Azure Data Engineering Master Program with AI - Live Training Demo

Azure Data Engineering Master Program with AI – Live Training

(Master Azure Data Engineering with hands-on training in Azure Data Factory, Databricks, PySpark, Delta Lake, Synapse Analytics, and AI-powered development.)

Master Azure Data Engineering with AI through 30 hours of live, hands-on online training led by an industry-experienced trainer, with recorded videos and 1-year access for flexible learning.

Want to become a job-ready Azure Data Engineer? This beginner-friendly program covers Azure Data Factory, Databricks, PySpark, Delta Lake, Synapse Analytics, Power BI, and real-world data engineering projects.

Azure Data Engineering with AI is a comprehensive, hands-on training program designed to help you master modern data engineering using Microsoft Azure. Learn how to build scalable data pipelines, manage cloud storage, process large datasets with Azure Databricks and PySpark, implement Delta Lake and Medallion Architecture, and create enterprise reporting solutions using Azure Synapse Analytics.

This course combines industry best practices with real-time projects and practical labs covering Azure Data Factory (ADF), Azure Data Lake Storage Gen2 (ADLS Gen2), Blob Storage, ETL/ELT pipelines, data transformation, and workflow automation. You’ll also discover how to leverage AI tools like GitHub Copilot to generate SQL, PySpark code, ADF expressions, technical documentation, and accelerate debugging and code reviews.

By the end of the course, you’ll complete a real-world end-to-end capstone project, building a production-ready data pipeline from data ingestion to analytics and visualization using Azure Data Factory, ADLS Gen2, Azure Databricks, Delta Lake, Azure Synapse Analytics, and Power BI. This job-oriented program equips you with the practical skills, hands-on experience, and industry knowledge required to become a confident Azure Data Engineer.

Prerequisites:

Basic SQL
No Azure experience required

About the Instructor:

Annapoorani is an experienced IT professional and passionate technical trainer with over 9+ years of diversified industry experience in software development, database technologies, and cloud-based data solutions. She has extensive knowledge of modern data engineering concepts and specializes in building scalable data pipelines, implementing ETL/ELT processes, and designing cloud-native data platforms using Microsoft Azure. Her expertise includes Azure Data Factory (ADF), Azure Data Lake Storage Gen2 (ADLS Gen2), Azure Databricks, PySpark, Delta Lake, Azure Synapse Analytics, Azure SQL Database, and Lakehouse Architecture, enabling organizations to develop efficient, secure, and high-performance data solutions.

With 4+ years of dedicated online training experience, Annapoorani has successfully trained students, software professionals, and career changers through structured, hands-on learning programs. Her teaching methodology focuses on bridging the gap between theory and real-world implementation by combining interactive sessions, practical assignments, live demonstrations, and industry-oriented projects. She believes in creating a strong foundation in data engineering while helping learners gain the confidence to work with enterprise-grade Azure technologies and modern data platforms.

Annapoorani is committed to preparing learners for successful careers in Data Engineering by providing comprehensive guidance on industry best practices, real-time project development, interview preparation, and problem-solving techniques. Her practical approach, clear explanations, and focus on current industry trends ensure that students not only understand the concepts but also develop the skills required to excel in today’s cloud and data-driven ecosystem. Her goal is to empower every learner with the knowledge and confidence needed to become a job-ready Azure Data Engineer.

Live Sessions Price:

For LIVE sessions – Offer price after discount is 3~~00 USD 259 ~~USD~~~~ 99 USD Or ~~13000 INR~~ ~~12900 INR~~ 7900 Rupees

Enroll For Free Demo

Free Demo Session:

17th September @ 8 PM – 9 PM (IST) (Indian Timings)

17th September @ 10:30 AM – 11:30 AM (EST) (U.S Timings)

17th September @ 3:30 PM – 4:30 PM (BST) (UK Timings)

Class Schedule:

For Participants in India: Monday to Friday @ 8 PM – 9:00 PM (IST)

For Participants in the US: Monday to Friday @ 10:30 AM – 11:30 AM (EST)

For Participants in the UK: Monday to Friday @ 3:30 PM – 4:30 PM (BST)

What student’s have to say about Trainer :

👩 Excellent trainer with real-time examples and hands-on Azure Data Engineering sessions. – Sneha

👨 The trainer explained Azure Data Engineering concepts with excellent real-time examples. The hands-on labs and end-to-end project made learning practical and engaging. Highly recommended for anyone looking to build a career in Data Engineering. – David

👩 Excellent course with well-structured content and interactive sessions. I gained practical experience in Azure Data Factory, Databricks, and PySpark. – Sarah

👨 This course is well-structured and packed with practical knowledge. The trainer made complex Azure Data Engineering concepts easy to understand with live demonstrations. I especially enjoyed learning Azure Data Factory, Delta Lake, and Synapse Analytics. The AI-powered development sessions using GitHub Copilot were an added advantage. It was a fantastic learning experience from start to finish. – Arjun

👩 The trainer’s industry expertise and real-time demonstrations made complex topics easy to understand. I now feel confident working on Azure Data Engineering projects. – Emily

What will I learn by the end of this course?

Build a strong Data Engineering foundation — data types, storage, compute, throughput, latency, batch and real-time processing.
Design modern data platforms using Data Warehouses, Data Lakes, and Lakehouse architecture, including ETL/ELT and data modelling.
Work with Azure cloud & storage including Azure Storage, Blob Storage, ADLS, access management, redundancy, lifecycle management and optimization.
Develop end-to-end data pipelines using Azure Data Factory, including activities, parameters, variables, triggers, data flows and Key Vault.
Automate and monitor pipelines with Azure DevOps, Logic Apps, alerts, logging and pipeline recovery.
Master SQL for Data Engineering including CTEs, views, stored procedures and indexes.
Process real-time data using Apache Kafka, Azure Event Hubs and Stream Analytics.
Use Python, Pandas and PySpark for data processing, distributed computing, transformations and handling large datasets.
Work hands-on with Azure Databricks, including compute, notebooks, workflows, Unity Catalog and data governance.
Build Lakehouse solutions with Delta Lake, including schema evolution, ACID transactions, time travel, MERGE/UPSERT, Auto Loader and incremental processing.
Implement advanced data pipelines using declarative pipelines, data quality expectations, full/incremental loads and scheduling.
Build Databricks SQL data warehouses and dashboards, with security and governance features such as row filters, column masking and data classification.
Learn GenAI for Data Engineering, including LLMs, embeddings, document processing, vector databases and RAG pipelines.

Salient Features:

30 Hours of Live Training along with recorded videos
1 Year access to the recorded videos
Course Completion Certificate

Who can enroll for this course?

Aspiring Data Engineers looking to build a career in Azure Data Engineering
Data Engineers who want to upgrade their Azure, Databricks, and Lakehouse skills
Software/ETL Developers transitioning into Data Engineering
SQL & Database Professionals interested in modern cloud data platforms
Cloud Professionals who want hands-on experience with Azure Data Factory, Databricks, and ADLS
Python/PySpark Developers who want to work with big data and distributed processing
BI & Analytics Professionals looking to strengthen their data engineering foundation
Freshers & IT Professionals with basic programming/SQL knowledge who want to enter Data Engineering

Course syllabus:

Module 1: Data Fundamentals

Bandwidth
Batch Processing
Real-Time Processing
Data Pipelines
Orchestration
Data Terminologies and Storage
Compute – CPU
Memory – RAM, Disk
Storage – SSD, HDD
Data Caching
Throughput
Latency
Types of Data:
- Unstructured
- Structured
- Semi-Structured

Module 2: Modern Data Platforms – Data Warehouse to Lakehouse

Characteristics of:
- Database
- Data Warehouse
- Data Lake
- Data Lakehouse
Data Warehouse vs Data Lake
Limitations of Data Warehouse and Data Lake
Why Data Lakehouse is preferred
Architecture diagrams of DWH, Data Lake and Data Lakehouse
ETL vs ELT
ELT Architecture Walkthrough
ETL Architecture Walkthrough

Module 3: Data Modeling Fundamentals

Conceptual Data Modeling
Logical Data Modeling
Physical Data Modeling
Relational Data Modeling
Advantages of Relational Data Modeling
Project 1: Design a Relational Data Model for Retail

Module 4: Dimensional Data Modeling & SCD

Advantages of Dimensional Data Modeling
Fact Tables
Dimension Tables
Star Schema
Snowflake Schema
Project 2: Design a Dimensional Data Model
Introduction to Slowly Changing Dimensions
Types of SCD
Implementing SCD Types in Dimensional Data Models

Module 5: Cloud & Microsoft Azure Fundamentals

CapEx vs OpEx
Advantages of Cloud over On-Premises
Disadvantages of Data Centers
Types of Cloud:
- Public
- Private
- Hybrid
Azure Portal Walkthrough
Azure Terminologies:
- Resource Groups
- Subscriptions
- Entra ID
- IAM Roles
- Cost Management

Module 6: Azure Data Availability & Regional Architecture

Data Redundancy Options
Data Centers
Availability Zones
Regions
How Azure Ensures Data Availability Using Regional Architecture

Module 7: Azure Storage Fundamentals

Introduction to Azure Storage
Distributed Storage and Advantages
Azure Blob Storage vs Azure Data Lake Storage
Access Types in Azure Storage Account
Azure Storage Services:
- Containers
- File Shares
- Tables
- Queues
Demo:
- Folder Creation
- File Uploads
- File Deletion
- Storage Account Options

Module 8: Advanced Azure Storage & Optimization

Types of Blobs:
- Block Blob
- Page Blob
- Append Blob
Data Access Tiers:
- Hot
- Cool
- Archive
Versioning
Soft Delete
Lifecycle Management Rules
Project 3: Azure Storage Optimization Project

Module 9: Azure SQL Database

Azure SQL Family Introduction
Services Offered in Azure SQL
Advantages of Azure SQL Database
Creating and Configuring SQL Server
Creating and Configuring SQL Database in Azure
Firewall Management
Project 4: Solve a Case Study Using Azure SQL
Assignment 1: Pizza Runner Case Study

Module 10: Azure Data Factory Fundamentals

What is Azure Data Factory
Advantages of Azure Data Factory
Data Movement Problems Solved by ADF
Connecting Azure Storage Account with ADF
Creating Azure Data Factory Data Studio
Azure Data Factory Terminologies
Integration Runtime:
- SHIR
- Azure IR
- Linked SHIR
Demo:
- Create SHIR for On-Premises
- Create Linked SHIR
- Create Azure IR

Module 11: ADF Linked Services, Datasets & Triggers

Linked Services
Creating Linked Services for:
- Azure Storage Account
- GitHub Repository
- Azure Databricks
- Azure SQL
- On-Premises SQL Server
- On-Premises File System
Datasets
Data Stores
Compute Stores
Pipelines
Triggers:
- Scheduled
- Event-Based
- Tumbling Window

Module 12: ADF Activities, Dependencies, Parameters & Variables

ADF Activities:
- Copy Data
- Data Flow
- Get Metadata
- Lookup
- Execute Pipeline
- For Each
Activity Dependencies:
- On Success
- On Skip
- On Failure
- On Complete
Parameters
Dynamic Parameterization of ADF Pipelines
Variables
Variables in Pipelines
Difference Between Variables and Pipelines
Demo: Get Metadata Activity with Variables and Pipelines

Module 13: Azure Key Vault & Secure Data Access

Azure Key Vault
Features of Key Vault
Keys
Secrets
Certificates
Authorizing Users Using IAM Roles
Demo: Connect to Azure Storage Account Using Key Vault
Project 5: Harmonizing Clinical Real-World Data at Fusion Pharma Analytics

Module 14: ADF Data Flows & Transformations

When and Why to Use Data Flows
Creating Data Flows in Pipelines
Connecting Data
Data Flow Activities:
- Conditional Split
- Source Stream
- Sink Stream
- Assert
- Derived Column
- Select
- Alter Row
Project 6: Implementing SCD Types Using ADF Data Flows

Module 15: Azure DevOps Integration with ADF

Git Integration in ADF
Rules for Triggering Pipelines with Git Integration
Live Mode vs Git Mode
Collaboration and Main Branches
ADF Pipeline Versioning
Connecting ADF with Azure DevOps
Creating Git Branches in ADF
Collaboration
Project 7: Sea Freight Logistics Data Modernization

Module 16: Azure Logic Apps, Monitoring & Alerts

Project 8: Sea Freight Logistics Data Modernization
Introduction to Azure Logic Apps
Logic Apps Overview
Connecting Logic Apps with ADF
Actions and Triggers
Demo: Connecting Azure Logic Apps with ADF Workspace
Pipeline Monitoring and Alerts
Adding Alerts in Pipelines
Sending Notification Emails to Stakeholders
Re-Run from Last Processed Activity
Reviewing Copy Activity Logs
Updating Pipeline Status in SQL Table

Module 17: Advanced SQL – CTEs, Indexes & Stored Procedures

Writing CTEs in SQL Server
CTE vs Subquery
Indexes
Advantages and Disadvantages of Indexes
Types of Indexes:
- Clustered
- Non-Clustered
Stored Procedures
When to Use Stored Procedures
Demo: Creating Stored Procedure in SSMS
Project 9: Architecting the Operational Database for Aura Music Festival

Module 18: Kafka & Real-Time Streaming with Azure Event Hubs

Introduction to Apache Kafka
Kafka Terminologies:
- Topics
- Partitions
- Producers
- Consumers
- Brokers
Serialization and Deserialization
File Format Best Suited for Kafka Messaging
Azure Event Hubs
Advantages of Azure Event Hubs
Integrating Kafka with Event Hubs
Real-Time Data Streaming
Stream Analytics Jobs
Project 10: Ingest Real-Time Streaming Data to ADLS Using Event Hubs

Module 19: Pandas & Apache Spark Fundamentals

Pandas DataFrame
Pandas Series
Project 11: Comparing Online In-Store Product Performance
Distributed Computing
Apache Spark
Spark vs MapReduce in Hadoop
Advantages of Spark:
- DAG Scheduler
- Lazy Evaluation
- Parallel Processing
- In-Memory Computation
Spark Architecture and Internals
Demo: Connect Spark in Jupyter Notebook

Module 20: Apache Spark Components & PySpark Data I/O

Spark Components:
- Driver
- Executor
- Resource Manager
- SparkSession
- DataFrame
- Dataset
- RDD
Demo: Create RDD and DataFrame in Notebook
Reading and Writing Data Using PySpark
DataFrame Reader API
DataFrame Writer API
Read Options for File Formats
Read Modes:
- Permissive
- Fail Fast
- Malformed
Processing Corrupt/Bad Data
Demo: Read and Write CSV Data
Demo: Process Corrupt Records
Spark SQL
Writing SQL Queries on DataFrames
Temporary Views
Global Temporary Views
Temporary View vs Global Temporary View

Module 21: PySpark Transformations, Actions & Data Processing

Narrow Transformations
Wide Transformations
Actions
Dependencies with Transformations
Jobs
Stages
Tasks
Data Shuffle
Shuffle Partitions
Shuffle Read
Shuffle Write
Project 12: Transforming Big Mart Sales
Assignment 2: Diner’s Pizza Case Study Using PySpark
Demo: Analyze Jobs, Stages and Tasks Based on a Scenario

Module 22: Azure Databricks Fundamentals

Databricks Architecture
Advantages of Databricks
Databricks Compute:
- All-Purpose
- Job
- Serverless
Workspaces:
- Repos
- Shared
- Users
DBSQL Warehouse:
- Serverless
- General
Data Ingestion:
- Lakeflow Connect
- External Connectors
- Fivetran
- Upload Data to Delta Table
- Upload to Volume
Portal Walkthrough
Creating Resources
Connecting Databricks to Visual Studio Code
VS Code Options
Databricks with Azure:
- ARM Templates
- Managed Resource Group
Demo: Databricks and Azure Architecture

Module 23: Unity Catalog, Lakehouse Design & Workflow Automation

Introduction to Unity Catalog
Data Governance Using Unity Catalog
Three-Level Namespace Model
Permission Model
Unity Catalog Features:
- Storage Credentials
- External Locations
- Data Lineage
- History
- Data Insights
Metastore with External Location
Types of Catalogs:
- Standard
- Foreign / Lakehouse Federation
Schemas:
- External
- Managed
Tables:
- External
- Managed
Views:
- Temporary
- Materialized
Volumes:
- External
- Managed
Creating Storage Credentials
Accessing Files in ADLS
Creating Catalog, Schema and Delta Tables on ADLS
Uploading CSV Data to Volume
Creating Views for Dashboards
Lakehouse Architecture
Three-Layer Architecture
Introduction to Notebooks
Notebook Automation
Workflow with Job Cluster
Scheduling Notebook Execution
Project 13: Implement Lakehouse Architecture Using ADF and Databricks

Module 24: Databricks PySpark, Delta Lake & Declarative Pipelines

Access ADLS Using Service Principals
Secret Scopes
Databricks Utilities
Transforming Datasets Using:
- SQL
- PySpark
Cluster Pools
Serverless Clusters
Cluster Policies
Creating Cluster Policies
Limitations of Parquet Format
Introduction to Delta Lake Tables
Parquet vs Delta Lake Tables
Delta Lake Features:
- Schema Evolution
- ACID Compliance
- Time Travel
- Metadata Log
Deep vs Shallow Clones
MERGE and UPSERTS
Deletion Vectors
VACUUM
Delta Table Properties
COPY INTO
Auto Loader
Demo: MERGE, COPY INTO and Auto Loader
Lakeflow Declarative Pipelines
Project 14: Incremental Data Ingestion Using Databricks
Project 15: Implementing SCD Types in Databricks
Imperative vs Declarative Pipelines
Declarative Pipelines vs Classic Notebook-Based Spark
Lakeflow Declarative Pipeline Overview
ETL Using Lakeflow Declarative Pipelines
Joins, Aggregations and Window Functions in Python and SQL
Data Quality Expectations
Constraints
Severity:
- Warn
- Fail
Handling Bad Data
Pipeline Triggers
Full vs Incremental Updates
Integrating Pipelines with Jobs

Module 25: Databricks SQL, Data Warehousing, GenAI & RAG

SQL Warehouses
Types of SQL Warehouses
Portal Walkthrough
Creating a SQL Warehouse
SQL Alerts
DBSQL Commands
Unity Catalog Functions Using DBSQL:
- Row Filter
- Column Mask
- Data Classification Tags
Applying Row Filters and Column Masks
Users and Groups
Creating Users and Groups
Assigning Roles
Data Access Based on Users or Groups
Views in DB Warehouses
Types of Views
Advantages of Views
Differences Between View Types
Demo: Create All Types of Views
Project 16: Build a Sales Dashboard