical changes. Also, you expect it to be very easy to integrate into your application. You don’t want to face any complexity. But does the embedded BI really meet your expectations? In this post, you will find all the details. What is Embedded BI?

Why is Yellowfin the best embedded BI tool of 2023?

Can the Yellowfin really meet my expectations?

Can it help me effortlessly add analytics to my product?

Does it allow me to effectively extend the analytics experience?

Does it help me to effectively analyze and monitor the data?

Can it help me identify critical changes in data?

Does it allow me to create threshold-based alerts?

How can I integrate the full Yellowfin application into my application or website? Ready to get started with the best Embedded BI tool of 2023?

What is Embedded BI? Why should you use Embedded BI?

Make data-driven business decisions

Improve sales and enhance revenue

Reduce cost by eliminating the need for ongoing technical resources

Boost productivity and improve customer satisfaction

Read: 5 Benefits of Modernizing Your Application’s Analytics with Embedded Analytics

What is the best Embedded BI tool of 2023?

The best embedded BI tool of 2023 is Yellowfin. It enables you to solve data complexity with action-based dashboards. It focuses on simplifying the entire analytics workflow by utilizing automation, data storytelling, and collaboration features.

Why is Yellowfin the best embedded BI tool of 2023?

The only analytics tool on the market that successfully combines action-based dashboards with automated analysis and data storytelling.

Helps you automate the business monitoring process by efficiently discovering changes and outliers in the data.

Offers rich data visualizations to help you quickly create insightful data stories.

Can the Yellowfin really meet my expectations?

Yellowfin is an outstanding tool. But can it really meet your expectations? Let’s dive in.

Can it help me effortlessly add analytics to my product?

Yellowfin allows you to easily deliver an actionable dashboard to your customers. It supports robust embedding methods, like iFrames, APIs and SDKs, to help you easily integrate the whole dashboard module into your web application. As a result, your web application user can quickly discover key insights and make better business decisions. You can even contextually embed individual dashboards directly into your application’s workflows. Yellowfin offers flexibility and convenience.

Does it allow me to effectively extend the analytics experience?

Yellowfin allows you to use your own components. As a result, you can meet the specific needs for extending the analytics experience for the end-users. It supports extensions in several key areas, including analytical functions and JavaScript charts. Adding Logic Another great way is using Code Mode. It enables you to access some of the underlying code of the Yellowfin object. As a result, you can conveniently create custom UI objects. For example, you can add a custom button that executes custom code. Other BI tools, like Sisense, offer limited extension capability for developers. But with Yellowfin, you will find endless ways of extending the analytics experience. Integrate custom charts Yellowfin supports a feature, called “JavaScript charts”. It supports several popular charting libraries such as chúng tôi and chúng tôi By utilizing them, you can conveniently extend the analytics experience with your own custom chart.

Does it help me to effectively analyze and monitor the data? Can it help me identify critical changes in data?

You can use Yellowfin Signals to automate the process of discovering key insights in your data. It deploys a variety of complex algorithms to identify major variations in your data. This includes changes in total and average, trend direction, and outliers. The algorithms enable Signals to automatically detect significant changes in data. You can configure it to simultaneously monitor different metrics and dimensions. It will alert you whenever it identifies a major variation. As a result, you will never have to worry about missing critical information. Other similar embedded BI tools, including Sisense, don’t have any equivalent capability. Yellowfin is clearly the better solution for efficiently monitoring critical changes in data.

Does it allow me to create threshold-based alerts?

Yellowfin allows you to set threshold-based alerts. You will receive notifications whenever the changes in data exceed the specified limit. However, you must set an appropriate threshold. Otherwise, you will receive meaningless signals.

How can I integrate the full Yellowfin application into my application or website?

You can integrate Yellowfin using two methods:

Redirection: The easiest integration approach. You can route your users directly into Yellowfin. However, you might need to restyle your application to maintain a consistent look and feel.

iFrame: You can consider embedding Yellowfin within your application by using an iFrame. If you need to implement custom navigation, you will find this approach easier than the previous method.

Ready to get started with the best Embedded BI tool of 2023?

Yellowfin is an outstanding business intelligence tool. It helps you simplify the entire analytics workflow. It can automatically analyze and monitor the data. So, you can easily get key insights into your business. Also, Yellowfin allows you to use custom charts to deliver the best analytics experience to your app users. Besides, it is very easy to integrate into your application. For these reasons, Yellowfin has become the best embedded BI tool of 2023.

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