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What is Power BI? Get To Know Insights

Power BI is a set of software services, apps, and connectors that work together to transform disparate data sets into logical, visually immersive, and interactive insights. Your data could be in the form of an Excel spreadsheet or a collection of cloud-based and on-premises hybrid data warehouses. Connect to your data sources, visualize and uncover what matters, and share your results with whoever you choose using Power BI.

Power BI Architecture:

The Power BI architecture is a service that runs on Azure. Power BI can connect to a variety of data sources. Power BI Desktop lets you to create reports and data visualisations based on the dataset. The Power BI gateway provides real-time data for reporting and analysis by connecting to on-premise data sources. Power BI services refer to the cloud services that are used to create Power BI reports and data visualisations. With Power BI mobile apps, you can connect to their data from anywhere. There are Power BI apps for Windows, iOS, and Android.

What exactly is the Power BI Service?

Power BI Service (or just Power BI) is the whole version of Power BI that is hosted on Azure, Microsoft's cloud platform. There are two editions of Power BI Service: Power BI Pro and Power BI Premium.

What is Power BI Pro and how does it work?

In terms of capabilities, Power BI Pro is similar to Power BI Desktop. They have the same visualization choices, storage, and file upload size restrictions, and report refresh time limits as each other. The primary difference is that with Pro, you can securely share your data, reports, and dashboards with others—as long as they also own a Power BI Pro subscription.

What is Power BI Premium?

Power BI Premium is a new way to get access to Power BI. Premium allows you to pay for the amount of storage and processing power you need for your business rather than buying licenses for individual users like Power BI Pro does. Users can choose from six capacity models, each with varying quantities of memory, to power their Power BI platform.

What is Power BI Report Server?

Power BI Report Server is a server solution that comes as part of the Power BI Premium subscription. Users can run Power BI on their own computers using Report Server.

Instead than using a web browser, users who use Report Server must use the Power BI Desktop software. They also don't have access to dashboards, real-time streaming, content packs, app workspaces, natural language queries, the capacity to analyse data in Excel, email subscriptions, and data alerts.

What is Power BI Embedded?


Power BI Embedded is a pay-as-you-go "white-label" service that enables ISVs and developers to incorporate Power BI functionality and capabilities into their own apps. Businesses can integrate Power BI into their products instead of designing their own reporting solutions, and Power BI will handle all of the reporting and data processing.

What is Power BI Mobile?

Power BI Mobile is the company's native mobile app, and it's available for Windows, iOS, and Android devices. Regardless of whether the data is kept in the cloud or on-premise in SQL Server, users can receive secure access to real-time dashboards and reports via the app. Mobile users can also create and share reports, make natural language queries, and receive push notifications for personal data warnings.

Tech Stack:

Pivot Point (Data Modeling Technique)
The Data Analysis Expression (DAX) Language is a programming language for expressing data analysis.

Closure:

Power BI comes with a variety of tools and services that make business analytics a breeze. Advanced analyses can be done by non-technical users, and interactive dashboards and live reports can be created to view data.

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How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)

How to Automate Excel with Python

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation

What is OPENPYXL

Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

Sheet: A sheet is a single page composed of cells for organizing data.

Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

Row: A row is a horizontal line represented by a number (1,2, etc.).

Column: A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

pip install openpyxl

CREATE A NEW WORKBOOK

We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.

from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws = wb.active
#creating new worksheets by using the create_sheet method

ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position

#Renaming the sheet
ws.title = "Example"

#save the workbook
wb.save(filename = "example.xlsx")

READING DATA FROM WORKBOOK

We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.

#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")

 

GETTING SHEETS FROM THE LOADED WORKBOOK

 

#getting sheet names
wb.sheetnames
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']

#getting a particular sheet
sheet1 = wb["sheet2"]

#getting sheet title
sheet1.title
result = 'sheet2'

#Getting the active sheet
sheetactive = wb.active
result = 'sheet1'

 

ACCESSING CELLS AND CELL VALUES

 

#get a cell from the sheet
sheet1["A1"] <
  Cell 'Sheet1'.A1 >

  #get the cell value
ws["A1"].value 'Segment'

#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)
d.value
10

 

ITERATING THROUGH ROWS AND COLUMNS

 

#looping through each row and column
for x in range(1, 5):
  for y in range(1, 5):
  print(x, y, ws.cell(row = x, column = y)
    .value)

#getting the highest row number
ws.max_row
701

#getting the highest column number
ws.max_column
19

There are two functions for iterating through rows and columns.

Iter_rows() => returns the rows
Iter_cols() => returns the columns {
  min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.

Example:

#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
  for cell in row:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C3 >

  #iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
  for cell in col:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.C3 >

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.


Example:

for row in ws.values:
  for value in row:
  print(value)

 

WRITING DATA TO AN EXCEL FILE

Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.

 

CREATING AND SAVING A NEW WORKBOOK

 

#creates a new workbook
wb = openpyxl.Workbook()

#saving the workbook
wb.save("new.xlsx")

 

ADDING AND REMOVING SHEETS

 

#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")

#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")

#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']

#deleting a sheet
del wb['sheet 0']

#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']

 

ADDING CELL VALUES

 

#checking the sheet value
ws['B2'].value
null

#adding value to cell
ws['B2'] = 367

#checking value
ws['B2'].value
367

 

ADDING FORMULAS

 

We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.
 

For example:

import openpyxl
from openpyxl
import Workbook

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']

ws['A9'] = '=SUM(A2:A8)'

wb.save("new2.xlsx")

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.

image

 

MERGE/UNMERGE CELLS

Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

For example:
Merge cells

#merge cells B2 to C9
ws.merge_cells('B2:C9')
ws['B2'] = "Merged cells"

Adding the above code to the previous example will merge cells as below.

image

UNMERGE CELLS

 

#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')

The above code will unmerge cells from B2 to C9.

INSERTING AN IMAGE

To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.

Example:

import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3

ws.add_image(img, 'A3')

wb.save("new2.xlsx")

Result:

image

CREATING CHARTS

Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:

Example

import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series

wb = openpyxl.load_workbook("example.xlsx")
ws = wb.active

values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
chart.add_data(values)
ws.add_chart(chart, "E3")
wb.save("MyChart.xlsx")

Result
image


How to Automate Excel with Python with Video Tutorial

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling

📄 Resources 📄
OpenPyXL Docs: https://openpyxl.readthedocs.io/en/stable/ 
Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial 
Subscribe: https://www.youtube.com/c/TechWithTim/featured 

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Power BI vs Tableau

In your search for a Business Intelligence (BI) or data visualization tool, you have probably come across the two front-runners in the category: Power BI and Tableau. They are very similar products, and you have to look quite closely to figure out which product might work the best for you. I work for Encore Business Solutions; a systems partner that specializes in both Power BI and Tableau. We’ve seen more than a few scenarios in which Tableau was being used when the company really should have gone with Power BI, and vice-versa. That was part of the inspiration for this side-by-side comparison.

This is image title

Unfortunately, the internet is full of auto-generated and biased pages regarding which product trumps the other. The truth is, the best product depends more on you, your organization, your budget, and your intended use case than the tools themselves. It is easy to nit-pick at features like the coding language that supports advanced analysis, or the type of maps supported — but these have a minimal impact for most businesses. I’m going to do my best to stay away from these types of comparisons.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

In writing this comparison, I did a lot of research. The result was more than just this article: I also created a tool that can generate a recommendation for you based on your response to a short questionnaire. It will generate a score for both Power BI and Tableau, plus provide a few other things to think about.

Tableau Software
Founded in 2003, Tableau has been the gold-standard in data visualization for a long time. They went public in 2013, and they still probably have the edge on functionality over Power BI, thanks to their 10-year head start. There are a few factors that will heavily tip the scales in favour of Tableau, which I’ll cover in the next few paragraphs.

Tableau: Key Strengths
Let’s make one thing clear from the start: if you want the cream of the crop, all other factors aside, Tableau is the choice for you. Their organization has been dedicated to data visualization for over a decade and the results show in several areas: particularly product usability, Tableau’s community, product support, and flexible deployment options. The range of visualizations, user interface layout, visualization sharing, and intuitive data exploration capabilities also have an edge on Power BI. Tableau offers much more flexibility when it comes to designing your dashboards. From my own experience, Tableau’s functionality from an end-user perspective is much farther ahead of Power BI than the Gartner Magic Quadrant (below) would have you believe.

Tableau built their product on the philosophy of “seeing and exploring” data. This means that Tableau is engineered to create interactive visuals. Tableau’s product capabilities have been implemented in such a way that the user should be able to ask a question of their data, and receive an answer almost immediately by manipulating the tools available to them. I have heard of cases in which Tableau actually declined to pursue the business of a customer in the scenario that the customer didn’t have the right vision for how their software would be used. If you just want something to generate reports, Tableau is overkill.

Tableau is also much more flexible in its deployment than Power BI. You can install the Tableau server in any Window box without installing the SQL server. Power BI is less flexible which I will discuss in Power BI Weaknesses.

Tableau can be purchased on a subscription license and then installed either in the cloud or an on-premise server.

Finally, Tableau is all-in on data visualization, and they have their fingers firmly on the pulse of the data visualization community’s most pressing desires. You can expect significant future improvements in terms of performance when loading large datasets, new visualization options, and added ETL functions.

Tableau Weaknesses
Unfortunately, Tableau comes at a cost. When it comes to the investment required to purchase and implement Tableau – 9 times out of 10 it will be more expensive than Power BI, by a fair margin. Often, Tableau projects are accompanied by data-warehouse-building endeavours, which compound the amount of money it takes to get going. The results from building a data warehouse and then hooking up Tableau are phenomenal, but you’ll need an implementation budget of at the very least $50k – plus the incremental cost of Tableau licenses. Learn more from Power bi online course

Of course, a data warehouse is not a requirement. Tableau connects to more systems out-of-the-box than Power BI. However, Tableau users report connecting to fewer data sources than most other competing tools. Overall, considering the investment required to implement a data warehouse is a worthy indicator of the commitment required to get the most out of Tableau.

This is image title

Power BI
Power BI is Microsoft’s data visualization option. It was debuted in 2013, and has since quickly gained ground on Tableau. When you look at Gartner’s most recent BI Magic Quadrant, you’ll notice that Microsoft is basically equal to Tableau in terms of functionality, but strongly outpaces Tableau when it comes to “completeness of vision”. Indeed, the biggest advantage of Power BI is that it is embedded within the greater Microsoft stack, which contributes to Microsoft’s strong position in the Quadrant.

This is image title

Power BI: Key Strengths
Though Tableau is still regarded by many in the industry as the gold standard, Power BI is nothing to scoff at. Power BI is basically comparable to all of Tableau’s bells and whistles; unless you care deeply about the manifestation and execution of small features, you’re likely to find that Power BI is fully adequate for your BI needs.

As I mentioned, one of the biggest selling points of Power BI is that it is deeply entrenched in the Microsoft stack – and quickly becoming more integrated. It’s included in Office 365, and Microsoft really encourages the use of Power BI for visualizing data from their other cloud services. Power BI is also very capable of connecting to your external sources.

Because Power BI was originally a mostly Excel-driven product; and because the first to adopt Microsoft products are often more technical users, My personal experience is that Power BI is especially suitable for creating and displaying basic dashboards and reports. My own executive team really likes being able to access KPIs from the Office portal, without having to put much time into the report’s creation, sharing, and interactivity.

Power BI’s biggest strength; however, is its rock-bottom cost and fantastic value. For a product that is totally comparable to the category leader, it’s free (included in Office 365) for basic use and $10/user/month for a “Pro” license. This increases adoption of the product as individuals can use Power BI risk-free. For companies that don’t have the budget for a large Business Intelligence project (including a data warehouse, dedicated analysts, and several months of implementation time), Power BI is extremely attractive. Companies that are preparing to “invest” in BI are more likely to add Tableau to their list of strongly considered options.

Power BI is available on a SaaS model and on-premise; on-premise is only supported by Power BI Premium licensing.

Microsoft is also investing heavily in Power BI, and they’re closing the small gaps in their functionality extremely fast. All of those little issues some users have with Power BI are going to disappear sooner rather than later.

Power BI Weaknesses
As I’ve mentioned, Tableau still has the slight edge on Power BI when it comes to the minutiae of product functionality; mostly due to their 10-year head start. But perhaps Power BI’s greatest weakness is its lack of deployment flexibility. For Power BI on-premise you need to install the Power BI Report Server as well as the SQL Server.

I also mentioned that Tableau works well for users with large amounts of data and for users that want on-premise systems. You should be aware that there are some new features being added to Power BI via Power BI Premium that help catch Microsoft up to Tableau in the areas of large datasets and on-premise capabilities – but Power BI Premium adds significant cost, and these features are relatively new. Tableau still reigns in these areas.

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Comparing Power BI with other tools

the Business Intelligence (BI) world has been moving towards self-service BI. As expected, several vendors created tools empowering regular users to gain insights from their data. Among the many, there is Power BI. Nowadays, users want to understand the differences between Product X and Power BI.

This is image title

One of the most common questions in conferences and user group sessions is likely, “can you provide a comparison between this product and Power BI?”.

The answer is almost always, “No, I cannot compare them, because they are too different”. First, one needs to understand the deep difference between Power BI and most other reporting tools on the market. Only later does a comparison make any sense. As a matter of fact, I think Power BI can be compared to only a few products on the market today. I would like to add my point of view to the discussion.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

Indeed, Power BI is a tremendously powerful data modeling tool that happens to come with a pretty face; most other products are beautifully crafted reporting tools with a pretty face. The only thing they have in common is the pretty face. If you stop at what they have in common, you are only comparing a small fraction of the whole product, and that would be unfair.

To go further, a deeper understanding of basic BI concepts is needed.

Beware: this article is biased. I love Power BI and I make my living out of it. Nevertheless, I am a BI professional; I started working with Business Intelligence many years ago and I have gathered experience that I can share. I will try to be as fair as I can in this post, as my goal is not to provide a comparison with any tool. The goal of this post is to help you understand what you really need to evaluate when making (or reading) any comparison between different BI products.

At the top level, any Business Intelligence solution is composed of three layers:

Raw data: these are the data sources that one wants to analyze. Raw data comes as is.
Semantic model: this is where data is re-arranged to optimize it for analysis. Here you also define the calculations required by the reports.
Reports: these are the nice dashboards you can build with the tool.

Power BI manages all three layers: you start from raw data, you can build a semantic layer, and finally you prepare reports. Most other reporting tools are focused on the last layer and are limited in the previous two. In other words, they are missing the capability to build a real semantic layer. It is important to clarify what a semantic layer is, to understand what you would miss by choosing a different product.

In the old ages of BI, there was a clear separation between users and developers. A BI developer would build a project to help users extract insights from their data, and build reports. Users did not need to understand tables, relationships, or calculations. The developer oversaw shaping the tables, providing predefined calculations and giving sensible names to entities. Leveraging the semantic model, users did not have to know DAX, MDX or SQL.

A semantic model lets users interact with business entities like customers, sales, and products. Users would place those entities in reports made with Excel or with other reporting tools. Regular users were happy with just Excel and a Pivot Table. More advanced users wanted more powerful tools, and this led to the creation of several reporting tools with their ad-hoc programming language to create more advanced formulas. Regardless, the important thing is that no matter how powerful those tools are, they were still reporting tools based on the existence of a previously crafted semantic model. No semantic model, no reporting.

Picture this: a BI tool lets a developer build a semantic model. A reporting tool lets a user build a report on top of an existing semantic model. You need both to create a BI solution.Learn more from Power BI online course

Unfortunately, building a BI project takes time. Users were hungry for reports. This led to the start of the Self-Service BI era. Self-service BI is the idea of users building reports themselves, to reduce development time and to build a democratic knowledge about data. Sounds cool and terrifying at the same time.

Anyway, this is where we are today. Obviously, driven by the market several vendors started to build self-service BI tools. A few new products appeared on the market. Rather, existing tools evolved into new ones, targeting self-service BI. Keep in mind: any self-service BI tool requires the functionalities to build both the semantic model and the report in the same tool. Thus, depending on where you start, you have two options to have an existing product evolve into a self-service product:

If you already have a semantic model tool, you need to add reporting capabilities. You need to make it easier to use, because the target is no longer a BI professional but a regular user instead.
If you already have a reporting tool, you need to add the capability to build a semantic model because your users need to massage the data and build calculations on top of the resulting model.

In both cases, in the end you obtain a tool that mixes the capabilities to create a semantic model and to build reports. After this first step, you can add tons of different features like sharing with other users, building wizards to automatically connect to other services, improving the formula language and so on. But the core is always the same: a semantic model and a reporting tool, bound together in a nice package.

Even though we consider Power BI to be a new product, it is actually the evolution of Power Pivot and Analysis Services Tabular (semantic model), Power Query (querying tool), and Power View (the first version of the reporting tool released with Excel and SharePoint). Other vendors took similar steps, with different starting points. It is fair to say that several vendors started from a reporting tool, adding the semantic model to it.

Now, if you need to compare two BI tools, you need to compare at least these two features: the semantic model and the tools to build a report.

Say you want to compare Product X with Power BI; you show me how easy it is to build a gorgeous report on top of an SQL view, much easier and much more powerful than Power BI. Cool, but you are only comparing a fraction of both products. Reporting-wise, sure, Product X is better than Power BI. But there are other considerations: can you load multiple tables in Product X? Can you build relationships between them? Can you use a programming language to author complex calculations that involve scanning different tables? All these operations belong to the semantic model. A fair comparison needs to apply to all the features.

This is what Power BI offers you:

Power Query – a data transformation tool which is easy to use and yet incredibly powerful. It can load virtually anything and join data from different sources.
A modeling environment where you can build different kinds of relationships between tables and build powerful models. It does not hurt that it runs on top of one of the fastest databases I have ever seen.
DAX – a programming language which is not easy, but lets you author nearly any query and calculation. Yes, on this I am biased for sure!
Power BI – a reporting engine which is very good in building dashboards and reports. It can also be extended with custom visuals and third-party products.

Then, there is web-based reporting and sharing, a mobile experience, the ability to load from nearly any data source in the cloud or on premises and many other useful features. Yet, the core is composed of the four features above. If you want to compare apples to apples, you need to compare at least these four parts. Be mindful: you need all of them. A tool that requires you to build a single table because it does not let you relate two tables is nothing but a nice reporting tool. Comparing it to Power BI does not make much sense to me.

Moreover, it does not come by chance that to learn Power BI, one needs to learn new programming languages. Each feature has its own language, and this is just the right thing to have.

Finally, reporting. Reporting is only the last part, even though it is the most visible one. You might find other products are better than Power BI when it comes to reporting. This is fine, if you are aware that you are only comparing a fraction of Power BI with the whole of Product X.

I love Power BI, and I would really love to see a fair comparison between Power BI and any other product. We could learn a lot from the topic. But for it to be fair, it cannot just be based on how easy it is to build a pie chart (just kidding! You are not using a pie chart, are you?). One needs to evaluate everything both products have to offer.

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