For Next-Gen Cloud BI & Data Engineers

Fabric Data Engineering + Power BI + Unified Cloud + AI Automation
The Future of Data Engineering for Next-Gen Professionals.

This course is for professionals ready to lead the future of data engineering. Master end-to-end data workflows with Fabric, including data ingestion, Power BI, AI automation, and tools like Lakehouse, KQL Analytics, and Python. Whether you're new to data engineering or looking to advance your skills, this course equips you with the expertise to thrive in an ever-changing field.

Tools You'll Learn

Visualization Tool

Power BI

App Development

Power Apps

Automation Tool

Power Automate

Cloud Workflows

Data Pipelines (Fabric)

Warehouse Tool

Data Warehouse (Fabric)

Data Modeling

DAX

Data Prep Tool

Dataflows Gen2

Pipeline Tool

Data Factory (in Fabric)

Cloud Platform

Microsoft Fabric

Query Language

SQL / KQL

Lakehouse Tool

Lakehouse (Fabric OneLake)

Scripting Language

Python

In just 8 weeks, you'll be able to:

Build End-to-End Data Pipelines – Orchestrate data workflows using Data Factory and Eventstream.

Manage Cloud Data Infrastructure – Use OneLake and Data Warehouse for seamless data storage and retrieval.

Create Advanced BI Dashboards – Develop interactive reports with Power BI and DAX.

Automate Data Workflows – Streamline processes with Power Automate and Dataflows Gen2.

Perform Advanced Data Analytics – Leverage KQL Analytics for real-time insights.

Integrate BI with Apps – Connect Power Apps, Power Automate, and Power BI for end-to-end automation.

In just 8 weeks, you'll be able to:

Build End-to-End Data Pipelines – Orchestrate data workflows using Data Factory and Eventstream.

Manage Cloud Data Infrastructure – Use OneLake and Data Warehouse for seamless data storage and retrieval.

Create Advanced BI Dashboards – Develop interactive reports with Power BI and DAX.

Automate Data Workflows – Streamline processes with Power Automate and Dataflows Gen2.

Perform Advanced Data Analytics – Leverage KQL Analytics for real-time insights.

Integrate BI with Apps – Connect Power Apps, Power Automate, and Power BI for end-to-end automation.

Ideal For:

Cloud Data Engineers

Building and managing scalable data pipelines with Fabric.

BI Professionals

Enhancing cloud-based BI skills and automation.

Data Analysts

Mastering data engineering workflows and cloud analytics.

Automation Specialists

Streamlining data processes and reporting with AI tools.

Enterprise Architects

Designing scalable cloud-native data solutions.

Tech Leaders & Decision Makers

Adopting advanced data engineering tools for business transformation.

A snapshot of what you'll be learning in 8-weeks.

Course Syllabus Overview

Advanced Charting Techniques

The Science of Chart Choice

Decode the intent behind visuals: comparison, distribution, trend, or composition
Know when to use Clustered vs Stacked vs 100% Column Charts
Choose the right axis, scale, and layout based on your message
Avoid misleading visuals and cluttered dashboards

Smart Charting Techniques

Master column, bar, line, area, and combo charts with use-case depth
Conditional charts to highlight exceptions, variances, or risk points
Use formulas to drive attention: Top N, outliers, or KPI status

Data Storytelling Framework

Learn the 3-layer structure: Data → Insight → Impactful Visual
Build visuals that answer business questions, not just decorate reports
Turn dry tables into executive-ready visuals

Interactive & Dynamic Charting

Drop-down-driven visuals for user-led exploration
Pivot-linked visuals for real-time insights
Use slicers and timelines like a pro

Design for Interpretation

Clean layout, minimal ink, maximum clarity
Design tricks: focus colors, white space, custom labels, layering
Chart templates for consistency across reports

Quick Excel brush-up

Fundamentals Of Functions & Formulas Quick techniques to work with formulas & functions DATE AND TIME FUNCTIONS For data and time calculations

Today, Now
Day, Month, Year
Date, DateDif, DateAdd
EOMonth, Weekday
Workdays, NetWorkdays, Edate

TEXT FUNCTIONS For data transformations

Upper, Lower, Proper
Left, Mid, Right
Trim, Len
Concatenate
Find, Substitute

LOGICAL FUNCTIONS For conditional statement development

Nested If ( And Conditions , Or Conditions )
Alternative Solutions for Complex IF Conditions to make work simple
And, Or, Not

MATHEMATICAL FUNCTIONS For fundamental analysis

Sum, Count, CountA, Average, AverageA, Max & Min
SumIf, SumIfs
CountIf, CountIfs
AverageIf, AverageIfs
MaxIfs, MinIFs

LOOKUP FUNCTIONS For joins with multiple tables

Vlookup / HLookup
Match
Dynamic Two Way Lookup
Creating Smooth User Interface Using Lookup
Offset
Index
Dynamic Worksheet linking using Indirect

ERROR HANDLING FUNCTIONS For handling errors in functions

Power BI

Building Blocks of Power BI

Visualizations
Datasets
Reports
Dashboards
Tiles

Building Your First Power BI Report

Connect to Data Sources in Power BI Desktop
Clean and Transform Your Data With the Query Editor
Create a report in Power BI Desktop
Publish the report in the Power BI service

Data Modelling with Power BI

Fundamentals of Modelling
How to Manage Your Data Relationships
Create Calculated Columns
Optimizing Data Models for Better Visuals
Create measures and work with time-based functions
Create Calculated Tables
Explore Time-Based Data

Visualizations

Create and Customize Simple Visualizations

Building compelling data visualizations

Identify metrics and pair them with appropriate data visuals
Using slicers
Creating Map Visualizations
Creating Tables and Matrixes
Creating Waterfall and Funnel Charts
Using Gauges and Single Number Cards
Charting Options including Formatting with Colors, Shapes, Text Boxes, Images, etc.

Designing User-friendly reports

Customize themes
Create versatile layouts for your reports
Design principles to reduce noise and highlight data stories

Creating interactive reports for data exploration

Filtering & drilling for insights
Difference between filters & slicers
Filter pane for reporting needs

Power Query

Overview

Introduction
Loading & Refresh
Combine data from multiple data sources

Data Transformation

Editing Queries Created with Power Query
Editing Column Headers in Power Query
Splitting Column Data with Power Query
Sorting Data
Multi-Level Sorting
Filtering Data
Aggregate data from a column
Insert a custom column into a table
Merge columns
Remove columns
Remove rows with errors
Promote a row to column headers
Transforming Text Values
Replacing Data
Using the Fill command
Pivot and Unpivot Column
Transpose Query Data
Pivot Column Command in Action
Unpivot Columns Command
Grouping Data
Create a Duplicate Query
Group and Summarize Data
Advanced Data Grouping
Working with multiple sources in Power Query
Multiple Excel Tables
Expand a column containing an associated table
Understanding Table Relationships
Merging Queries

Loading Power Query Data to Destinations

Familiarity with the Load & Refresh Settings
Loading it to Workbook
Loading it to Data Model

DAX Functions

Overview

What is DAX?
Data Types
Table-Valued Functions
Building a Calendar Table
Date and Time Functions
Filter Functions
Information Functions
Logical Functions
Mathematical and Trigonometric Functions
Statistical Functions
Text Functions
Time Intelligence Functions
Creating Advanced DAX Measures With Advanced DAX Functions

Creating Advanced Dax Measures With Advanced Dax Functions

Calculate()
All()
Filter()
IF()
Switch()
SumX()

Evaluation Context

Filter Context
Row Context
Using RELATED in a Row Context
Filters and Relationships
USERELATIONSHIP

Hierarchies in DAX Querying with DAX Relationships

One-to-Many Relationships
Many-to-Many Relationships

Advanced DAX Functions

ADVANCED DAX FUNCTIONS ADVANCED CONTEXT CONCEPTS

Understanding and Debugging Context Transition
Expanded Tables and Filter Propagation
Using VAR for Performance and Clarity
Using TREATAS() to Apply Filters Between Unrelated Tables
Virtual Relationships using DAX
Shadow Filters and Filter Overriding Techniques

ADVANCED CALCULATION PATTERNS

Running Totals with Custom Filter Logic
Rolling Averages (e.g., 7-day, 12-month)
Year-over-Year (YoY), Quarter-over-Quarter (QoQ), MoM with Non-Standard Calendars
Top N Reporting with Others Grouping
Parent-Child Hierarchy Navigation using PATH, PATHITEM, PATHLENGTH
Budget vs. Actual Comparison Patterns
Custom Grouping (Bucketing) in DAX

ADVANCED TIME INTELLIGENCE

Semi-Additive Measures (e.g., Closing Balance, Opening Balance)
Workdays and Custom Holiday Calendars
Dynamic Period Selection (MTD, QTD, YTD) based on Slicers
Cumulative Totals Across Multiple Tables or Years

ADVANCED FILTER + CALCULATE PATTERNS

Multiple Filters in a Single CALCULATE
Using NOT, EXCEPT, INTERSECT inside CALCULATE
Combine CALCULATE with FILTER(), VALUES(), ALLSELECTED(), KEEPFILTERS()

RELATIONSHIP MODELING TECHNIQUES

Virtual Relationships using DAX (TREATAS, LOOKUPVALUE)
USERELATIONSHIP vs. CROSSFILTER
Handling Bi-Directional Relationships with Care
Many-to-Many Solutions using DAX with Bridge Tables

PERFORMANCE OPTIMIZATION

Understanding and Using DAX Studio and VertiPaq Analyzer
Optimizing Calculated Columns vs. Measures
Query Plans and Storage Engine vs. Formula Engine
Cardinality and its impact on performance
Avoiding common DAX performance anti-patterns (e.g., misuse of SUMX inside FILTER)

DEBUGGING AND TESTING

Using DEFINE MEASURE and EVALUATE in DAX Studio
Evaluating DAX logic step-by-step with VAR and RETURN
Tools: Performance Analyzer in Power BI

SPECIAL FUNCTIONS AND USE CASES

GENERATE(), GENERATEALL(), ADDCOLUMNS()
SUMMARIZE(), SUMMARIZECOLUMNS(), GROUPBY()
ISINSCOPE(), SELECTEDVALUE() vs VALUES()
RANKX(), TOPN(), PERCENTILEX.INC
CONTAINS(), LOOKUPVALUE(), RELATEDTABLE()

SQL Querying

Introduction to MS-SQL

Creating a Database
Understanding Tables and Creating Tables
Inserting, Updating and Deleting Data
Querying Data
Filtering Data
Grouping Data
Ordering Data
Column Aliases
Table Aliases

DDL INSIGHTS

CREATE TABLE
Dropping Objects
CREATE INDEX
TEMPORARY OBJECTS
Object Naming and Dependencies

SELECT STATEMENTS

Simple SELECTs
Calculated and Derived Fields
SELECT TOP / BOTTOM Records
Derived Tables
Joins
Predicates
Subqueries
Aggregate Functions
GROUP BY and HAVING
UNION
ORDER BY

SQL Programming

DDL INSIGHTS

CREATE TABLE
Dropping Objects
TEMPORARY OBJECTS
Object Naming and Dependencies

INTRODUCTION TO SQL PROGRAMMING (T-SQL)

What is T-SQL?
Differences between SQL Querying and SQL Programming
Benefits of procedural SQL
Use cases in reporting, automation, and ETL

VARIABLES AND CONTROL STRUCTURES

Declaring and Using Variables (DECLARE, SET)
Conditional Logic: IF…ELSE
Loops: WHILE, BREAK, CONTINUE
Error Handling: TRY…CATCH, THROW
GOTO statement (rare but useful for certain scenarios)

USER-DEFINED FUNCTIONS (UDFs)

Scalar-Valued Functions
Table-Valued Functions (Inline and Multi-statement)
Best practices for performance
Use in SELECT, WHERE, JOIN clauses

STORED PROCEDURES

Creating and Executing Stored Procedures
Input Parameters, Output Parameters
Reusability and Modularity
Nested Stored Procedures
Use in ETL and Reporting pipelines

TEMPORARY AND TABLE VARIABLES

Temporary Tables (#Temp, ##GlobalTemp)
Table Variables (DECLARE @TableVar TABLE)
Differences, Use Cases, and Scope
CTEs (Common Table Expressions)

CURSORS

Introduction to Cursors
Declaring and Using Cursors
Static vs Dynamic Cursors
Use Cases and Performance Considerations

DYNAMIC SQL

Constructing SQL Statements on the Fly
Executing with EXEC() and sp_executesql

TRANSACTIONS AND ERROR HANDLING

Introduction to Transactions
BEGIN TRAN, COMMIT, ROLLBACK
Nesting Transactions
Isolation Levels
Locking and Blocking

TRIGGERS

AFTER INSERT, AFTER UPDATE, AFTER DELETE
INSTEAD OF Triggers
Auditing Changes
Performance considerations

TESTING AND DEBUGGING

PRINT Statements
RAISERROR for debugging
SQL Server Profiler / Extended Events (if applicable)
Debugging in SQL Server Management Studio (SSMS)

Power BI Administration

Objective: Get hands-on experience with advanced administration settings, permissions, Data refresh times etc.

Overview

Publishing Power BI Reports
Creating & Managing Workspaces and Its Access
Creating & Managing Dashboard and Its Access
Installing & Configuring Data gateway
Scheduling and configuring data refresh
Managing & Reusing Datasets
Scheduling Report Alerts
Setting up Row Level Permissions
Managing Users & Audit Log
Custom Branding Power BI For your Organization
Adding Custom Visuals for your Organization

Microsoft Fabric and Power BI Ecosystem

Introduction to Microsoft Fabric architecture and vision
Understanding the unified platform concept of Fabric
Overview of Fabric modules: Lakehouse, Warehouse, Real-Time Analytics, Data Activator, Data Science
Exploring OneLake as the single data lake for all workloads
Introduction to DirectLake and how it changes Power BI performance
Power BI's deep integration into the Fabric platform
Comparison of Fabric with legacy tools (Synapse, Dataflows, Data Factory)
Navigating Fabric workspaces, domains, and permissions
Understanding Fabric's role in end-to-end data lifecycle
Licensing structure and Fabric capacities
Fabric as an evolution of Azure Synapse and Power BI Premium
Cross-module data sharing within Fabric
Use of Notebooks and Spark runtime for data professionals
Understanding Fabric in the context of business intelligence
Connecting Power BI Desktop to Fabric datasets and models

Microsoft Fabric: Data Modeling with Fabric Lakehouse

Introduction to Lakehouse and its architecture
Differences between Lakehouse and traditional data lakes
Creating Lakehouse objects: managed vs unmanaged tables
Loading data using Dataflows Gen2 into Lakehouse
Delta Lake format and its benefits for performance and reliability
Schema management and table partitioning
Working with notebooks in Lakehouse for transformations
Using Spark to process and refine raw data
Connecting Power BI to Lakehouse via DirectLake
Creating semantic models from Lakehouse tables
Best practices for folder structures and naming conventions
Lakehouse security and access control in Fabric
Performance optimization for large data models in Lakehouse
Versioning and auditability in Lakehouse data
Integrating Lakehouse data into Power BI dashboards

Microsoft Fabric: Data Modeling with Fabric Data Warehouse

Introduction to the Fabric Data Warehouse module
Understanding structured modeling vs Lakehouse
Creating Warehouse tables, views, stored procedures
Building star schemas and dimensional models
Data ingestion into Warehouse using pipelines or Dataflows
Best practices for table relationships and indexing
Optimizing DAX performance on Warehouse models
Creating and querying views for report consumption
Understanding DirectQuery vs DirectLake vs Import modes
Connecting Power BI to Fabric Data Warehouse
Warehouse data governance and role-based access control
Integrating Warehouse and Lakehouse models in one dataset
Use cases for Warehouse vs Lakehouse
Monitoring and auditing Warehouse usage
Data lineage and impact analysis within Fabric Warehouse

Microsoft Fabric: Developing Reports and Dashboards Fabrics

Connecting Power BI to Lakehouse and Warehouse datasets
Building semantic models with measures, columns, hierarchies
Designing star schema datasets for optimal performance
Creating dynamic visuals: charts, tables, KPI cards
Using bookmarks, slicers, drillthrough, and tooltips
Creating interactive navigation experiences in Power BI
Implementing row-level and object-level security
Using custom visuals for advanced reporting needs
Formatting reports with branding and design standards
Designing mobile-friendly Power BI dashboards
Creating paginated reports for pixel-perfect outputs
Embedding reports in web apps or Teams environments
Using Q&A and AI visuals for smart exploration
Performance tuning with Aggregations and Composite Models
Publishing, sharing, and certifying reports within Fabric workspaces

Microsoft Fabric: Real-Time Analytics in Fabric

Introduction to KQL (Kusto Query Language)
Creating Real-Time Analytics databases in Fabric
Ingesting streaming data from multiple sources
Defining data ingestion policies for real-time datasets
Querying KQL databases from Power BI
Designing dashboards for real-time metrics
Use cases: IoT, web analytics, stock prices, sales live feed
Setting up alerts and thresholds with Data Activator
Comparing Real-Time Analytics with traditional BI
Managing ingestion latency and data freshness
Visualizing real-time vs near real-time data
Securing KQL datasets and managing access
Building composite models with real-time + historical data
Scaling real-time dashboards with large volume streams
Monitoring and logging for KQL and streaming performance

Microsoft Fabric: Advanced Topics

Power BI Governance in Fabric: certification, endorsement, monitoring
Deployment pipelines for Dev ➝ Test ➝ Prod
Workspace and artifact-level permission strategies
Advanced RLS and OLS implementations
Performance optimization with VertiPaq Analyzer and DAX Studio
Version control and change tracking in Fabric environments
Incremental refresh in DirectLake and hybrid models
Power BI Composite and Hybrid models explained
Creating semantic calculation groups and perspectives
Automating report workflows using Power Automate
Using Data Activator for rule-based triggers and alerts
Audit logging and activity monitoring in Fabric tenant
Capacity management and Fabric licensing optimization
Using Fabric with external tools: Excel, Azure Data Explorer, Synapse
Building reusable report templates and model layers for enterprise use

Data Engineering: Microsoft Fabric Enterprise Architecture and Integration

Deep dive into Microsoft Intelligent Data Platform and Fabric’s role
OneLake metadata, multi-format storage, and virtualization
Cross-domain architecture and enterprise data mesh strategies
Fabric vs Synapse vs Azure Data Lake Gen2 vs Databricks: Advanced comparison
Advanced workspace security architecture and cross-tenant collaboration
Resource governance with Fabric capacities and metrics analysis
Managed identity integration and token-based access with Azure AD
DevOps enablement using YAML pipelines and Fabric REST APIs
Establishing CI/CD frameworks across Fabric workspaces
Enterprise project structuring and naming conventions
Hybrid deployment models: Fabric + Azure Synapse + Databricks
Licensing optimization and multi-sku capacity design
Cross-region failover design with global distribution
Integration with Microsoft Purview for data cataloging and governance
Enterprise-wide telemetry, lineage, and impact analysis in Fabric

Data Engineering: Advanced Data Ingestion and Workflow Orchestration

Designing scalable multi-source ingestion using Pipelines
Handling unstructured, semi-structured, and nested JSON/XML ingestion
High-throughput ingestion patterns with partition and parallelism
Data movement between Lakehouse, Warehouse, and external stores
Metadata-driven ingestion framework using dynamic mapping
Automating schema drift detection and schema evolution
Event-based triggering, watermarking, and incremental pipeline logic
CI/CD for pipeline artifacts using YAML + Git integration
Auto-healing and recovery patterns in critical workflows
Performance diagnostics, retry patterns, and load distribution
On-demand ingestion pipelines using REST API and webhook
Creating metadata registries and data quality layers
Delta loading with deduplication and upsert logic
Ingestion cost modeling and Fabric throughput planning
Integration with Power Automate for orchestrated alerting

Data Engineering: Lakehouse Engineering & Distributed Big Data Processing

Architecting petabyte-scale Lakehouses with optimized partitions
Advanced PySpark for ETL, joins, windowing, and batch refinement
Spark performance tuning with broadcast joins and caching strategies
Leveraging Delta Lake time travel and vacuum operations
Structured Streaming with notebooks for near real-time prep
Managing schema evolution in high-churn Lakehouses
Auto-loading from streaming pipelines into Lakehouse
Secure file storage, data masking, and object-level control
Access control via workspace roles, ACLs, and token-scoped URIs
Delta Live Tables (DLT)-style modeling using Fabric Pipelines + Spark
Data lifecycle management, versioning, and retention policies
Using notebooks to orchestrate Fabric-native ML feature stores
Spark SQL vs T-SQL: Fabric interoperability best practices
Integrating Databricks Delta tables with Fabric Lakehouse (interop)
Publishing refined Lakehouse models for cross-workspace consumption

Data Engineering: Advanced Data Warehouse Engineering in Fabric

Multi-model schema deployment using T-SQL and templates
Advanced table storage patterns (columnstore, rowstore, hybrid)
Query folding, execution plan tuning, and materialized views
Synapse-to-Fabric migration patterns for Warehouse workloads
Multi-zone Warehouse design: Staging, Curation, Semantic Zones
Data snapshots, CDC, and Slowly Changing Dimensions (SCD Types 1/2/3)
Warehouse lineage tracking using Fabric catalog and logs
Partitioned table design and lifecycle policy enforcement
Schema-bound views vs open views and indexing strategies
Managing Warehouse with GitOps and Deployment Pipelines
Storing audit logs and temporal data for forensic analysis
Integrating Power BI composite models with multiple Warehouses
Monitoring long-running queries, auto-cancel, and workload isolation
Automated tests and validation of Warehouse pipelines
Secure sharing of Warehouse models via Fabric domains and endorsements

Data Engineering: Real-Time Data Engineering and Advanced KQL Analytics

High-velocity streaming ingestion with Fabric KQL + Event Hub
Designing low-latency architectures with warm and hot paths
KQL functions, macros, and materialized views for high-frequency queries
Time-series modeling and outlier detection in KQL
Writing efficient joins, summarize, parse, and mv-expand statements
Stream aggregation with windows, tumbling, and hopping
Advanced ingestion policies with batch triggers and auto-purge
Integration with IoT, telemetry systems, and Azure Stream Analytics
Building reusable streaming dashboards with Power BI and KQL
Securing KQL DB with RBAC, audit trails, and managed identities
Retention and hot-cold tiering for streaming archives
Monitoring ingestion health and latency SLAs
Creating composite real-time + historical BI models
Triggering Power Automate flows from KQL outputs
Cost optimization and resource planning for KQL workloads

Data Engineering: Semantic Modeling, Governance, and Power BI at Scale

Designing enterprise-wide semantic models on Lakehouse/Warehouse
Composite model architecture and DirectLake dataset design
Row-Level, Object-Level, and Field-Level security patterns
Building reusable calculation groups, perspectives, and templates
Dataset certification workflow and publishing governance
Managing refresh schedules, incremental loading, and query caching
Scaling Power BI for thousands of users and distributed teams
Monitoring model usage, report telemetry, and capacity health
Automating deployment pipelines for datasets and reports
Using XMLA endpoint for external governance tools
Power BI API and service principal authentication for devops
Admin monitoring, audit logs, and tenant-level insights
Deployment rollback, dataset versioning, and failover models
Curated workspace provisioning using Azure Automation + Fabric API
Aligning Power BI governance with Microsoft Purview and compliance

Power Apps

INTRODUCTION TO POWER APPS

Overview of Power Apps

What is Power Apps?
Benefits and use cases
Types of Power Apps: Canvas, Model-Driven, and Portal Apps

Getting Started

Setting up your Power Apps environment
Navigating the Power Apps interface

DATA INTEGRATION AND MANAGEMENT

Connecting to Data Sources

Introduction to connectors
Connecting to common data sources (SharePoint, Excel, SQL Server, etc.)

Managing Data

Understanding data tables and collections

BUILDING YOUR FIRST CANVAS APP

Basics of Canvas Apps

Understanding Canvas Apps
Creating a simple Canvas App
Adding screens and navigation
Creating simple app to view details from data source.
Using simple forms to display and edit data

ADVANCED CANVAS APP FEATURES

User Experience Design

Designing responsive layouts
Using themes and templates
Best practices for user interface design

Working with Controls

Using different types of controls like
Button
Text input
Drop down
Combo Box
Date picker
List box
Radio
Text label
Vertical gallery
Horizontal gallery
Flexible height gallery
Blank Vertical gallery
Blank Horizontal gallery
Blank Flexible height gallery
Data table
Horizontal container
Vertical container
Container
Image
Icons
Shapes
Working with control properties

Working with Variables

Global variables
Context variable
Collections

Using formulas for dynamic form management

Functions to be used in forms
SubmitForm
EditForm
Clear
ClearCollect
Collect
Filter
If
Navigate
NewForm
Notify
Patch
Refresh
Search
Set
Text
ThisItem
Value

Advanced Controls and Features

Working with media (images, videos)
Implementing charts and graphs
Using Power Automate for workflows

Understanding the Common Data Service (Dataverse)

Creating entities and relationships
Building a simple Model-Driven App

SECURITY AND ADMINISTRATION

Security in Power Apps

Understanding security roles and permissions

Implementing data security

App Management

Managing app versions
Publishing and sharing apps

BEST PRACTICES AND ADVANCED TOPICS

Performance Optimization
Tips for improving app performance
Debugging and troubleshooting techniques
Real-World Use Cases

REALTIME PROJECTS

Project 1: Employee Leave Request App
Project 2: Inventory Management App
Project 3: Customer Feedback App
Project 4: Project Management Dashboard
Project 5: Sales Order Processing App

Power Automate

INTRODUCTION TO POWER AUTOMATE

Overview of Power Automate

What is Power Automate?
Benefits and use cases
Types of flows: Cloud Flows, Desktop Flows, and Business Process Flows

Getting Started

Setting up your Power Automate environment
Navigating the Power Automate interface

CREATING YOUR FIRST FLOW

Basics of Flow Creation

Understanding triggers and actions
Creating a simple flow
Running and testing flows

Flow Templates

Using predefined templates
Customizing template flows
Best practices for using templates

WORKING WITH CONNECTORS

Introduction to Connectors

Understanding connectors and their roles
Connecting to common data sources (SharePoint, OneDrive, Outlook, etc.)

Advanced Data Integration

Using premium connectors
Connecting to SQL Server, Azure, and other advanced data sources

ADVANCED FLOW FEATURES

Conditions and Loops

Implementing conditional logic
Using loops for repetitive tasks

Approvals and Notifications

Creating approval workflows
Sending email and mobile notifications
Error Handling and Troubleshooting

Managing errors in flows

Debugging and troubleshooting techniques

DESKTOP FLOWS (RPA)

Introduction to Desktop Flows

Understanding Robotic Process Automation (RPA)
Setting up Power Automate Desktop

Building Desktop Flows

Recording desktop actions
Automating desktop applications

Advanced Desktop Flow Features

Using conditions and loops in desktop flows
Integrating with cloud flows

BUSINESS PROCESS FLOWS

Introduction to Business Process Flows

Understanding business process automation
Creating a simple business process flow

Customizing Business Process Flows

Defining stages and steps
Implementing business rules and logic

Advanced Business Process Flow Features

Using custom entities and fields
Integrating with Power Apps

PROJECTS

Power Automate Projects

Project 1: Automated Invoice Approval Workflow
Project 2: Employee Onboarding Automation
Project 3: Social Media Post Scheduler
Project 4: Customer Support Ticketing System
Project 5: Monthly Sales Report Automation

Desktop Flows (RPA)

Project 1: Automated Data Entry from Emails
Project 2: Invoice Processing and Archiving
Project 3: Automated Report Generation
Project 4: Customer Account Reconciliation
Project 5: Automated Data Migration

Python for Automation

System Requirements

System Requirements:

Power BI Desktop

Free & Downloadable from Microsoft Store App

https://www.microsoft.com/store/productid/9NTXR16HNW1T?ocid=pdpshare
Excel 2016 & above with PowerPivot

Available with Office 365 subscriptions that include desktop versions of Excel for Windows.
MS-SQL Server

https://www.microsoft.com/en-in/sql-server/sql-server-downloads

Kindly install Developer Edition as it is full-featured free edition, licensed for use as a development and test database in a non-production environment.
SQL Server Management Studio (SSMS)

Download and install SSMS from https://learn.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-ver16
Secondary Monitor (optional, but recommended)

Having a secondary monitor will greatly assist in following the pace of the trainer. It allows you to view instructions and your own workspace simultaneously, enhancing your learning experience.

Taught by Microsoft Certified Trainers

All our classes are live,
hands-on and with
real-trainers.

Cloud-Powered Data Engineering & BI Projects

Real-time Projects (Practical Application)

Master the full data lifecycle—from ingestion to insight—with hands-on projects in Azure Data Factory, Databricks, Synapse Analytics, and Power BI. Build automated ETL pipelines, optimize real-time data processing, and drive AI-powered analytics. Gain practical, job-ready skills to architect scalable, cloud-based data solutions that power business intelligence and automation at an enterprise level.

Power BI

SQL

Data Factory & KQL Analytics

Fabric Lakehouse

Power Automate & Fabric Workflows

Python

Power Apps

Enterprise Sales Performance Dashboard

Develop an interactive Power BI dashboard to analyze regional and product-wise sales performance. Utilize DAX measures for YOY growth, sales variance, and customer segmentation, while Power Query is used to clean and transform raw sales data from multiple sources.

Customer Churn Prediction

Build a churn prediction model by integrating customer interaction data, support tickets, and transactional history. Use Power Query for data transformation and DAX measures to calculate churn probability based on behavioral patterns.

HR Analytics & Workforce Planning

Create an HR dashboard to track employee retention, hiring trends, and performance metrics. Connect multiple data sources to Power BI, apply DAX formulas to calculate attrition rates, and implement role-based security for restricted views.

Supply Chain & Inventory Optimization

Design a Power BI solution to monitor inventory levels, supplier performance, and stock movement across locations. Use Power Query to merge purchase order data with warehouse stock levels and apply DAX for predictive analytics on stock replenishment.

Financial Reporting & Budget vs. Actuals Analysis

Automate financial reporting using DAX calculations for variance analysis, custom KPIs, and trend forecasting. Apply Power Query transformations to consolidate financial statements from different departments into a unified report.

Database Design & Normalization

Design and normalize relational databases using primary keys,foreign keys, and normal forms to optimize storage and scalability.

Customer Data Analysis

Analyze customer data with JOINs, GROUP BY, and HAVING to identify trends, segment groups, and generate insights for marketing.

Sales Data Analysis

Use window functions and CTEs to track sales metrics, revenue, and product performance, generating actionable insights.

Inventory Management Queries

Leverage subqueries, UNION, and CASE to track inventory, product stock, and sales velocity for decision-making.

Employee Performance Tracker

Apply ranking functions (e.g., RANK(), DENSE_RANK()) to evaluate employee performance, track KPIs, and enhance workforce management.

Financial Reporting Queries

Retrieve financial data using aggregate functions and GROUP BY to automate real-time reports for balance sheets and P&L statements.

Sales Data ETL Pipeline

Build a data pipeline with Microsoft Fabric Data Factory to extract sales data from multiple sources (CRM, ERP systems), transform the data using KQL Analytics for cleaning and analysis, and load the data into a Power BI dataset.

Customer Segmentation Data Pipeline

Create an ETL pipeline that extracts customer data from various systems, cleanses it using KQL, and loads the data into a Lakehouse for further analysis. Use Power BI to visualize customer segments and their behavior.

Product Inventory Data Integration

Build a pipeline to integrate product inventory data from multiple sources, transforming and cleaning it using KQL and loading it into a Lakehouse. Create reports in Power BI to monitor inventory levels, sales trends, and restocking needs.

Marketing Data Aggregation Pipeline

Create an automated data pipeline that aggregates marketing campaign data from social media platforms, cleanses it with KQL, and loads it into a Lakehouse. Use Power BI to track campaign performance and ROI.

Financial Data Transformation Pipeline

Develop an ETL pipeline that transforms financial transaction data from multiple sources. Use KQL Analytics to clean and aggregate the data, then load it into a Lakehouse for analysis. Build a Power BI report to monitor financial health and trends.

Customer Data Lakehouse

Set up a Fabric Lakehouse to store and manage customer data from CRM, transactional systems, and support platforms. Use Delta Tables to ensure ACID transactions and track historical data. Analyze this data using KQL and visualize it with Power BI.

Sales Data Lakehouse

Build a Lakehouse to consolidate sales data from different sources and transform it using KQL. Utilize Delta Tables to manage incremental data updates and perform real-time analysis. Use Power BI to create a sales performance dashboard.

Inventory Management Lakehouse

Set up a Lakehouse for storing inventory data from multiple sources like warehouse management systems. Use KQL to clean, aggregate, and analyze data, and store transactional data in Delta Tables to track inventory changes over time. Visualize it using Power BI.

Marketing Data Lakehouse

Create a Lakehouse to store and analyze marketing campaign data. Use Delta Tables to track real-time updates and changes. Use KQL Analytics to perform customer segmentation and analyze marketing performance, with visualizations in Power BI.

Financial Data Lakehouse

Build a Lakehouse for storing and transforming financial transaction data from various sources, leveraging Delta Tables for ACID compliance and historical data tracking. Use KQL for querying and Power BI for detailed financial reporting.

Automated Sales Report Refresh

Create a Power Automate workflow that automatically refreshes Power BI sales dashboards every time new data is loaded into the Lakehouse or when an update is detected in the Delta Tables. Set notifications for stakeholders when the reports are ready.

Customer Data Sync Workflow

Build a workflow using Power Automate to automatically synchronize customer data between the CRM and the Lakehouse. Trigger data updates and refresh reports in Power BI when new customer information is added or updated.

Automated Marketing Performance Updates

Create a Power Automate workflow to refresh marketing performance reports in Power BI every time new marketing data is loaded into the Lakehouse. Automate the email notifications to stakeholders when updates are completed.

Order Processing & Dataflow Automation

Use Power Automate to automate the processing of sales orders, integrating data from order management systems into the Lakehouse. Automatically trigger data transformation and updates via Dataflows Gen2, and refresh Power BI reports.

Data Pipeline Failure Alerting Workflow

Build a Power Automate workflow that monitors Data Factory pipeline runs. If a pipeline fails, automatically send an alert email or create a task in a project management system. Integrate this with Power BI to track pipeline performance.

Data Cleansing Pipeline for Lakehouse

Use Python (via Notebooks in Fabric) to clean, deduplicate, and normalize raw CSV/parquet files before storing them in Delta format in the Fabric Lakehouse. Integrate with a Data Factory pipeline for scheduled ingestion.

Automated Data Quality Checks on Delta Tables

Build a reusable Python script to perform column-level validation (null checks, data types, range thresholds) on Delta tables in the Lakehouse. Automatically log failures to a monitoring table and trigger alerts.

Generate KPI Summary Tables for Power BI

Write a Python routine to compute summary KPIs (e.g., weekly sales, customer retention, churn rates) and store results in a Gold layer table. These output tables are optimized for reporting in Power BI dashboards.

Notebook-Driven ETL for Semi-Structured Data

Automate ETL for JSON or nested data (e.g., API exports, logs) using Python in a Fabric Notebook. Transform and flatten the data structure, then write the result into Delta tables for KQL/Power BI consumption.

Automated Archival and Partition Management

Use Python to periodically move older data partitions to cold storage and maintain optimized table size for querying. This improves performance and cost-efficiency within the Lakehouse.

Employee Leave Request App

Design a user-friendly app for employees to submit leave requests. Automate approval workflows, track balances, and integrate with SQL databases for real-time leave management.

Sales Order Management App

Build an interactive app for sales teams to create, update, and track orders. Integrate with SQL to retrieve customer data and Power Automate to generate invoices and email confirmations.

Helpdesk System

Develop a mobile-friendly app where customers can log support tickets. Use Power Automate to assign cases, send updates, and generate Power BI reports for service performance tracking.

Expense Reimbursement Tracker

Create an app for employees to submit expense claims with receipt uploads. Automate approval workflows, validate entries against company policies, and generate real-time reimbursement status updates.

Inventory & Asset Management App

Design a system to track inventory and company assets, with barcode scanning capabilities. Connect to a SQL database for real-time stock updates and automate notifications for low inventory levels.

Design and normalize relational databases using primary keys, foreign keys, and normal forms to optimize storage and scalability.