Azure Data Engineering Master Program with AI, Databricks & Power BI – Live Training

Teacher

Azure Data Engineering Master Program with AI, Databricks & Power BI – Live Training

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

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
Basic Python (preferred)
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~~~~ 119 USD Or ~~13000 INR~~ ~~12900 INR~~ 9900 Rupees

Enroll For Free Demo

Free Demo Session:

1st September @ 8 PM – 9 PM (IST) (Indian Timings)

1st September @ 10:30 AM – 11:30 AM (EST) (U.S Timings)

1st September @ 3:30 PM – 4:30 PM (BST) (UK Timings)

Class Schedule:

For Participants in India: Monday to Friday @ 8 PM – 9:30 PM (IST)

For Participants in the US: Monday to Friday @ 10:30 AM – 12:00 PM (EST)

For Participants in the UK: Monday to Friday @ 3:30 PM – 5:00 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?

Understand Modern Azure Data Engineering Architecture.
Build Azure Data Factory (ADF) Pipelines for batch data ingestion.
Store and manage data using Azure Data Lake Storage Gen2 (ADLS Gen2).
Develop scalable PySpark Transformations in Azure Databricks.
Implement Medallion Architecture using Delta Lake.
Query and analyze data with Azure Synapse Analytics.
Leverage AI Tools for faster Development, Debugging, Code Generation, and Documentation.
Build and showcase a Real-World End-to-End Azure Data Engineering Project.

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.
ETL Developers who want to upgrade their skills with Microsoft Azure and modern data platforms.
SQL Developers interested in cloud-based data engineering and data pipeline development.
Data Analysts looking to transition into Azure Data Engineering roles.
Software Developers who want to learn Azure Data Factory, Databricks, and PySpark.
Cloud Engineers interested in data engineering solutions on Microsoft Azure.
Business Intelligence (BI) Professionals working with data warehouses, reporting, and analytics.
Data Warehouse Professionals looking to modernize their skills with Lakehouse architecture and Delta Lake.
Fresh Graduates seeking a career in Cloud Data Engineering with hands-on project experience.
IT Professionals who want to upskill in Azure Data Engineering, AI-powered development, and real-world data pipeline implementation.

Course syllabus:

Module 1: Data Engineering Foundations + Azure Basics

What is Data Engineering?
Data Engineer Roadmap
Modern Data Platform
ETL vs ELT
Batch vs Streaming
Data Warehouse
Data Lake
Lakehouse
Medallion Architecture
Azure Data Engineering Services

Hands-on

Azure Account Setup
Resource Group Creation
Storage Account Creation

Module 2: Azure Storage & Data Lake Gen2

Blob Storage
ADLS Gen2
Containers
File Structure
Security
SAS Tokens
RBAC

Lab

Create
Raw
Processed
Curated
Archive
Logs
Upload CSV
Explore folders

Module 3: Azure Data Factory Fundamentals

Pipeline
Dataset
Linked Service
Copy Activity
Parameters
Variables
Dynamic Content

Lab: CSV -> Azure Data Lake -> Azure SQL

Module 4: Azure Data Factory Advanced

Lookup
ForEach
Incremental Load
Metadata Driven Pipelines
REST API
Error Handling
Triggers

Lab: Build Dynamic Pipeline

Module 5: Azure Databricks & PySpark

Workspace
Cluster
Notebook
Spark Basics
DataFrame
Reading Files

PySpark

select()
filter()
groupBy()
joins()
window()
write()

Lab: Clean sales dataset

Module 6: Delta Lake + Medallion Architecture

Delta Tables
ACID
Time Travel
Merge
Upsert
Vacuum
Optimize

Lab: Bronze -> Silver -> Gold Pipeline

Module 7: Azure Synapse Analytics

Serverless SQL
Dedicated SQL
External Tables
Spark
Query Data Lake

Lab: Create reporting layer

Module 8: AI for Data Engineers

This is your biggest differentiator.

AI for SQL
AI for PySpark
AI for ADF Expressions
AI Documentation
GitHub Copilot
AI Debugging
AI Code Review

Lab:

Generate

SQL
PySpark
Pipeline
Documentation

using AI.

Module 9: End-to-End Project

Capstone Project: CSV -> ADF -> ADLS -> Databricks -> Delta -> Synapse -> Power BI

Deliverables:

Students will receive:

Git Hub notes
PySpark notebooks
Azure Data Factory pipelines
SQL scripts
Architecture diagrams
AI prompt library for Azure Data Engineering
Assignments after each session
One end-to-end capstone project
Session recordings
GitHub repository with all source code

How can I enroll for this course?

Enroll For Free Demo

For any other details, Call me or Whatsapp me on +91-9133190573

Live Sessions Price:

For LIVE sessions – Offer price after discount is 3~~00 USD 259 ~~USD~~~~ 119 USD Or ~~13000 INR~~ ~~12900 INR~~ 9900 Rupees

Sample Course Completion Certificate:

Your course completion certificate looks like this….

Important Note:

To maintain the quality of our training and ensure a smooth learning experience for all participants, we do not allow batch repetition or switching between courses.

To reiterate, moving from one course to another or shifting from one trainer to another (even if it is the same course) is not possible. Changing batches or trainers in any form is strictly not permitted.

We request all learners to attend the scheduled sessions regularly and make the most of their learning journey. Thank you for your understanding and continued support.

Course Features

Lectures 173
Quiz 0
Duration 48 hours
Skill level All levels
Language English
Students 0
Assessments Yes

25 Sections
173 Lessons
48 Hours

Expand all sectionsCollapse all sections

Discuss data terminologies and how they impact data storage
11
- 1.1
  Compute (CPU)
- 1.2
  Memory (RAM, Disk)
- 1.3
  Storage (SSD, HDD)
- 1.4
  Data Caching
- 1.5
  Throughput
- 1.6
  Latency
- 1.7
  Network Bandwidth
- 1.8
  Batch processing
- 1.9
  Real time processing
- 1.10
  Pipelines
- 1.11
  Orchestration
Types of Data
3
- 2.1
  Unstructured
- 2.2
  Structured
- 2.3
  Semi – structured
From Warehouse to Lakehouse: A Guide to Modern Data Platforms
10
- 3.1
  Characteristics of database, data warehouse, data lake, data Lakehouse
- 3.2
  Differences between data warehouse and data lake
- 3.3
  Limitations of data warehouse and data lake
- 3.4
  Why data Lakehouse is preferred over data warehouse / data lake
- 3.5
  Demo: Architecture diagrams of DWH, DL, DLH
- 3.6
  ETL vs ELT which is preferred?
- 3.7
  Demo: ELT architecture diagram walkthrough and ETL architecture diagram walkthroug
- 3.8
  Conceptual, Logical and Physical data modelling
- 3.9
  Relational data modelling and its advantages
- 3.10
  Project 1: Design a relational data model for retail
Data Modelling – OLAP
7
- 4.1
  Dimensional data modelling advantages
- 4.2
  Introduction to fact and dimension tables
- 4.3
  Star and snowflake schema design
- 4.4
  Project 2: Design a dimensional data model
- 4.5
  Introduction to Slowly changing dimensions
- 4.6
  Types of SCD
- 4.7
  Implementing SCD types in Dimensional data model
Fundamentals of cloud and azure
10
- 5.1
  CapEx vs OpEx
- 5.2
  Advantages of cloud over on premises
- 5.3
  Disadvantages of data centers
- 5.4
  Types of cloud
- 5.5
  Azure Portal Walkthrough
- 5.6
  Azure Terminologies
- 5.7
  Data redundancy options
- 5.8
  How Azure ensures data availability using regional architecture
- 5.9
  Introduction to Azure Storage
- 5.10
  Demo: Folder creation, file uploads, file deletion and storage account options
Advance concepts in Azure Storage
6
- 6.1
  Types of blobs
- 6.2
  Types of data access tiers
- 6.3
  Versioning
- 6.4
  Soft delete
- 6.5
  Lifecycle management rules
- 6.6
  Project 3: Azure Storage Optimization project
A Guide to Azure SǪL: Databases, Servers, and Rule Management
7
- 7.1
  Azure SǪL family introduction
- 7.2
  Services offered in Azure SǪL
- 7.3
  Advantages of using Azure SǪL database
- 7.4
  Create and configure SǪL server and SǪL database in Azure
- 7.5
  Setup rules to allow access (firewall management)
- 7.6
  Project 4: Solve a Case study using Azure SǪL
- 7.7
  Assignment 1: Pizza Runner Case Study
Azure Data Factory Basics: What It Is and Why It Matters
5
- 8.1
  What is Azure Data Factory and advantages
- 8.2
  How Azure Data factory solves some of the data movement problems in the industry
- 8.3
  Connecting azure storage account with ADF
- 8.4
  Demo: Creating Azure data factory data studio
- 8.5
  Azure Data Factory Terminologies
Understanding Azure Data Factory: Activities, Parameters, Variables, and Authentication
8
- 9.1
  Activities
- 9.2
  Activity dependencies
- 9.3
  Parameters and how to dynamically parameterize ADF pipelines
- 9.4
  Variables and how to use them in a pipeline
- 9.5
  Differences between variables and pipelines
- 9.6
  Demo: Prepare a pipeline with the Get Metadata activity and explain the variables and pipelines
- 9.7
  Azure Key Vault
- 9.8
  Project 5: Harmonizing Clinical C Real-World Data at Fusion Pharma Analytics
Azure Data Factory: Transformations using Data Flows
4
- 10.1
  When should we use Data Flows and why
- 10.2
  Demo: Creating data flows in pipelines and connecting data
- 10.3
  Data Flow activities
- 10.4
  Project 6: Implementing SCD types using ADF Data flows
How Azure DevOps Enhances Azure Data Factory Workflows
10
- 11.1
  Git Integration in ADF
- 11.2
  Rules to consider while triggering pipelines with Git integration
- 11.3
  Live mode vs Git mode in ADF pipelines
- 11.4
  Collaboration and main branches in ADF
- 11.5
  How DevOps enhances ADF pipeline versioning
- 11.6
  Demo: Connecting ADF with Azure DevOps and creating git branches in ADF for collaboration
- 11.7
  Project 7: Sea Freight Logistics Data Modernization
- 11.8
  Project 8: Sea Freight Logistics Data Modernization
- 11.9
  Introduction to Azure Logic Apps
- 11.10
  Pipeline Monitoring and Alerts
SǪL Building Blocks: CTEs, Views, Stored Procedures, and Indexes
9
- 12.1
  How to write CTEs in SǪL server
- 12.2
  Difference between CTE and Subquery
- 12.3
  What are Indexes
- 12.4
  Advantages and disadvantages of using Indexes
- 12.5
  Types of Indexes
- 12.6
  What are stored procedures
- 12.7
  When should we use stored procedures
- 12.8
  Demo: Creating a stored procedure in SSMS
- 12.9
  Project 9: Architecting the Operational Database for Aura Music Festival
Streaming Data using Azure Event Hubs, Kafka
6
- 13.1
  Introduction to Kafka
- 13.2
  Terminologies
- 13.3
  Serialization and de-serialization in Kafka
- 13.4
  Which file format is best suited for Kafka messaging
- 13.5
  Azure Event Hubs C Stream Analytics
- 13.6
  Project 10: Ingest real -time streaming data to ADLS using Event Hubs
Mastering Pandas Data Frame Operations
3
- 14.1
  Pandas Data frame and Series
- 14.2
  Project 11: Comparing Online In-Store Product Performance
- 14.3
  Getting Started with Apache Spark: Distributed Computing and Architecture
Components of Apache spark
9
- 15.1
  Driver
- 15.2
  Executor
- 15.3
  Resource manager
- 15.4
  Spark session
- 15.5
  Data Frame
- 15.6
  Dataset
- 15.7
  RDD
- 15.8
  Demo: Create RDD and Data frame in notebook
- 15.9
  Reading and Writing data using Pyspark
Transformations, Actions in PySpark
8
- 16.1
  Narrow transformations
- 16.2
  Wide transformations
- 16.3
  Actions and dependencies with transformations
- 16.4
  Project 12: Transforming big mart sales
- 16.5
  Assignment 2: Case study on Diner’s pizza using pyspark
- 16.6
  Jobs, Stages and tasks
- 16.7
  Data shuffle (shuffle partitions, shuffle read, shuffle write)
- 16.8
  Demo: Analyze jobs, stages and tasks required based on a scenario
Introduction to Azure Databricks
2
- 17.1
  Databricks Architecture, advantages
- 17.2
  Terminologies
Meta store and Unity catalog
12
- 18.1
  Introduction to Unity Catalog
- 18.2
  Data governance using UC
- 18.3
  Three level namespace model
- 18.4
  Permission model in UC
- 18.5
  Features of UC
- 18.6
  Demo: Create Meta store with external location
- 18.7
  Types of Catalogs
- 18.8
  Schemas (External, managed)
- 18.9
  Tables (External, managed)
- 18.10
  Views (Temporary, materialized)
- 18.11
  Volumes (external, managed)
- 18.12
  Demo
Lakehouse Design and Workflow Automation
5
- 19.1
  Discuss Lakehouse architecture and how it is implemented in industry
- 19.2
  Introduction to notebooks
- 19.3
  How notebook automation works in Azure Databricks
- 19.4
  Demo: Create workflow along with job cluster and schedule notebook execution
- 19.5
  Project 13 – Implement Lakehouse architecture using ADF and Databricks
Process Data in Databricks using PySpark
10
- 20.1
  Access ADLS within Databricks using service principals
- 20.2
  Secret scopes, Databricks utilities walkthrough
- 20.3
  Transform dataset using both SǪL and Pyspark in Databricks
- 20.4
  Cluster Pools
- 20.5
  Serverless clusters
- 20.6
  Cluster policies
- 20.7
  Demo: create your own cluster policy
- 20.8
  Limitations of parquet format
- 20.9
  Introduction to delta lake tables
- 20.10
  Parquet vs Delta Lake tables
Delta Lake, Delta tables
8
- 21.1
  Unique features of Delta Lake Tables using sample data
- 21.2
  Deep vs shallow clones
- 21.3
  MERGE and UPSERTS
- 21.4
  Deletion Vectors
- 21.5
  VACCUUM
- 21.6
  Delta table properties
- 21.7
  copy into, Autoloader
- 21.8
  Demo: Process data using MERGE, Copy INTO, Autoloader and understand the differences
Lake flow declarative pipelines
2
- 22.1
  Project 14: Incremental data ingestion using Databricks
- 22.2
  Project 15: Implementing SCD types in Databricks
Declarative Pipelines
7
- 23.1
  Imperative vs Declarative pipelines
- 23.2
  How they differ from classic notebooks-based Spark
- 23.3
  Lake flow declarative pipelines overview
- 23.4
  Demo: Lake flow declarative pipeline for ETL
- 23.5
  Joins, aggregations, window functions in Python and SǪL within declarative
- 23.6
  Data quality with expectations for incremental loads: defining constraints, severity (warn/fail), and handling bad
- 23.7
  Running and scheduling pipelines
Data warehousing using DB SǪL
7
- 24.1
  SǪL warehouses and types
- 24.2
  Portal walkthrough
- 24.3
  Demo: Creating a Warehouse
- 24.4
  Setting up SǪL alerts
- 24.5
  DB SǪL commands
- 24.6
  Functions in Unity Catalog using DB SǪL
- 24.7
  Users and Groups
Views in DB Warehouses
4
- 25.1
  Types of views
- 25.2
  Advantages, differences between types of views
- 25.3
  Demo: create all types of views
- 25.4
  Project 16: Build a sales dashboard (e.g., by region/product) using existing Lakehouse tables

Annapoorani

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.

3. Other Courses

Azure Data Engineering Master Program with AI, Databricks & Power BI – Live Training