Kirk Munroe’s Post

Insert Different Object in PowerBI Step 1: Creating modify slicer that contain City, Country/Region and Region. Step 2: In output I’m showing the Profit by Category and Country/Region using Slicer. Step 3: Now, load the data using Get Data option. After that go to Modeling menu bar, in that click on New Parameter and select Fields. For More Details Visit https://lnkd.in/gQhC6VEk #PowerBI,#BusinessIntelligence,#DataVisualization,#PowerBIObjects,#PowerBITutorial,#PowerBIVisuals,#DataAnalytics,#DataDashboard,#BIReporting,#PowerBIDevelopment,#PowerBIForBeginners,#DataStorytelling,#DataDrivenDecisions,#PowerBIDashboard,#DataInsights

Data Visualization in Power BI 🌐

❓ Do you use Bravo by SQLBI when building Power BI semantic models? As well as analysing your model, my favourite feature of Bravo is the built-in UI for creating date tables. Not only does it provide the ability to easily customize your Date table, but will also automatically create any Time Intelligence measures you might need. A huge time saver! Check it out here - it's free! https://bravo.bi/ #powerbi #data #sqlbi #bravo

Tableau is indeed a playground, i was able to analyze a new dataset from scratch ,various visualizations:Dual Axis graphs, scatter plots etc, calculated fields,Bins and bin sizes, i also leveraged the power of interactivity through: filters, visualizations as filters, parameters.

Data Visualisation in Power BI Complete with DataCamp

I am excited to learn Data Visualization in Power BI.

🚀 Excited to share my latest project in Power BI! I recently learned Power BI through live sessions from WsCube Tech and created this insightful dashboard showcasing transaction data (PhonePe Pulse). This experience enhanced my skills in data visualization, helping me transform complex datasets into meaningful insights. Looking forward to applying these skills in real-world projects! Feedback and suggestions are welcome. 🙌 #PowerBI #DataVisualization #LearningJourney #DashboardDesign #PhonePePulse

#DataFam, I rarely get a chance to use maps in my day-to-day Tableau work. At #NextLevelTableau with coach Andy Kriebel, the team explored the following mapping capabilities 1. Spatial Data Files 2. MAKEPATH 3. MAKEPOINT and 4. OUTLINE Check out what we prepared https://lnkd.in/gEEZ2n-U Do you use maps? What are your favourite capabilities?

Anyone who thinks it’s a great idea to put your semantic layer inside a BI tool such as Power BI or Tableau, has clearly never managed a data platform at scale.  Although these tools make it very convenient and easy to create the semantic models, the engines underneath are usually very inefficient, requiring a ton of compute.  And then to address the elephant in the room, what happens when you need to migrate to another tool? All that work has to be unwound and redone elsewhere. And you better hope alternative connectors exist to repoint your dashboards. 😂 Bottom Line - keep your semantic layer as close to the data as possible, ideally in views at the source or close to it. You can also consider a virtual cube such as atScale or CubeDev to name a few. #dataengineering

At most organizations, the BI layer is pure chaos. Hundreds, sometimes thousands, of people build disparate semantic models with little collaboration or visibility across silos. The result? Rising costs, slower performance, and—most importantly—a loss of trust in analytics. As Matt points out, shifting logic out of the BI tool and into a true semantic layer is a key step to mitigate this sprawl and ensure consistency in reporting. At Datalogz, we recently introduced a semantic layer recommendations across Power BI, Tableau, and Qlik. Datalogz Control Tower identifies overlapping business logic and expressions for reuse, enabling teams to move that logic into the warehouse or a true semantic layer. The general rule? If a model/expression/measure is being used more than five times across departments, it’s time to move it out of BI! Bottom line: The closer your semantic layer is to the data source, the better it scales and performs—helping your teams save time, costs, and complexity.