15-Day Power BI for Data Engineering and Analytics Master Guide

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For 4–10 Years Experienced Data Engineers / BI Engineers / Data Analysts

Objective

This guide is designed to:

  • Build strong Power BI fundamentals

  • Understand reporting and dashboard development

  • Learn DAX and data modeling deeply

  • Build real-time analytical dashboards

  • Understand enterprise reporting architecture

  • Prepare for senior-level interviews

Target Audience:

  • Data Engineers

  • BI Developers

  • Power BI Developers

  • Reporting Engineers

  • Data Analysts

  • Analytics Engineers

Daily Time Commitment:

  • 3 Hours Per Day

  • 15 Days Total

Learning Strategy:

  • 20% Theory

  • 80% Hands-On Practice

Goal:

  • Build interactive dashboards

  • Understand DAX deeply

  • Create enterprise reports

  • Build optimized data models

  • Handle real-time reporting scenarios

Daily Learning Structure

Hour 1 – Learn Concepts

Focus on:

  • Understanding WHY Power BI concepts exist

  • Reporting architecture understanding

  • Data modeling concepts

  • Real-time analytics thinking

Avoid:

  • Memorizing visuals blindly

  • Watching endless tutorials

Hour 2 – Hands-On Development

Focus on:

  • Building dashboards

  • Writing DAX

  • Creating reports

  • Data transformations

  • Data modeling

Hour 3 – Real-Time Scenarios

Focus on:

  • Performance optimization

  • Security implementation

  • Reporting requirements

  • KPI dashboards

  • Enterprise reporting patterns

SECTION 1 – POWER BI BASICS

Topics:

  • What is Power BI

  • Why Power BI

  • Power BI Components

  • Reporting Architecture

  • Self-Service BI

WHAT IS POWER BI

Power BI is a business intelligence and data visualization platform by Microsoft.

Purpose:

  • Reporting

  • Dashboarding

  • Data visualization

  • Business analytics

  • KPI monitoring

WHY POWER BI IS USED

Problems Power BI Solves:

  • Manual reporting

  • Static Excel dashboards

  • Business analytics complexity

  • Interactive reporting needs

  • Enterprise visualization

POWER BI COMPONENTS

Topics:

  • Power BI Desktop

  • Power BI Service

  • Power BI Gateway

  • Power BI Report Server

  • Power BI Mobile

REPORTING ARCHITECTURE

Typical Flow:

Source Systems
↓
SQL / ADF / Databricks
↓
Data Warehouse / Delta Lake
↓
Power BI Dataset
↓
Reports
↓
Dashboards
↓
Business Users

SECTION 2 – POWER BI DESKTOP

Topics:

  • Interface overview

  • Report view

  • Data view

  • Model view

  • Visualizations pane

  • Filters pane

REPORT VIEW

Purpose:
Dashboard creation.

DATA VIEW

Purpose:
Inspect imported data.

MODEL VIEW

Critical Topic.

Purpose:
Relationship management.

SECTION 3 – DATA CONNECTIONS

Topics:

  • Import mode

  • DirectQuery

  • Live connection

  • Data sources

IMPORT MODE

Purpose:
Load data into Power BI.

Benefits:

  • Faster reports

  • Better performance

Limitations:

  • Dataset refresh dependency

DIRECTQUERY

Purpose:
Query source directly.

Benefits:

  • Near real-time reporting

Limitations:

  • Performance dependency on source

LIVE CONNECTION

Purpose:
Connect directly to SSAS/semantic models.

DATA SOURCES

Practice:

  • SQL Server

  • Azure SQL

  • Databricks

  • Excel

  • CSV

  • JSON

  • APIs

SECTION 4 – POWER QUERY (DATA TRANSFORMATION)

Topics:

  • Power Query Editor

  • Data cleaning

  • Transformations

  • Merge queries

  • Append queries

  • M language basics

POWER QUERY

Purpose:
Data transformation before reporting.

Real-Time Usage:

  • Cleansing

  • Standardization

  • Filtering

  • Merging datasets

COMMON TRANSFORMATIONS

Practice:

  • Remove duplicates

  • Change data types

  • Split columns

  • Replace values

  • Pivot/unpivot

  • Grouping

MERGE QUERIES

Purpose:
Join datasets.

APPEND QUERIES

Purpose:
Combine datasets vertically.

SECTION 5 – DATA MODELING

Topics:

  • Relationships

  • Cardinality

  • Star schema

  • Snowflake schema

  • Fact tables

  • Dimension tables

STAR SCHEMA

Critical Interview Topic.

Benefits:

  • Faster reporting

  • Simpler relationships

  • Better performance

FACT TABLES

Purpose:
Store measures.

Examples:

  • Sales

  • Transactions

  • Calls

DIMENSION TABLES

Purpose:
Store descriptive attributes.

Examples:

  • Customer

  • Employee

  • Product

RELATIONSHIPS

Topics:

  • One-to-many

  • Many-to-many

  • Active relationships

  • Inactive relationships

SECTION 6 – DAX (DATA ANALYSIS EXPRESSIONS)

Topics:

  • Measures

  • Calculated columns

  • Calculated tables

  • Aggregations

  • Time intelligence

DAX

Most Important Power BI Topic.

Purpose:
Business calculations.

MEASURES

Purpose:
Dynamic calculations.

Examples:

  • Total sales

  • Average revenue

  • Profit margin

CALCULATED COLUMNS

Purpose:
Row-level calculations.

IMPORTANT DAX FUNCTIONS

Topics:

  • SUM

  • COUNT

  • DISTINCTCOUNT

  • CALCULATE

  • FILTER

  • ALL

  • RELATED

  • IF

  • SWITCH

  • DIVIDE

TIME INTELLIGENCE FUNCTIONS

Critical Topic.

Functions:

  • TOTALYTD

  • SAMEPERIODLASTYEAR

  • DATEADD

  • DATESMTD

Use Cases:

  • Year-over-year growth

  • Monthly trends

  • Running totals

CALCULATE FUNCTION

Most Important DAX Function.

Purpose:
Modify filter context.

FILTER CONTEXT VS ROW CONTEXT

Critical Interview Topic.

Understand:

  • Row context

  • Filter context

  • Context transition

SECTION 7 – VISUALIZATIONS

Topics:

  • Bar charts

  • Line charts

  • Pie charts

  • Tables

  • Matrix

  • KPI cards

  • Maps

  • Slicers

KPI CARDS

Purpose:
Show business metrics.

Examples:

  • Revenue

  • Profit

  • Customer count

MATRIX VISUALS

Purpose:
Hierarchical reporting.

SLICERS

Purpose:
Interactive filtering.

DRILLTHROUGH

Purpose:
Detailed analysis.

TOOLTIPS

Purpose:
Enhanced report interaction.

SECTION 8 – EMPLOYEE REPORTING DASHBOARDS

Topics:

  • HR dashboards

  • Attendance dashboards

  • Payroll reporting

  • Performance reporting

EMPLOYEE DASHBOARD REQUIREMENTS

Metrics:

  • Employee count

  • Department-wise employees

  • Attrition rate

  • Salary distribution

  • Attendance percentage

  • Leave utilization

  • Promotion trends

  • Hiring trends

HR DASHBOARD

Visuals:

  • Employee distribution

  • Gender diversity

  • Experience bands

  • Joining trends

  • Attrition analysis

ATTENDANCE DASHBOARD

Metrics:

  • Average attendance

  • Absent percentage

  • Late logins

  • Overtime analysis

PAYROLL DASHBOARD

Metrics:

  • Salary trends

  • Bonus analysis

  • Department payroll

  • Cost analysis

PERFORMANCE DASHBOARD

Metrics:

  • KPI achievement

  • Top performers

  • Team performance

  • Goal completion

SECTION 9 – SECURITY

Topics:

  • Row-Level Security (RLS)

  • Workspace roles

  • Dataset permissions

  • Gateway configuration

ROW-LEVEL SECURITY

Critical Enterprise Topic.

Purpose:
Restrict data visibility.

Examples:

  • Region-specific access

  • Department-specific access

WORKSPACE ROLES

Roles:

  • Admin

  • Member

  • Contributor

  • Viewer

GATEWAY

Purpose:
Connect on-premises data.

Topics:

  • Personal gateway

  • Enterprise gateway

SECTION 10 – POWER BI SERVICE

Topics:

  • Publishing reports

  • Dashboards

  • Apps

  • Workspaces

  • Refresh schedules

PUBLISHING REPORTS

Practice:

  • Publish reports

  • Configure refresh

  • Share dashboards

WORKSPACES

Purpose:
Collaboration and deployment.

APPS

Purpose:
Enterprise report distribution.

REFRESH SETTINGS

Critical Topic.

Types:

  • Scheduled refresh

  • Incremental refresh

  • On-demand refresh

INCREMENTAL REFRESH

Purpose:
Refresh only changed data.

Benefits:

  • Faster refresh

  • Reduced load

SECTION 11 – PERFORMANCE OPTIMIZATION

Topics:

  • Query optimization

  • DAX optimization

  • Model optimization

  • Aggregations

DAX OPTIMIZATION

Best Practices:

  • Avoid unnecessary calculated columns

  • Use measures efficiently

  • Reduce row context complexity

MODEL OPTIMIZATION

Topics:

  • Star schema

  • Remove unused columns

  • Proper relationships

  • Reduce cardinality

PERFORMANCE ANALYZER

Purpose:
Identify slow visuals.

SECTION 12 – REAL-TIME REPORTING ARCHITECTURE

Topics:

  • Enterprise reporting flow

  • Databricks integration

  • Azure SQL integration

  • ADF integration

ENTERPRISE REPORTING FLOW

Source Systems
↓
ADF / Databricks
↓
Delta Lake / Azure SQL
↓
Power BI Dataset
↓
Reports
↓
Dashboards
↓
Executives / Business Teams

SECTION 13 – REAL-TIME PROJECT STRUCTURE

Typical Power BI Project Structure:

project/
│
├── datasets/
│ ├── sales_dataset.pbix
│ ├── employee_dataset.pbix
│
├── reports/
│ ├── executive_dashboard.pbix
│ ├── hr_dashboard.pbix
│ └── finance_dashboard.pbix
│
├── dax/
│ └── dax_measures.sql
│
├── documentation/
│ └── report_design.docx
│
└── source_files/
├── csv/
└── excel/

SECTION 14 – MID-LEVEL PROJECTS

PROJECT 1 – SALES DASHBOARD

Requirements:

  • Revenue trends

  • Region-wise sales

  • Top products

  • Customer analysis

Concepts Used:

  • DAX

  • KPIs

  • Slicers

  • Time intelligence

PROJECT 2 – EMPLOYEE ANALYTICS DASHBOARD

Requirements:

  • Employee count

  • Attrition trends

  • Attendance analysis

  • Salary analysis

Concepts Used:

  • Relationships

  • DAX measures

  • RLS

  • Drillthrough

PROJECT 3 – CALL CENTER DASHBOARD

Requirements:

  • SLA metrics

  • Agent performance

  • Escalation analysis

  • Resolution trends

Concepts Used:

  • Aggregations

  • KPIs

  • Filters

  • Conditional formatting

PROJECT 4 – FINANCE DASHBOARD

Requirements:

  • Profit analysis

  • Expense tracking

  • Budget vs actual

  • Forecasting trends

Concepts Used:

  • Time intelligence

  • DAX optimization

  • Matrix visuals

PROJECT 5 – EXECUTIVE KPI DASHBOARD

Requirements:

  • CEO summary dashboard

  • Business KPIs

  • Trend analytics

  • Drillthrough reports

Concepts Used:

  • Advanced DAX

  • KPI cards

  • Dynamic filtering

SECTION 15 – POWER BI INTERVIEW QUESTIONS

BASIC QUESTIONS

  1. What is Power BI?

  2. Difference between Import and DirectQuery.

  3. What is DAX?

  4. Difference between measure and calculated column.

  5. What is Power Query?

  6. What is star schema?

  7. What are relationships?

  8. What is RLS?

  9. What is Power BI Gateway?

  10. What are dashboards?

INTERMEDIATE QUESTIONS

  1. Explain filter context.

  2. Explain row context.

  3. Explain CALCULATE function.

  4. Explain time intelligence.

  5. Explain incremental refresh.

  6. Explain DirectQuery limitations.

  7. Explain DAX optimization.

  8. Explain model optimization.

  9. Explain Power BI Service.

  10. Explain security implementation.

ADVANCED QUESTIONS

  1. Design enterprise reporting architecture.

  2. Optimize slow Power BI reports.

  3. Design HR analytics dashboard.

  4. Handle large datasets efficiently.

  5. Implement row-level security.

  6. Explain DAX performance optimization.

  7. Explain enterprise deployment strategy.

  8. Integrate Power BI with Databricks.

  9. Design executive KPI dashboards.

  10. Explain production troubleshooting strategy.

SECTION 16 – 15-DAY EXECUTION PLAN

WEEK 1 – FOUNDATION

Day 1

  • Power BI basics

  • Components

  • Reporting architecture

Day 2

  • Power BI Desktop

  • Report/Data/Model views

Day 3

  • Import mode

  • DirectQuery

  • Data connections

Day 4

  • Power Query

  • Data transformations

Day 5

  • Data modeling

  • Relationships

  • Star schema

Day 6

  • DAX basics

  • Measures

  • Calculated columns

Day 7

  • Mini dashboard project

WEEK 2 – ADVANCED POWER BI

Day 8

  • Advanced DAX

  • CALCULATE

  • FILTER

  • Time intelligence

Day 9

  • Visualizations

  • KPI dashboards

  • Drillthrough

Day 10

  • Employee reporting dashboards

  • HR analytics

Day 11

  • Power BI Service

  • Workspaces

  • Publishing

Day 12

  • Security

  • RLS

  • Gateway

Day 13

  • Performance optimization

  • Incremental refresh

Day 14

  • Mid-level projects

Day 15
FINAL MOCK INTERVIEW + REVISION

REAL-TIME BEST PRACTICES

Always Follow:

  • Use star schema

  • Optimize DAX

  • Avoid unnecessary calculated columns

  • Use measures properly

  • Reduce dataset size

  • Implement RLS

  • Use incremental refresh

  • Optimize visuals

  • Remove unused columns

  • Use proper naming conventions

MOST IMPORTANT SKILLS FOR SENIOR ENGINEERS

You must become strong in:

  • DAX optimization

  • Data modeling

  • Reporting architecture

  • KPI dashboard design

  • Security implementation

  • Enterprise reporting

  • Performance tuning

  • Real-time analytics

  • Business storytelling

  • Executive reporting

FINAL INTERVIEW EXPECTATIONS

At 4–10 years experience, interviewers expect:

  • Strong DAX knowledge

  • Data modeling expertise

  • Dashboard design capability

  • Enterprise reporting understanding

  • Performance optimization mindset

  • Security implementation knowledge

  • Real-time reporting understanding

  • Power BI Service knowledge

  • Databricks + Power BI integration understanding

They do NOT expect only visualization knowledge.

They expect:

  • Analytical thinking

  • Business understanding

  • Reporting architecture capability

  • Optimization mindset

  • Enterprise BI engineering understanding

END OF DOCUMENT

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