Data Science Developer with Microsoft Power BI Salary in 2024

Where is Microsoft Power BI used?

Microsoft Power BI Alternatives

Tableau

Interactive data visualization tool for business intelligence. Capable of handling large volumes of data for creating complex and understandable charts.

//Example Tableau code is not typically shared as it uses a drag-and-drop interface

• Intuitive user interface.

• Robust data handling and visualization.

• Strong mobile support.

• Can be expensive for smaller companies.

• Steep learning curve for complex functions.

• Requires manual refresh for live data.

QlikView

Data discovery product that provides self-service BI for all business users. Employs associative data modeling for creating and sharing interactive reports.

//QlikView script examples are proprietary and depend on GUI operations

• Deep insights with associative modeling.

• Fast data processing.

• Flexible and customizable.

• High total cost of ownership.

• Less intuitive for new users.

• Desktop client needed for development.

Google Data Studio

Free online tool to turn data into custom dashboards and reports. Integrates with other Google products and supports connectors to various data sources.

//Google Data Studio uses a visual interface, thus no coding samples

• Free of charge for users.

• Seamless Google services integration.

• Real-time data collaboration.

• Limited data connectors in free version.

• Basic compared to other BI tools.

• Performance may be sluggish with complex reports.

Quick Facts about Microsoft Power BI

The Brainchild of Giants

So, imagine the tech titans at Microsoft huddling up in a conference room and going, "You know what the world needs? More graphs." And just like that, in the summer of 2011, Power BI was born, offering us mere mortals a slice of that sweet, sweet data visualization pie. Initially part of Excel as an add-on, it got so buff from crunching numbers, it had to get its own gym membership as a standalone product by 2015.

Version Evolution or Power BI-lutionary

The metamorphosis of Power BI is more dramatic than a season finale of your favorite show. Initially, it started as small-scale features in Excel—Power Query, Power Pivot, Power View. Fast forward a few years, and it's strutting down the runway as Power BI Desktop, its own designer attire! It got faster, better, and with more models than a fashion week in Milan, rolling out monthly updates like they're going out of style!

Breaking New Ground, Literally

When Power BI hit the scene, it wasn't just about making charts and graphs look pretty. Oh no, it came with groundbreaking features like natural language query - you could literally type a question about your data like you're texting a friend. "Yo BI, how many slices of pizza did we sell last Thursday?" And BAM! It tells you, without even a hint of sarcasm that you might be eating too much pizza.

// Here's a whimsical pseudo-code that wouldn't run, but imagine if it did:
pizzaQuery = "How much pizza sold on Thursday?"
answer = PowerBI.magicAsk(pizzaQuery)
print("Behold! You sold " + answer + " slices. Yum!")

What is the difference between Junior, Middle, Senior and Expert Microsoft Power BI developer?

Top 10 Microsoft Power BI Related Tech

1. 
   

   DAX (Data Analysis Expressions)

   
   

   
   Imagine a world where Excel formulas went to the gym and got swole – that's DAX for you. It's like the language of the Power BI universe, flexing its muscles to manipulate data models with complex calculations. If DAX were a superhero, it'd be flexing its pecs while solving your data riddles.

   
   

   
               // An example DAX formula that calculates total sales
               Total Sales = SUMX(Sales, Sales[Quantity] * Sales[Unit Price])
           

1. 
   

   Power Query M language

   
   

   
   Think of Power Query M as the backstage magician of Power BI. Abracadabra, and your messy data is transformed into a clean, delightful spreadsheet! This language is all about making sure your data doesn't look like it was organized by a toddler.

   
   

   
               // A snippet of M code to filter a column to values greater than 500
               let
                   Source = Csv.Document(File.Contents("C:\sales.csv"), ...),
                   FilteredRows = Table.SelectRows(Source, each [Sales] > 500)
               in
                   FilteredRows
           

1. 
   

   SQL (Structured Query Language)

   
   

   
   SQL is like the elder statesman of data languages, respected and a tad bit old-fashioned, but indispensable. It's the bedrock for pulling data from databases, and without it, you're like a knight without a sword in the data realm.

   
   

   
               -- SQL query example that fetches total sales from a Sales table
               SELECT SUM(UnitPrice * Quantity) AS TotalSales
               FROM Sales
           

1. 
   

   MDX (Multidimensional Expressions)

   
   

   
   MDX is like DAX's cousin who's into three-dimensional chess. Sure, it's a bit less limelight-stealing, but when you're dealing with OLAP cubes, this is the cat's pajamas. It's there to slice and dice data that resides in multiple dimensions.

   
   

   
               // MDX query example that slices data by the Time dimension
               SELECT NON EMPTY { [Measures].[Internet Sales Amount] } ON COLUMNS,
               NON EMPTY { ([Time].[Calendar].[Month].ALLMEMBERS ) } DIMENSION PROPERTIES MEMBER_CAPTION ON ROWS
               FROM [Adventure Works]
           

1. 
   

   Power BI Service

   
   

   
   Behold the cloud fortress where all Power BI warriors unite - the Power BI Service. It's where your dashboards and reports go to socialize and get shared, and where data refresh dances happen. Think of it as the town square for all things Power BI.

1. 
   

   Azure Data Services

   
   

   
   Strap on your jetpack and enter the Cloud-stratosphere with Azure Data Services. This is where data gets big, fast, and somewhat intimidating – like having a pet dragon for your data needs. It's all about building a fortress of services around your data.

1. 
   

   Power BI Embedded

   
   

   
   Want to give your apps a dash of data visualization without the hassle? Power BI Embedded is like the secret spice you sprinkle over your applications to give them superpowers – BI superpowers, making data insights look stunningly seamless.

1. 
   

   REST APIs

   
   

   
   REST APIs to Power BI are like messenger pigeons that actually know a thing or two about tech. They're the way you whisper sweet nothings (or rather, crucial data updates) to Power BI from other applications, making everything play together nicely.

   
   

   
               // An example of using the Power BI REST API to push data
               POST https://api.powerbi.com/v1.0/myorg/datasets/{dataset_id}/rows
               Authorization: Bearer {access_token}
               Content-Type: application/json
   
               { "rows": [
                   { "SalesTerritory": "Northwest", "TotalSales": 500000.00 }
               ]}
           

1. 
   

   JavaScript

   
   

   
   Sometimes, your Power BI reports need to play tag with your webpages. JavaScript is like the cool playground supervisor that ensures your embedded reports behave nicely on the web, letting them run, jump and provide interactive insights.

   
   

   
               // JavaScript code to embed a Power BI report in a webpage
               var embedConfiguration = {
                   ...
                   accessToken: 'H4n...uw==',
                   embedUrl: 'https://app.powerbi.com/reportEmbed?reportId=b1c...9dd'
                   ...
               };
               var report = powerbi.embed(element, embedConfiguration);
           

1. 
   

   Power BI Mobile

   
   

   
   Take your dashboards on a date – Power BI Mobile ensures your nuggets of insights can keep you company and whisper sweet nothings to you, anytime, anywhere. It's all the Power BI goodness, but pocket-sized.