Power BI Interview Questions with Answers
Question: How would you write a DAX formula to calculate a running total that resets every year?
RunningTotal =
CALCULATE( SUM('Sales'[Amount]),
FILTER( ALL('Sales'),
'Sales'[Year] = EARLIER('Sales'[Year]) &&
'Sales'[Date] <= EARLIER('Sales'[Date])))
Question: How would you manage and optimize Power BI reports that need to handle very large datasets (millions of rows)?
Solution:
1. Use DirectQuery mode if real-time data is needed.
2. Pre-aggregate data in the data source.
3. Use dataflows for preprocessing.
4. Implement incremental refresh.
Question: What steps would you take if a scheduled data refresh in Power BI fails?
Solution:
Check the Power BI service for error messages.
Verify data source connectivity and credentials.
Review gateway configuration.
Optimize and simplify the query.
Question: How would you create a report that dynamically updates based on user input or selections?
Solution: Use slicers and what-if parameters. Create dynamic measures using DAX that respond to user selections.
Question: How would you incorporate advanced analytics or machine learning models into Power BI?
Solution:
Use R or Python noscripts in Power BI to apply advanced analytics.
Integrate with Azure Machine Learning to embed predictive models.
Use AI visuals like Key Influencers or Decomposition Tree.
Question: How would you integrate Power BI with other Microsoft services like SharePoint, Teams, or PowerApps?
Solution: Embed Power BI reports in SharePoint Online and Microsoft Teams. Use PowerApps to create custom forms that interact with Power BI data. Automate workflows with Power Automate.
Question: How to use if Parameters in Power BI?
Go to "Manage Parameters":
Navigate to the "Home" tab in the ribbon.
Click on "Manage Parameters" from the "External Tools" group.
Click on "New Parameter."
Enter a name for the parameter and select its data type (e.g., Text, Decimal Number, Integer, Date/Time).
Optionally, set the default value and any available values (for dropdown selection).
Question: What is the role of Power BI Paginated Reports and when are they used?
Solution: Power BI Paginated Reports (formerly SQL Server Reporting Services or SSRS) are used for pixel-perfect, printable, and paginated reports. They are typically used for operational and transactional reporting scenarios where precise formatting and layout control are required, such as invoices, statements, or regulatory reports.
Question: What are the options available for managing query parameters in Power Query Editor?
Solution: Power Query Editor allows users to define and manage query parameters to dynamically control data loading and transformation. Parameters can be created from values in the data source, entered manually, or generated from expressions, providing flexibility and reusability in query design.
Question: How would you write a DAX formula to calculate a running total that resets every year?
RunningTotal =
CALCULATE( SUM('Sales'[Amount]),
FILTER( ALL('Sales'),
'Sales'[Year] = EARLIER('Sales'[Year]) &&
'Sales'[Date] <= EARLIER('Sales'[Date])))
Question: How would you manage and optimize Power BI reports that need to handle very large datasets (millions of rows)?
Solution:
1. Use DirectQuery mode if real-time data is needed.
2. Pre-aggregate data in the data source.
3. Use dataflows for preprocessing.
4. Implement incremental refresh.
Question: What steps would you take if a scheduled data refresh in Power BI fails?
Solution:
Check the Power BI service for error messages.
Verify data source connectivity and credentials.
Review gateway configuration.
Optimize and simplify the query.
Question: How would you create a report that dynamically updates based on user input or selections?
Solution: Use slicers and what-if parameters. Create dynamic measures using DAX that respond to user selections.
Question: How would you incorporate advanced analytics or machine learning models into Power BI?
Solution:
Use R or Python noscripts in Power BI to apply advanced analytics.
Integrate with Azure Machine Learning to embed predictive models.
Use AI visuals like Key Influencers or Decomposition Tree.
Question: How would you integrate Power BI with other Microsoft services like SharePoint, Teams, or PowerApps?
Solution: Embed Power BI reports in SharePoint Online and Microsoft Teams. Use PowerApps to create custom forms that interact with Power BI data. Automate workflows with Power Automate.
Question: How to use if Parameters in Power BI?
Go to "Manage Parameters":
Navigate to the "Home" tab in the ribbon.
Click on "Manage Parameters" from the "External Tools" group.
Click on "New Parameter."
Enter a name for the parameter and select its data type (e.g., Text, Decimal Number, Integer, Date/Time).
Optionally, set the default value and any available values (for dropdown selection).
Question: What is the role of Power BI Paginated Reports and when are they used?
Solution: Power BI Paginated Reports (formerly SQL Server Reporting Services or SSRS) are used for pixel-perfect, printable, and paginated reports. They are typically used for operational and transactional reporting scenarios where precise formatting and layout control are required, such as invoices, statements, or regulatory reports.
Question: What are the options available for managing query parameters in Power Query Editor?
Solution: Power Query Editor allows users to define and manage query parameters to dynamically control data loading and transformation. Parameters can be created from values in the data source, entered manually, or generated from expressions, providing flexibility and reusability in query design.
❤4
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: You have 2 minutes to create a Power BI visual.
Task: Show each department’s highest salary, but only display departments where the top salary exceeds $70,000.
𝗠𝗲: Challenge accepted!
Steps I followed in Power BI:
1. Load the dataset – Imported the employees table.
2. Created a DAX measure:
HighestSalary = MAX(employees[salary])
3. Added a table visual with department and HighestSalary.
4. Applied visual-level filter → HighestSalary > 70000
5. Formatted the values for readability.
Result? A clean, interactive report that highlights only high-paying departments!
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗧𝗶𝗽 𝗳𝗼𝗿 𝗝𝗼𝗯 𝗦𝗲𝗲𝗸𝗲𝗿𝘀:
It’s not about adding too many visuals — it’s about delivering clear, focused insights. Master DAX, filtering logic, and visual storytelling to stand out in interviews.
💬 Tap ❤️ for more!
Task: Show each department’s highest salary, but only display departments where the top salary exceeds $70,000.
𝗠𝗲: Challenge accepted!
Steps I followed in Power BI:
1. Load the dataset – Imported the employees table.
2. Created a DAX measure:
HighestSalary = MAX(employees[salary])
3. Added a table visual with department and HighestSalary.
4. Applied visual-level filter → HighestSalary > 70000
5. Formatted the values for readability.
Result? A clean, interactive report that highlights only high-paying departments!
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗧𝗶𝗽 𝗳𝗼𝗿 𝗝𝗼𝗯 𝗦𝗲𝗲𝗸𝗲𝗿𝘀:
It’s not about adding too many visuals — it’s about delivering clear, focused insights. Master DAX, filtering logic, and visual storytelling to stand out in interviews.
💬 Tap ❤️ for more!
❤6
𝗙𝗥𝗘𝗘 𝗖𝗮𝗿𝗲𝗲𝗿 𝗖𝗮𝗿𝗻𝗶𝘃𝗮𝗹 𝗯𝘆 𝗛𝗖𝗟 𝗚𝗨𝗩𝗜😍
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Hurry Up🏃♂️.....Limited Slots Available
Prove your skills in an online hackathon, clear tech interviews, and get hired faster
Highlightes:-
- 21+ Hiring Companies & 100+ Open Positions to Grab
- Get hired for roles in AI, Full Stack, & more
Experience the biggest online job fair with Career Carnival by HCL GUVI
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
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Interviewer: You have 2 minutes to solve a Power BI problem.
Task: Show total sales by region, but only for regions where total sales exceed $1,000,000.
Me: Let’s do it.
Steps I followed in Power BI:
1. Load the dataset - Imported the sales table with Region and Sales columns.
2. Create a DAX measure
TotalSales = SUM(sales[SalesAmount])
3. Add a visual - Inserted a table or bar chart. Added Region and TotalSales.
4. Apply filter logic - Visual-level filter. TotalSales greater than 1000000.
5. Format for clarity - Currency formatting. Sorted by TotalSales descending.
Result: A focused visual showing only high-performing regions. No noise. No extra visuals. Clear insight.
Power BI interview tip: Interviewers test speed and logic. They watch how you think, not how fancy your report looks. Practice these daily:
- Simple DAX measures
- Visual-level vs page-level filters
- Business-driven conditions
- Clean formatting
Double Tap ♥️ For More Power BI interview scenarios
Task: Show total sales by region, but only for regions where total sales exceed $1,000,000.
Me: Let’s do it.
Steps I followed in Power BI:
1. Load the dataset - Imported the sales table with Region and Sales columns.
2. Create a DAX measure
TotalSales = SUM(sales[SalesAmount])
3. Add a visual - Inserted a table or bar chart. Added Region and TotalSales.
4. Apply filter logic - Visual-level filter. TotalSales greater than 1000000.
5. Format for clarity - Currency formatting. Sorted by TotalSales descending.
Result: A focused visual showing only high-performing regions. No noise. No extra visuals. Clear insight.
Power BI interview tip: Interviewers test speed and logic. They watch how you think, not how fancy your report looks. Practice these daily:
- Simple DAX measures
- Visual-level vs page-level filters
- Business-driven conditions
- Clean formatting
Double Tap ♥️ For More Power BI interview scenarios
❤19
𝗧𝗼𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗚𝗲𝘁 𝗛𝗶𝗴𝗵 𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯 𝗜𝗻 𝟮𝟬𝟮𝟲😍
Opportunities With 500+ Hiring Partners
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📈 Start learning today, build job-ready skills, and get placed in leading tech companies.
Opportunities With 500+ Hiring Partners
𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸:- https://pdlink.in/4hO7rWY
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:- https://pdlink.in/4fdWxJB
📈 Start learning today, build job-ready skills, and get placed in leading tech companies.
30-Day Power BI Beginner Roadmap ✅📊
📚 Week 1: Foundations – Getting Started with Power BI Desktop
Days 1–3:
⦁ What is Power BI? Understand core components (Desktop, Service, Mobile).
⦁ Explore interface: ribbons, panes, views.
⦁ Install Power BI Desktop (free).
⦁ Connect to simple data (Excel, CSV).
⦁ Start using Power Query Editor for basic data cleaning and transformations.
Days 4–7:
⦁ Advanced data transformations (splitting, merging, unpivoting).
⦁ Manage missing data, errors.
⦁ Learn data modeling basics: table relationships, star schema.
⦁ Create simple visualizations (bar, line, pie charts).
⦁ Build your first dashboard with filters and slicers.
————————
📚 Week 2: Intermediate Concepts – DAX & Visuals
Days 8–14:
⦁ Learn DAX basics: SUM, AVERAGE, COUNT, calculated columns.
⦁ Create measures using CALCULATE, SUMX, filter and row contexts.
⦁ Explore time intelligence functions like TOTALYTD, SAMEPERIODLASTYEAR.
⦁ Use advanced visualizations (treemaps, maps).
⦁ Practice formatting, interactivity (drill-through, drill-down).
⦁ Experiment with parameters and “what-if” analysis.
⦁ Mid-week project: build a multi-page interactive dashboard with DAX.
————————
📚 Week 3: Power BI Service & Sharing
Days 15–21:
⦁ Understand Power BI Service and how it differs from Desktop.
⦁ Publish reports and create dashboards online.
⦁ Use Q&A natural language queries.
⦁ Set alerts and share dashboards with colleagues securely.
⦁ Learn about row-level security for data access control.
⦁ Schedule data refresh using gateways.
⦁ Explore Power BI Mobile app and embedding reports.
————————
📚 Week 4: Practice, Projects & Next Steps
Days 22–28:
⦁ Practice with diverse datasets from Kaggle or data.gov.
⦁ Complete an end-to-end capstone project: import, transform, model, analyze, visualize, publish.
⦁ Document your project and storytelling.
⦁ Get feedback from communities or peers.
⦁ Refine your data model and visuals based on feedback.
Days 29–30:
⦁ Join Power BI communities and user groups online.
⦁ Plan further learning: advanced DAX, Python/R integration, Power BI certifications.
————————
💡 Tips to stay on track:
⦁ Practice daily, even if just 30–60 mins.
⦁ Use free resources: Microsoft Docs, YouTube tutorials (like the 8-hour Power BI full course), interactive websites.
⦁ Build real-world projects to showcase skills on GitHub or LinkedIn.
Double Tap ♥️ For More 😊
📚 Week 1: Foundations – Getting Started with Power BI Desktop
Days 1–3:
⦁ What is Power BI? Understand core components (Desktop, Service, Mobile).
⦁ Explore interface: ribbons, panes, views.
⦁ Install Power BI Desktop (free).
⦁ Connect to simple data (Excel, CSV).
⦁ Start using Power Query Editor for basic data cleaning and transformations.
Days 4–7:
⦁ Advanced data transformations (splitting, merging, unpivoting).
⦁ Manage missing data, errors.
⦁ Learn data modeling basics: table relationships, star schema.
⦁ Create simple visualizations (bar, line, pie charts).
⦁ Build your first dashboard with filters and slicers.
————————
📚 Week 2: Intermediate Concepts – DAX & Visuals
Days 8–14:
⦁ Learn DAX basics: SUM, AVERAGE, COUNT, calculated columns.
⦁ Create measures using CALCULATE, SUMX, filter and row contexts.
⦁ Explore time intelligence functions like TOTALYTD, SAMEPERIODLASTYEAR.
⦁ Use advanced visualizations (treemaps, maps).
⦁ Practice formatting, interactivity (drill-through, drill-down).
⦁ Experiment with parameters and “what-if” analysis.
⦁ Mid-week project: build a multi-page interactive dashboard with DAX.
————————
📚 Week 3: Power BI Service & Sharing
Days 15–21:
⦁ Understand Power BI Service and how it differs from Desktop.
⦁ Publish reports and create dashboards online.
⦁ Use Q&A natural language queries.
⦁ Set alerts and share dashboards with colleagues securely.
⦁ Learn about row-level security for data access control.
⦁ Schedule data refresh using gateways.
⦁ Explore Power BI Mobile app and embedding reports.
————————
📚 Week 4: Practice, Projects & Next Steps
Days 22–28:
⦁ Practice with diverse datasets from Kaggle or data.gov.
⦁ Complete an end-to-end capstone project: import, transform, model, analyze, visualize, publish.
⦁ Document your project and storytelling.
⦁ Get feedback from communities or peers.
⦁ Refine your data model and visuals based on feedback.
Days 29–30:
⦁ Join Power BI communities and user groups online.
⦁ Plan further learning: advanced DAX, Python/R integration, Power BI certifications.
————————
💡 Tips to stay on track:
⦁ Practice daily, even if just 30–60 mins.
⦁ Use free resources: Microsoft Docs, YouTube tutorials (like the 8-hour Power BI full course), interactive websites.
⦁ Build real-world projects to showcase skills on GitHub or LinkedIn.
Double Tap ♥️ For More 😊
❤5🔥1
𝗧𝗼𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗢𝗳𝗳𝗲𝗿𝗲𝗱 𝗕𝘆 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲 & 𝗜𝗜𝗠 𝗠𝘂𝗺𝗯𝗮𝗶😍
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𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4tcUPia
Hurry..Up Only Limited Seats Available
Placement Assistance With 5000+ Companies
Deadline: 25th January 2026
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗔𝗜 :- https://pdlink.in/49UZfkX
𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:- https://pdlink.in/4pYWCEK
𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4tcUPia
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Interviewer: You have 2 minutes.
Task: Show monthly sales trend, but only for months where profit is positive.
Me: Challenge accepted!
Steps I followed in Power BI:
1. Load the dataset
Imported the sales table with Date, SalesAmount, and Profit.
2. Create DAX measures
TotalSales = SUM(sales[SalesAmount])
TotalProfit = SUM(sales[Profit])
3. Add a visual
Used a line chart.
Axis: Month from Date.
Values: TotalSales.
4. Apply filter logic
Visual-level filter.
TotalProfit greater than 0.
5. Improve readability
Month sorted by date.
Data labels enabled.
Result:
A clean trend line showing only profitable months.
Loss-making periods removed from analysis.
Power BI interview tip for you:
Interviewers want to see business filters, not raw data.
Always ask yourself, what should be excluded?
Master these fast:
- Time-based visuals
- Measure-driven filters
- Sorting dates correctly
- Hiding irrelevant data
This thinking separates report builders from analysts.
Task: Show monthly sales trend, but only for months where profit is positive.
Me: Challenge accepted!
Steps I followed in Power BI:
1. Load the dataset
Imported the sales table with Date, SalesAmount, and Profit.
2. Create DAX measures
TotalSales = SUM(sales[SalesAmount])
TotalProfit = SUM(sales[Profit])
3. Add a visual
Used a line chart.
Axis: Month from Date.
Values: TotalSales.
4. Apply filter logic
Visual-level filter.
TotalProfit greater than 0.
5. Improve readability
Month sorted by date.
Data labels enabled.
Result:
A clean trend line showing only profitable months.
Loss-making periods removed from analysis.
Power BI interview tip for you:
Interviewers want to see business filters, not raw data.
Always ask yourself, what should be excluded?
Master these fast:
- Time-based visuals
- Measure-driven filters
- Sorting dates correctly
- Hiding irrelevant data
This thinking separates report builders from analysts.
❤4
✅ Complete Power BI Roadmap in 2 Months
Month 1: Power BI Foundations
Week 1: Power BI Basics and Data Loading
- What Power BI does in analytics and business
- Power BI Desktop, Service, Mobile
- Import vs DirectQuery
- Connect Excel, CSV, SQL
- Data model overview
Outcome: You load data and understand the workflow.
Week 2: Data Cleaning with Power Query
- Power Query Editor
- Remove duplicates and nulls
- Change data types
- Split, merge, replace values
- Applied steps concept
Outcome: You clean messy data fast.
Week 3: Data Modeling Fundamentals
- Fact and dimension tables
- Star schema
- Relationships, cardinality, filter direction
- Active vs inactive relationships
Outcome: You build reliable models.
Week 4: DAX Basics
- Calculated columns vs measures
- SUM, COUNT, AVERAGE
- IF, SWITCH
- Basic CALCULATE usage
Outcome: You write basic business metrics.
Month 2: Analytics-Level Power BI
Week 5: Core DAX for Analytics
- CALCULATE in depth
- FILTER
- ALL, ALLEXCEPT
- Context basics, row vs filter
Outcome: You control calculations.
Week 6: Time Intelligence
- Date table creation
- TOTALYTD, SAMEPERIODLASTYEAR
- MoM and YoY growth
- Rolling averages
Outcome: You analyze trends over time.
Week 7: Visuals and Dashboards
- Bar, line, table, matrix
- KPI and card visuals
- Slicers and filters
- Drill down and drill through
Outcome: You build clear dashboards.
Week 8: Project and Interview Prep
- Build a sales or HR dashboard
- Business KPIs with DAX
- Optimize model and visuals
- Explain measures step by step
Outcome: You are Power BI interview ready.
Practice Ideas
- Microsoft Power BI sample datasets
- Kaggle business datasets
- Rebuild dashboards from YouTube case studies
Double Tap ♥️ For Detailed Explanation of Each Topic
Month 1: Power BI Foundations
Week 1: Power BI Basics and Data Loading
- What Power BI does in analytics and business
- Power BI Desktop, Service, Mobile
- Import vs DirectQuery
- Connect Excel, CSV, SQL
- Data model overview
Outcome: You load data and understand the workflow.
Week 2: Data Cleaning with Power Query
- Power Query Editor
- Remove duplicates and nulls
- Change data types
- Split, merge, replace values
- Applied steps concept
Outcome: You clean messy data fast.
Week 3: Data Modeling Fundamentals
- Fact and dimension tables
- Star schema
- Relationships, cardinality, filter direction
- Active vs inactive relationships
Outcome: You build reliable models.
Week 4: DAX Basics
- Calculated columns vs measures
- SUM, COUNT, AVERAGE
- IF, SWITCH
- Basic CALCULATE usage
Outcome: You write basic business metrics.
Month 2: Analytics-Level Power BI
Week 5: Core DAX for Analytics
- CALCULATE in depth
- FILTER
- ALL, ALLEXCEPT
- Context basics, row vs filter
Outcome: You control calculations.
Week 6: Time Intelligence
- Date table creation
- TOTALYTD, SAMEPERIODLASTYEAR
- MoM and YoY growth
- Rolling averages
Outcome: You analyze trends over time.
Week 7: Visuals and Dashboards
- Bar, line, table, matrix
- KPI and card visuals
- Slicers and filters
- Drill down and drill through
Outcome: You build clear dashboards.
Week 8: Project and Interview Prep
- Build a sales or HR dashboard
- Business KPIs with DAX
- Optimize model and visuals
- Explain measures step by step
Outcome: You are Power BI interview ready.
Practice Ideas
- Microsoft Power BI sample datasets
- Kaggle business datasets
- Rebuild dashboards from YouTube case studies
Double Tap ♥️ For Detailed Explanation of Each Topic
❤10
𝗜𝗻𝗱𝗶𝗮’𝘀 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗛𝗮𝗰𝗸𝗮𝘁𝗵𝗼𝗻 | 𝗔𝗜 𝗜𝗺𝗽𝗮𝗰𝘁 𝗕𝘂𝗶𝗹𝗱𝗮𝘁𝗵𝗼𝗻😍
Participate in the national AI hackathon under the India AI Impact Summit 2026
Submission deadline: 5th February 2026
Grand Finale: 16th February 2026, New Delhi
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-
https://pdlink.in/4qQfAOM
a flagship initiative of the Government of India 🇮🇳
Participate in the national AI hackathon under the India AI Impact Summit 2026
Submission deadline: 5th February 2026
Grand Finale: 16th February 2026, New Delhi
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-
https://pdlink.in/4qQfAOM
a flagship initiative of the Government of India 🇮🇳
Glad to see the amazing response on Power BI roadmap. ❤️
Today, let's start with the first topic of Power BI roadmap:
✅ What Power BI does in analytics and business
What Power BI is
- A business intelligence tool by Microsoft
- You use it to turn raw data into reports and dashboards
- It connects data, models it, and visualizes insights
Why companies use Power BI
- Track KPIs like revenue, profit, growth
- Monitor daily operations
- Share reports with teams in real time
- Replace manual Excel reporting
Where Power BI fits in analytics
- Data source: Excel, SQL, APIs
- Power BI sits after data collection
- You analyze, calculate, and present insights
- Decision-makers consume dashboards
Real business example
- Sales team tracks monthly revenue
- HR tracks attrition rate
- Finance tracks expenses vs budget
- Marketing tracks leads and conversions
Key components you should know
- Power BI Desktop: Build reports
- Power BI Service: Share and schedule refresh
- Visuals: Charts, tables, KPIs
- DAX: Business calculations
What Power BI is not
- Not a database
- Not a data entry tool
- Not a replacement for SQL
Your takeaway
- Power BI answers business questions
- You focus on insights, not raw data
- Every report should drive a decision
Today, let's start with the first topic of Power BI roadmap:
✅ What Power BI does in analytics and business
What Power BI is
- A business intelligence tool by Microsoft
- You use it to turn raw data into reports and dashboards
- It connects data, models it, and visualizes insights
Why companies use Power BI
- Track KPIs like revenue, profit, growth
- Monitor daily operations
- Share reports with teams in real time
- Replace manual Excel reporting
Where Power BI fits in analytics
- Data source: Excel, SQL, APIs
- Power BI sits after data collection
- You analyze, calculate, and present insights
- Decision-makers consume dashboards
Real business example
- Sales team tracks monthly revenue
- HR tracks attrition rate
- Finance tracks expenses vs budget
- Marketing tracks leads and conversions
Key components you should know
- Power BI Desktop: Build reports
- Power BI Service: Share and schedule refresh
- Visuals: Charts, tables, KPIs
- DAX: Business calculations
What Power BI is not
- Not a database
- Not a data entry tool
- Not a replacement for SQL
Your takeaway
- Power BI answers business questions
- You focus on insights, not raw data
- Every report should drive a decision
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Now, let's move to the next topic of Power BI roadmap:
✅ Power BI Components
Power BI is a Business Intelligence Platform
- Not a single tool, but a full reporting lifecycle platform
- Handles raw data to shared insights
- Different users do different work:
- Developers build reports
- Analysts manage data and logic
- Business users consume insights
Core Idea
- Build once, publish centrally, consume anywhere
Main Components of Power BI
1. Power BI Desktop
- Development environment for analysts and developers
- Focused on data modeling and report design
- Activities:
- Data connection
- Data cleaning
- Relationship creation
- DAX calculations
- Visual design
2. Power BI Service
- Distribution and governance layer for teams and organizations
- Focused on sharing and access control
- Activities:
- Report hosting
- Dashboard creation
- Scheduled refresh
- User permissions
- Security rules
3. Power BI Mobile
- Consumption layer for decision makers
- Focused on quick insights
- Activities:
- View dashboards
- KPI alerts
- Read-only access
Simple Mental Model
- Desktop builds
- Service manages
- Mobile views
Takeaway
- Power BI works as an ecosystem
- Each component solves a specific problem
- Together they support analytics at scale
Double Tap ♥️ For More
✅ Power BI Components
Power BI is a Business Intelligence Platform
- Not a single tool, but a full reporting lifecycle platform
- Handles raw data to shared insights
- Different users do different work:
- Developers build reports
- Analysts manage data and logic
- Business users consume insights
Core Idea
- Build once, publish centrally, consume anywhere
Main Components of Power BI
1. Power BI Desktop
- Development environment for analysts and developers
- Focused on data modeling and report design
- Activities:
- Data connection
- Data cleaning
- Relationship creation
- DAX calculations
- Visual design
2. Power BI Service
- Distribution and governance layer for teams and organizations
- Focused on sharing and access control
- Activities:
- Report hosting
- Dashboard creation
- Scheduled refresh
- User permissions
- Security rules
3. Power BI Mobile
- Consumption layer for decision makers
- Focused on quick insights
- Activities:
- View dashboards
- KPI alerts
- Read-only access
Simple Mental Model
- Desktop builds
- Service manages
- Mobile views
Takeaway
- Power BI works as an ecosystem
- Each component solves a specific problem
- Together they support analytics at scale
Double Tap ♥️ For More
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✅ Import vs DirectQuery in Power BI
• Why Power BI has different connection modes
• Data size varies
• Data freshness needs differ
• Performance expectations differ
• Security constraints differ
• Import mode
• Data loads into Power BI memory
• Stored in compressed format
• Fast report performance
• How it works: pull data from source, data sits in PBIX file, refresh updates data
• Use for: small-medium datasets, daily/hourly refresh, high-performance visuals
• Example: Excel sales data (2M rows), daily refresh at 7 AM
• DirectQuery mode
• Data stays in source system
• Power BI sends queries on demand
• No data stored in Power BI
• How it works: user interacts, Power BI generates SQL, database returns results
• Use for: large datasets, near real-time reporting, data security restrictions
• Example: Live SQL Server sales table, operations team monitors live metrics
• Key differences
• Performance. Import is faster
• Freshness. DirectQuery is fresher
• Load. Import loads Power BI memory. DirectQuery loads database
• Modeling. Import gives full DAX power
• Common mistakes
• Using DirectQuery for small data
• Ignoring database performance
• Mixing modes without need
• Your takeaway
• Choose Import for speed
• Choose DirectQuery for freshness
• Decision impacts design and cost
Double Tap ♥️ For More
• Why Power BI has different connection modes
• Data size varies
• Data freshness needs differ
• Performance expectations differ
• Security constraints differ
• Import mode
• Data loads into Power BI memory
• Stored in compressed format
• Fast report performance
• How it works: pull data from source, data sits in PBIX file, refresh updates data
• Use for: small-medium datasets, daily/hourly refresh, high-performance visuals
• Example: Excel sales data (2M rows), daily refresh at 7 AM
• DirectQuery mode
• Data stays in source system
• Power BI sends queries on demand
• No data stored in Power BI
• How it works: user interacts, Power BI generates SQL, database returns results
• Use for: large datasets, near real-time reporting, data security restrictions
• Example: Live SQL Server sales table, operations team monitors live metrics
• Key differences
• Performance. Import is faster
• Freshness. DirectQuery is fresher
• Load. Import loads Power BI memory. DirectQuery loads database
• Modeling. Import gives full DAX power
• Common mistakes
• Using DirectQuery for small data
• Ignoring database performance
• Mixing modes without need
• Your takeaway
• Choose Import for speed
• Choose DirectQuery for freshness
• Decision impacts design and cost
Double Tap ♥️ For More
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✅ Connecting Data Sources in Power BI
Power BI reads data from systems; it doesn't create data. The connection defines how data flows, and the source choice affects performance and refresh.
• Why This Step Matters
Wrong source leads to slow reports, wrong mode leads to refresh failures, and clean source saves modeling time.
• Main Data Sources
– Files: Excel, CSV
– Databases: SQL Server, MySQL, PostgreSQL
– Cloud sources: SharePoint, Google Sheets
• File-Based Sources
– Static files stored locally or in cloud
– Common in early analytics setups
– Easy to connect and test
– Excel: use structured tables, avoid merged cells
– CSV: plain text data, lightweight and fast; encoding and delimiter matter
• Database Sources
– Centralized structured storage
– Used in production systems
– Supports large volumes
– SQL connection basics: server name, database name, authentication method, Import or DirectQuery choice
• Cloud Sources
– Data hosted online
– Used by distributed teams
– Requires credentials and gateway sometimes
• Common Mistakes
– Connecting to raw, uncleaned files
– Changing column names after reports go live
– Mixing file and database logic poorly
• Your Takeaway
Always understand your source, choose the right connection mode, and stable sources create stable reports.
Double Tap ♥️ For More
Power BI reads data from systems; it doesn't create data. The connection defines how data flows, and the source choice affects performance and refresh.
• Why This Step Matters
Wrong source leads to slow reports, wrong mode leads to refresh failures, and clean source saves modeling time.
• Main Data Sources
– Files: Excel, CSV
– Databases: SQL Server, MySQL, PostgreSQL
– Cloud sources: SharePoint, Google Sheets
• File-Based Sources
– Static files stored locally or in cloud
– Common in early analytics setups
– Easy to connect and test
– Excel: use structured tables, avoid merged cells
– CSV: plain text data, lightweight and fast; encoding and delimiter matter
• Database Sources
– Centralized structured storage
– Used in production systems
– Supports large volumes
– SQL connection basics: server name, database name, authentication method, Import or DirectQuery choice
• Cloud Sources
– Data hosted online
– Used by distributed teams
– Requires credentials and gateway sometimes
• Common Mistakes
– Connecting to raw, uncleaned files
– Changing column names after reports go live
– Mixing file and database logic poorly
• Your Takeaway
Always understand your source, choose the right connection mode, and stable sources create stable reports.
Double Tap ♥️ For More
❤5
📈 Data Visualisation Cheatsheet: 13 Must-Know Chart Types ✅
1️⃣ Gantt Chart
Tracks project schedules over time.
🔹 Advantage: Clarifies timelines & tasks
🔹 Use case: Project management & planning
2️⃣ Bubble Chart
Shows data with bubble size variations.
🔹 Advantage: Displays 3 data dimensions
🔹 Use case: Comparing social media engagement
3️⃣ Scatter Plots
Plots data points on two axes.
🔹 Advantage: Identifies correlations & clusters
🔹 Use case: Analyzing variable relationships
4️⃣ Histogram Chart
Visualizes data distribution in bins.
🔹 Advantage: Easy to see frequency
🔹 Use case: Understanding age distribution in surveys
5️⃣ Bar Chart
Uses rectangular bars to visualize data.
🔹 Advantage: Easy comparison across groups
🔹 Use case: Comparing sales across regions
6️⃣ Line Chart
Shows trends over time with lines.
🔹 Advantage: Clear display of data changes
🔹 Use case: Tracking stock market performance
7️⃣ Pie Chart
Represents data in circular segments.
🔹 Advantage: Simple proportion visualization
🔹 Use case: Displaying market share distribution
8️⃣ Maps
Geographic data representation on maps.
🔹 Advantage: Recognizes spatial patterns
🔹 Use case: Visualizing population density by area
9️⃣ Bullet Charts
Measures performance against a target.
🔹 Advantage: Compact alternative to gauges
🔹 Use case: Tracking sales vs quotas
🔟 Highlight Table
Colors tabular data based on values.
🔹 Advantage: Quickly identifies highs & lows
🔹 Use case: Heatmapping survey responses
1️⃣1️⃣ Tree Maps
Hierarchical data with nested rectangles.
🔹 Advantage: Efficient space usage
🔹 Use case: Displaying file system usage
1️⃣2️⃣ Box & Whisker Plot
Summarizes data distribution & outliers.
🔹 Advantage: Concise data spread representation
🔹 Use case: Comparing exam scores across classes
1️⃣3️⃣ Waterfall Charts / Walks
Visualizes sequential cumulative effect.
🔹 Advantage: Clarifies source of final value
🔹 Use case: Understanding profit & loss components
💡 Use the right chart to tell your data story clearly.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Tap ♥️ for more!
1️⃣ Gantt Chart
Tracks project schedules over time.
🔹 Advantage: Clarifies timelines & tasks
🔹 Use case: Project management & planning
2️⃣ Bubble Chart
Shows data with bubble size variations.
🔹 Advantage: Displays 3 data dimensions
🔹 Use case: Comparing social media engagement
3️⃣ Scatter Plots
Plots data points on two axes.
🔹 Advantage: Identifies correlations & clusters
🔹 Use case: Analyzing variable relationships
4️⃣ Histogram Chart
Visualizes data distribution in bins.
🔹 Advantage: Easy to see frequency
🔹 Use case: Understanding age distribution in surveys
5️⃣ Bar Chart
Uses rectangular bars to visualize data.
🔹 Advantage: Easy comparison across groups
🔹 Use case: Comparing sales across regions
6️⃣ Line Chart
Shows trends over time with lines.
🔹 Advantage: Clear display of data changes
🔹 Use case: Tracking stock market performance
7️⃣ Pie Chart
Represents data in circular segments.
🔹 Advantage: Simple proportion visualization
🔹 Use case: Displaying market share distribution
8️⃣ Maps
Geographic data representation on maps.
🔹 Advantage: Recognizes spatial patterns
🔹 Use case: Visualizing population density by area
9️⃣ Bullet Charts
Measures performance against a target.
🔹 Advantage: Compact alternative to gauges
🔹 Use case: Tracking sales vs quotas
🔟 Highlight Table
Colors tabular data based on values.
🔹 Advantage: Quickly identifies highs & lows
🔹 Use case: Heatmapping survey responses
1️⃣1️⃣ Tree Maps
Hierarchical data with nested rectangles.
🔹 Advantage: Efficient space usage
🔹 Use case: Displaying file system usage
1️⃣2️⃣ Box & Whisker Plot
Summarizes data distribution & outliers.
🔹 Advantage: Concise data spread representation
🔹 Use case: Comparing exam scores across classes
1️⃣3️⃣ Waterfall Charts / Walks
Visualizes sequential cumulative effect.
🔹 Advantage: Clarifies source of final value
🔹 Use case: Understanding profit & loss components
💡 Use the right chart to tell your data story clearly.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Tap ♥️ for more!
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✅ Data model overview
What a data model is
- Logical structure of your data
- Defines how tables relate
- Controls how filters flow
- Decides accuracy of numbers
Why data modeling matters
Visuals depend on relationships
Wrong model gives wrong KPIs
Good model simplifies DAX
- Core idea
Reports do not read tables independently
They read relationships between tables
Basic elements of a data model
- Tables
- Fact tables store transactions
- Dimension tables store attributes
Example: FactSales (OrderID, Date, Amount), DimCustomer (CustomerID, Region), DimProduct (ProductID, Category)
Columns
- Keys connect tables
- Measures calculate results
- Attributes slice data
Relationships
- Connect tables using keys
- Define how filters move
- Control aggregation behavior
Key relationship properties: Cardinality (One to many), Filter direction (Single or both), Active vs inactive relationships
Simple mental model
Dimensions filter facts
Facts provide numbers
Common beginner mistakes
- Many to many relationships everywhere
- Using bidirectional filters blindly
- Mixing multiple fact tables without planning
Your takeaway
- Model before you visualize
- Clean relationships save time later
- Strong model equals correct insights
Double Tap ♥️ For More
What a data model is
- Logical structure of your data
- Defines how tables relate
- Controls how filters flow
- Decides accuracy of numbers
Why data modeling matters
Visuals depend on relationships
Wrong model gives wrong KPIs
Good model simplifies DAX
- Core idea
Reports do not read tables independently
They read relationships between tables
Basic elements of a data model
- Tables
- Fact tables store transactions
- Dimension tables store attributes
Example: FactSales (OrderID, Date, Amount), DimCustomer (CustomerID, Region), DimProduct (ProductID, Category)
Columns
- Keys connect tables
- Measures calculate results
- Attributes slice data
Relationships
- Connect tables using keys
- Define how filters move
- Control aggregation behavior
Key relationship properties: Cardinality (One to many), Filter direction (Single or both), Active vs inactive relationships
Simple mental model
Dimensions filter facts
Facts provide numbers
Common beginner mistakes
- Many to many relationships everywhere
- Using bidirectional filters blindly
- Mixing multiple fact tables without planning
Your takeaway
- Model before you visualize
- Clean relationships save time later
- Strong model equals correct insights
Double Tap ♥️ For More
❤5