Microsoft Fabric Analytics

Microsoft Fabric Analytics Ecosystem:

Microsoft Fabric is an end-to-end analytics platform that unifies all data and analytics workloads — data engineering, data integration, data warehousing, real-time analytics, BI, and governance — into one ecosystem.It brings together the best parts of Azure Data services under one unified SaaS environment.

Core Idea of Microsoft Fabric:

Microsoft Fabric provides:

One unified lake (OneLake)
One engine (Lakehouse & Warehouse powered by Delta/Parquet)
One security & governance model (Purview)
One workspace for all analytics personas
One platform for end-to-end analytics (from ingestion → transformation → reporting)

Why Microsoft Fabric Is Powerful:

Unified platform

All analytics services under one umbrella.

SaaS & serverless

No need to manage clusters or compute.

Shared storage

Everything stored in OneLake using open Delta format.

Cost efficient

Single capacity-based pricing.

One security model

End-to-end governance with Purview.

Microsoft Fabric Analytics Master Program

Module 1: Introduction to Microsoft Fabric

1.1 Overview of Fabric

What is Microsoft Fabric?
Why Fabric? Evolution from Azure Synapse, Power BI & Lakehouse
Fabric architecture: OneLake + Workloads
Key capabilities and unified analytics approach

1.2 Fabric Workspaces & User Interface

Navigating Fabric UI
Creating workspaces
Licensing & capacity models

Module 2: OneLake – Unified Data Lake

2.1 OneLake Concepts

What is OneLake?
Delta format & Lakehouse foundation
OneLake vs ADLS Gen2

2.2 OneLake Shortcuts

ADLS, Amazon S3 shortcuts
Managing distributed data across clouds

2.3 OneLake Security & Governance

Role-based access
Cross-workspace security
Data lineage in OneLake

Module 3: Introduction to Microsoft Fabric Data Factory

Understanding Data Factory in Fabric (How it differs from Azure Data Factory)
Key capabilities: Pipelines, Dataflows, Git integration, Lakehouse connectivity
Real-time vs batch data processing in Fabric
Role of Data Factory in the end-to-end Analytics workflow
Data Factory home page overview
Navigation through Pipeline, Dataflows, and Data Pipelines

Module 4: Pipelines in Microsoft Fabric

4.1 Understanding Pipelines

What is a Fabric Pipeline?
Comparison: ADF Pipelines vs Fabric Pipelines
Key components: Activities, Objects, Triggers

4.2 Pipeline Activities

Move Data Activities
- Copy activity
- Data movement sources & sinks
- File format support
Control Flow Activities
- If condition
- ForEach
- Wait
- Switch
- Set Variables
Transformation Activities
- Dataflow Gen2
- Notebook activity
- Spark job activity

4.3 Working with Pipeline Parameters

Pipeline parameters
Variable types
Dynamic content using expressions
Pipeline debugging & validation

Module 5: Dataflow Gen2 (Power Query in Fabric)

5.1 Introduction to Dataflow Gen2

What is Dataflow Gen2?
Differences from ADF Mapping Dataflows
Low-code/no-code ETL using Power Query

5.2 Dataflow Components

Connectors & source options
Power Query Editor (M code basics)
Transformations:
- Filter, Sort
- Merge, Append
- Group by
- Pivot/Unpivot
- Derived columns

5.3 Dataflow Gen2 Outputs

Writing data to Lakehouse (Delta tables)
Write modes: Append, Overwrite
Scheduling Dataflows
Performance optimization techniques

Module 6: Connecting to Lakehouse, Warehouse & Data Sources

6.1 Data Sources Supported

ADLS Gen2
Azure SQL Database
APIs & REST endpoints
On-prem SQL (using Gateway)

6.2 Data Integration Patterns

Ingesting data into Lakehouse
Writing to Tables, Files, Folders in OneLake
Using Warehouse connectors
Uploading Excel/CSV files

Module 7: Orchestration with Triggers & Schedules

Triggers in Fabric

Types of Triggers in Fabric
- Manual
- Scheduled
- Event-Based (upcoming features)
Setting Recurrence
Time Zone configuration
Managing Trigger history
Best practices for scheduling workloads

Module 8: Monitoring & Activity Debugging

Pipeline run view
Dataflow execution logs
Failure & Success output messages
Data Lineage & tracking
Performance dashboard
Error handling & retries
Logs and alerts

Module 9: Introduction to Spark Architecture in Microsoft Fabric

What is Apache Spark?
Spark in Microsoft Fabric (Workspace, Lakehouse, Notebook compute)
Components of Spark:
- Driver
- Executors
- Cluster Manager
- Spark Context & Session
How Spark executes jobs in Fabric
Stages, Tasks, DAG
Lazy evaluation concept
Storage formats used by Fabric (Delta, Parquet)
Understanding Fabric’s compute environment for Spark

Module 10: Reading Data from CSV Files

Reading CSV files using PySpark
Options: header, delimiter, inferSchema, multiline
- header
- delimiter
- inferSchema
- multiline
Using Spark’s file path in Lakehouse / OneLake
Handling corrupt/unwanted records
Saving CSV data into tables
Best practices for handling large CSV files

Module 11: Reading Data from JSON Files

Reading multiline JSON
When to use spark.read.json() vs read.format("json")
Working with nested JSON structures
Exploding arrays and nested structures
Converting JSON into structured DataFrames
Writing processed JSON into Delta tables

Module 12: Reading Data from XML Files

Installing and using Spark XML library in Fabric Notebook
Reading XML using:
- spark.read.format("xml")
XML options: rowTag, attributePrefix, valueTag
Flattening XML hierarchical data
Handling missing / irregular XML structures
Saving XML output to Delta tables

Module 13: Reading Data from Excel Files

Using Spark Excel library
Reading .xlsx and .xls files
Options: header, sheetName, dataAddress
Handling multiple sheets
Converting Excel data to DataFrame
Writing Excel processed data to Delta tables

Module 14: DataFrame Operations

Creating DataFrames (manual + from files)
Schema definition (manual + struct types)
Column operations: add, drop, rename
Filtering, sorting, null handling
Type casting & conversions
Handling duplicates
Aggregations, groupBy operations
Joins: inner, left, right, outer, semi, anti
Window functions
Statistical operations

Module 15: DataFrame Transformations

Narrow vs Wide transformations
Common transformations:
- select(), withColumn(), filter(), drop(), distinct()
- join(), union(), repartition(), coalesce()
Chaining transformations
Transformation functions: map, flatMap
Using Spark SQL functions (F module)
Repartitioning strategies for performance

Module 16: Delta Lake Fundamentals

What is Delta Lake?
Delta vs Parquet
ACID Transactions
Schema Enforcement & Schema Evolution
Delta Logs and File Structure
Compaction & Optimization
Time Travel operations
Using Delta in Fabric Lakehouse

Module 17: Working with Delta Tables

Creating managed & unmanaged Delta tables
Converting existing data to Delta format
CRUD operations on Delta
- Insert, Update, Delete
- Merge (UPSERT)
Versioning & Rollback
Vacuum & Optimize commands
Partitioning Delta tables
Auto Loader with Delta (optional)

Module 18: Spark Compute in Microsoft Fabric

Fabric Runtime for Apache Spark
Types of compute environments
How Compute is allocated
Session-level compute vs Job compute
Autoscaling behavior
Managing Spark jobs in Monitoring Hub
Optimizing compute usage & cost
When to use SQL Engine vs Spark Engine

Module 19: Spark SQL

Creating SQL temporary views
Running SQL queries in notebooks
SQL functions for analytics
Using Spark SQL to query Delta tables
Joins, aggregations, window functions
Optimizing Spark SQL queries
Saving SQL results as tables

Module 20: Types of Views in Spark / Fabric

Local Temporary Views
Global Temporary Views
When to use each
Limitations of temporary views
Accessing global views across notebooks

Module 21: Types of Tables in Fabric/Spark

Managed Tables
- Storage in Lakehouse managed location
External Tables
- Storage outside managed area
Delta Tables
Parquet / Other format tables
SQL Warehouse Tables vs Lakehouse Tables
Table creation using Spark SQL
Best practices for table selection

Module 22: Introduction to Real-Time Analytics in Fabric

What is Real-Time Analytics?
Role of KQL Database in Microsoft Fabric
Difference between Warehouse, Lakehouse & KQL DB
Architecture of RTA in Fabric

22.2 Components

KQL Database
Event Streams
Data Connections
Real-time dashboards

Module 23: Introduction to Microsoft Fabric & Data Warehouse

Understanding Lakehouse vs Warehouse vs KQL DB
Role of the Fabric Data Warehouse in the ecosystem
Differences between Fabric Warehouse and Azure Synapse Dedicated SQL Pool
Architecture overview:
- OneLake
- Delta format
- SQL Endpoint
- Direct Lake mode

Module 24: Fabric Warehouse Architecture

Storage layer (Delta tables in OneLake)
Compute layer (distributed SQL compute)
Metadata & cataloging (Unity Catalog upcoming)
Distributed query processing model
How Fabric Warehouse achieves performance
Warehouse item structure: Schemas, Tables, Views, Procedures

Module 25: Setting Up Fabric Warehouse

Creating a new Warehouse in Fabric Workspace
Understanding capacities & licensing
Warehouse UI & navigation
Creating schemas and managing objects
Permissions & role-based access control (RBAC)
Integrating Warehouse with Lakehouse

Module 26: Tables & Data Modeling

Managed tables
External tables (Delta Lake / Parquet / Lakehouse)
Differences & use cases

26.2 Data Modeling Concepts

Star schema vs Snowflake schema
Dimensions & Facts
Primary keys & surrogate keys
Slowly Changing Dimensions (SCD) Types 0–3

26.3 Table Operations

CREATE / ALTER / DROP table commands
Defining data types for Warehouse
Working with constraints (PK, FK, Unique, Not Null)
Partitioning considerations in Fabric

Module 27: Ingesting Data into Warehouse

Ingestion Methods

Data Pipelines (Fabric Data Factory)
Dataflow Gen2
Notebooks & PySpark
COPY INTO Command

Module 28: Integration with Power BI

Connecting Warehouse to Power BI
DirectQuery, Direct Lake & Import modes
Automatic model creation from Warehouse
Performance benefits of Direct Lake
Publishing & refreshing reports

Module 29: Performance Optimization

Query performance tuning
Delta file optimization (Optimize, Vacuum)
Index-like concepts in Fabric (Clustered storage ordering)
Techniques to speed up ingestion
Using materialized views for performance
Avoiding long-running queries

Module 30: Power BI in Microsoft Fabric

Creating datasets
Relationships, measures, DAX basics

30.1 Visualization & Reporting

Interactive dashboards
Power BI Desktop vs Fabric Power BI

30.2 DirectLake Mode

DirectQuery vs Import vs DirectLake
Performance optimization

Azure Data Engineering With Microsoft Fabric

Microsoft Fabric Analytics Ecosystem:

Core Idea of Microsoft Fabric:

Why Microsoft Fabric Is Powerful:

Microsoft Fabric Analytics Master Program

Train your teams on the theory and enable technical mastery of cloud computing courses essential to the enterprise such as security, compliance, and migration on AWS, Azure, and Google Cloud Platform.