Hands-On Agile Coaching | Azure Data Engineer With Microsoft Fabric & Data Analytics

About HAC

Empowering professionals worldwide with practical, job-focused technology training.

Hands-on Agile Coaching (HAC) Australia is a leading global IT training organization dedicated to delivering real-world, hands-on learning experiences. HAC has successfully trained and mentored thousands of students across Australia, the USA, Canada, the UK, and India in high-demand domains including Azure Data Engineering, Cybersecurity, Generative AI, Agile, PMP, and Business Analysis.

Our mission is simple: bridge the gap between learning and employment by providing practical skills, industry-relevant projects, and expert mentorship that prepare students for real-world success.

Why Choose HAC for Azure Training?

HAC follows a "Learning by Doing" approach, ensuring students gain hands-on experience through live projects, practical assignments, and real-world industry scenarios. Our Azure Data Engineer & Analyst Training covers Azure Data Factory, Databricks, PySpark, Microsoft Fabric, Power BI, Azure Data Lakes, SQL, and modern cloud data engineering practices.

With expert-led sessions, career mentoring, interview preparation, and industry-focused project work, HAC helps students build the confidence and technical expertise required to excel in today's cloud and AI-driven data landscape.

About the Founder

Lakshmi Nune, Founder & Director of HAC Australia, brings over 26 years of global IT industry experience. Throughout his career, he has trained and mentored thousands of professionals worldwide, helping them successfully transition into high-demand technology careers through practical, industry-focused training and coaching.

Learn from industry experts. Build practical skills. Advance your career with confidence.

Watch Our Real-Time Azure Data Engineer Demo Class

Course Overview

Master Azure Data Engineering, Microsoft Fabric, Databricks, and Power BI through hands-on projects and real-world industry scenarios.

HAC Australia's Hands-On Azure Data Engineer & Analyst Training Program is designed to help you build job-ready skills in modern cloud data technologies. Learn Azure Data Factory, Azure Databricks, PySpark, Microsoft Fabric, ADLS Gen2, Azure SQL, and Power BI through practical labs and instructor-led sessions.

Gain real-world experience by developing end-to-end data solutions, including data ingestion, transformation, storage, analytics, and reporting. Work with industry-standard architectures such as Delta Lake, Medallion Architecture, Fabric Lakehouse, and CI/CD deployment using Azure DevOps.

Whether you're a beginner, a working professional, or transitioning into data engineering, this program provides the technical expertise, project experience, interview preparation, and career guidance needed to succeed in today's AI-driven data industry.

Build in-demand skills. Work on real projects. Launch your Azure Data Engineering career with confidence.

Course Objectives

Gain practical, job-ready expertise in Azure Data Engineering, Microsoft Fabric, Databricks, PySpark, and Power BI through hands-on training, live projects, and real-world industry scenarios. This program is designed to help you build, manage, and optimize modern cloud-based data solutions from end to end.

Understand cloud computing fundamentals and the Microsoft Azure ecosystem.
Build scalable ETL/ELT pipelines using Azure Data Factory, Databricks, and Microsoft Fabric.
Work with Azure Data Lake Storage, Azure SQL Database, Delta Lake, and Lakehouse architectures.
Develop data transformation solutions using Python, PySpark, and Apache Spark.
Implement Medallion Architecture, Delta Live Tables (DLT), and Unity Catalog.
Create interactive dashboards, reports, and business insights using Power BI and DAX.
Learn data warehousing concepts including OLTP, OLAP, Star Schema, and Snowflake Schema.
Apply CI/CD practices, version control, and deployment strategies using Azure DevOps.
Build and deploy end-to-end real-world data engineering projects across multiple industries.
Prepare for Azure Data Engineer, Data Analyst, Microsoft Fabric Engineer, and Cloud Data Engineer roles.
Receive resume guidance, interview preparation, and career mentoring to accelerate job success.

Upon completion, you will have the practical skills and confidence to design, automate, and manage enterprise-grade data solutions using Microsoft's latest cloud and analytics technologies.

Enrolling in this course doesn't require predefined skills. However, familiarity with Linux, Python, SQL, and Scala would be advantageous.

Learning a DP-700 Certification Course can provide several benefits, such as:

Advancement in Career Opportunities:As the need for data engineering expertise grows, certified Azure Data Engineers are coveted by leading global firms. This creates numerous career prospects for professionals in this realm.
Anticipated Increase in Demand:The demand for proficient data engineers is expected to rise in the future, driven by the widespread adoption of cloud computing, big data analytics, and machine learning. This presents an opportune moment to pursue certification in Azure Data Engineering.
Skills:Obtaining the certification offers a thorough grasp of Azure data services, encompassing data modeling, ingestion, transformation, processing, analysis, visualization, monitoring, and optimization. This equips professionals to create and implement robust and efficient data pipelines and stores.
Increase in Salary Potential: Certified Azure Data Engineers often command higher salaries than their non-certified counterparts. This certification serves as a tangible demonstration of expertise and experience, paving the way for lucrative job prospects.
Practical Application Experience:Participants in the certification course gain practical experience through real-world case studies and industry projects, allowing them to apply their skills in authentic scenarios.

Our Azure Data Engineer Certification Course is crafted to industry standards, aiming to equip learners with the skills to design, implement, and uphold secure and compliant data processing pipelines utilizing Azure data services and frameworks. The course prepares candidates for the Azure Data Engineer Exam, assessing proficiency in integrating, transforming, and consolidating data from diverse sources.

Data modelling and database design
SQL and other query languages
Data integration and ETL Tools
Data analysis and visualization
Cloud Platform Skills
Programming languages such as Python and Java.
Data Pipeline Development

Course Schedule — Key Topics Covered

Weeks

Core modules

50+

Hands-on scenarios

ADF · Databricks · Fabric · Power BI

Key technologies

Cloud & Azure Foundations

Cloud intro · ADE services · Storage types

3 sections · 29 topics

Azure for the data engineer

Cloud computing basicsIntroduction to Microsoft AzureApplications of AzureAzure servicesUnderstanding dataOn-premises vs Cloud serversAzure Data Engineer roles & tasksData engineering processesUse cases for the CloudSetting up Azure account

Overview of ADE services

ADLS gen2Azure Blob StorageAzure SQL DatabaseAzure Data FactoryAzure DatabricksAzure Synapse AnalyticsMicrosoft Fabric

Azure data storages

File storesRelational data storeNon-relational data storesAzure Blob StorageAzure Data Lake StorageWhy Data LakeData Lake architectureAzure SQL DatabaseNoSQL & Azure Cosmos DBAzure DB for MySQL / MariaDB / PostgreSQLAzure Synapse Analytics

Azure Data Factory (ADF)

ETL/ELT · Pipelines · Ingestion methods · Scenarios

4 sections · 30 topics

ADF fundamentals

Introduction to ADFETL vs ELTOn-prem vs Cloud ETL toolsWhy ADFADF componentsLinked services in ADF

Integration runtime & pipelines

IR — AzureIR — Self-hostedIR — SSISCopy data activityControl flow activityData flow activity

Data ingestion methods

Full loadCopy data toolBatch loadSequential loadIncremental loadChange data capture (CDC)

ADF scenarios & operations

Copy data scenariosAzure SQL databaseControl flow activitiesData flow transformationsParameters in ADFSCD Type 1 & 2 pipelinesADF data migrationScheduling & triggering pipelinesMonitoring pipelinesCI/CD in ADF

SQL & Python Basics

Data analysis with SQL · Python fundamentals

2 sections · 25 topics

SQL for data analysis

SQL commands — DDL & DMLAggregate functions & groupingSQL Joins & set operatorsSubqueriesData cleaning & transformationWindow & logical functionsString & date functionsAnalytical functions

Python basics

Variables & datatypesOperators — arithmetic / logical / comparisonWorking with strings · Indexing & slicingLists · Tuples · Sets · DictionariesBuilt-in & user-defined functionsIf / While / For statementsRange() functionMap · Filter · ReduceLambda expressions

Apache Spark & Databricks Intro

Spark architecture · Databricks setup · RDDs

4 sections · 32 topics

Spark introduction

Introduction to Apache SparkHadoop vs SparkSpark architectureSpark context & APIsRDDs · DataFrames · DatasetsSpark transformations & actionsLazy evaluation & fault toleranceColumn manipulations (WithColumn / WithColumnRenamed)Partition by · Reading & writing formatsSpark SQL · Group by · Window functionsJoins · Date & conditional functionsCaching · Schema evolution · DAG

Azure Databricks introduction

Azure Databricks vs Free EditionDatabricks runtime engineWorkspace · Cluster · CatalogDBFS · Tables · Hive Metastore

Databricks utils & volumes

Volume introduction & creating volumeWorking with files using DBUTILSNotebook intro · Creating directoryCopy · Move · List · Remove commands

RDDs (PySpark) & storage integration

Integrating ADLS with DatabricksMounting with access keysCreating Spark RDDsSpark transformations & actionsLazy evaluation

PySpark DataFrames & Delta Lake

DataFrames · Spark SQL · Medallion architecture · Unity Catalog

3 sections · 24 topics

DataFrames (PySpark)

Creating Spark DataFramesReading data from different filesColumn manipulationsGroup by & aggregate functionsJoins in PySparkWindow functionsPartitioning with Partition byCaching · Schema evolution · DAGWriting data to different formats

Spark SQL & tables

Creating viewsSQL statements on viewsCreating tables — UI & notebooks

Delta Lake & advanced Databricks

Delta LakeManaged tablesExternal tablesLakehouseMedallion — Bronze · Silver · GoldScheduling Databricks jobs via ADF pipelinesDelta Live Tables (DLT)Unity CatalogSecret scopes via Azure Key VaultService principal · SAS tokens

Microsoft Fabric Data Engineering

Fabric platform · Lakehouse · Warehouse · Power BI in Fabric

4 sections · 27 topics

Fabric platform & Data Factory

Introduction to Microsoft FabricFabric Analytics platformFabric OneLakeData pipelinesDataflows gen2Transforming dataLoading data to LakehouseCopy jobADF to Fabric migration

Fabric Synapse data engineering

Fabric Synapse Data EngineeringFabric NotebooksAnalyze data with Apache SparkLakehouseDelta tables

Fabric Synapse data warehouse

Creating a Warehouse in FabricWays to load dataCOPY INTO commandPipeline to WarehouseDataFlow gen2 loadLakehouse vs Warehouse

Power BI in Fabric

Microsoft Power BI in FabricAccess data using PBI DesktopDirect LakeDirect QueryRow-level securityColumn-level security

Power BI & Data Warehousing

DWH concepts · Power BI Desktop & Service · DAX

2 sections · 13 topics

Data warehouse fundamentals

Introduction to Data WarehouseOLTP vs OLAPStar schema vs Snowflake schema

Power BI

Overview of Power BIPower BI DesktopConnecting to different data sourcesPower Query for data transformationData modelingDAXReports & visualization typesDashboardsPublishing reportsPower BI Service

Real-Time End-to-End Project

Azure DevOps · Medallion pipelines · Power BI reporting

3 sections · 34 topics

Project setup & DevOps

Overview of projectCreating Azure servicesSetting up data storesCreating metadata tablesMasking credentials with Key VaultsAzure DevOps repo setupWorking branch · Creating PR · Merging code

Dynamic pipelines & medallion framework

Dynamic linked servicesDynamic datasetsMetadata-driven pipelinesFramework for Bronze layerFramework for Silver layerNotebooks per tableApplying required transformationsJob cluster in ADFTrigger & scheduling for master pipelineGold layer → Power BIDimensional modelingCreating measures & publishing report

Hands-on real-time ADE scenarios

Creating ETL/ELT pipelines in ADFControl flow activitiesData flow transformationsIngesting multiple file sourcesIngesting multiple SQL tablesBatch load & sequential loadIncremental load — last modify dateIncremental load & CDCSCD1 & SCD2 pipelinesEmpty file scenariosFile count in folderValidate schema & structureCopy data from REST APIsEmail notificationsPySpark & Spark SQL analysisADLS analysis from DatabricksMounting & service principalCreating Delta tablesLakehouse (Medallion) architectureFabric pipelines & Dataflows gen2Lakehouse & Warehouse in FabricVisualizing data & dashboards in Power BI

DATA ANALYTICS

SQL
- SQL commands for Data Analysis (DDL & DML)
- Aggregate Functions & Grouping data
- SQL Joins & Set Operators
- Advanced Data Analysis: Subqueries
- Data Cleaning & Transformation
- Window & Logical Functions
- String & Date Functions
- Analytical Functions
Data Analytics with Power BI
- Introduction to Datawarehouse
- OLTP vs OLAP
- Star Schema vs Snowflake Schema
- Overview of Power BI
- Power BI Desktop
- Connecting Power BI with different data sources
- Power Query for Data Transformation
- Data Modeling
- DAX
- Reports & Visualization types
- Dashboards
- Publishing Reports
- Power BI Service

Why choose us?

There are several reasons why you should choose our Quality Assurance/Manual Testing course:

Experienced Instructors: Our instructors are industry experts with extensive experience in software testing and quality assurance. They bring real-world knowledge and practical insights into the classroom, ensuring that you receive high-quality training.
Comprehensive Curriculum: Our course covers a wide range of topics related to software testing, providing you with a well-rounded education in this field. You will learn both theoretical concepts and practical applications, allowing you to develop a solid foundation in quality assurance and manual testing.
Practical Learning: Our course emphasizes practical learning and hands-on experience. You will have the opportunity to work on real-world projects, create test plans, execute test cases, and analyze test results, giving you the practical skills and knowledge needed to excel in the field.
24/7 Expert Support: We understand that learning doesn't stop after the class is over. Our instructors are available 24/7 to provide expert support and guidance, ensuring that you have access to assistance whenever you need it.
Flexible Learning Options: We offer flexible learning options online, allowing you to learn at your own pace and convenience. You can access course materials and lectures from anywhere, at any time, making it convenient for those with busy schedules.
Free Re-enrolment: We value your commitment to learning. Once you have enrolled in our course, you can re-join any other batch free of cost providing you with the opportunity to reinforce your learning and further enhance your skills.

Join us now and take your career in software testing to the next level. Contact us at [email protected] to get started on your learning journey today!

Instructor

Bhaskar

Azure Data Engineering Trainer

3527 Reviews 4.4 Rating
3910 Students
16 Courses

Bhaskar brings 14+ years of strong industry experience in Data Engineering, Business Intelligence, Azure Data Factory (ADF), Azure Databricks, Data Warehousing, Analytics, Development, Testing, and Production Support across multiple domains

Bhaskar has been delivering hands-on Azure Data Engineering trainings for the past 5 years with strong \ real-time practical knowledge in ADF pipelines, Databricks, Star/Snowflake schemas, and modern data solutions

Hands-On Courses

Section HAC W1.1 : Cloud Introduction

On-premises vs Cloud-based servers
Cloud Computing basics
Setting Up Azure Account
Introduction to Microsoft Azure
Applications of Azure
Data Engineering processes
Azure Data Engineer roles & tasks
Use cases for the Cloud
Azure Datalake Storage (ADLS) gen2
Azure Blob Storage
Azure SQL database

Section HAC W1.2 : Overview Of ADE Services

Azure Data Factory
Azure Databricks
Azure Synapse Analytics
Microsoft Fabric

Section HAC W1.3 : Azure Data Storages

File stores
Relational data store
Non-Relational data stores
Azure Blob storage
Azure Blob Storage
Azure Data Lake Storage
Why Data Lake
Azure SQL Database

Section HAC W2.1 : Azure Data Factory

Introduction to Azure Data Factory (ADF)
ETL vs ELT
On-prem vs Cloud ETL tools
Why ADF
ADF Components
Linked Services in ADF
Integration Runtime (IR):
1. Azure
2. Self-hosted
3. SSIS
Pipelines and activities
1. Copy data
2. Control flow
3. Data Flow
Data Ingestion methods
Full load
Copy data tool
Batch load
Sequential load
Incremental load
Working with various Copy data scenarios

Section HAC W2.2 : ADF Scenarios

Working with Azure SQL database
Working with Parameters in ADF
ADF Data Migration
Scheduling & Triggering Pipelines
Monitoring Pipelines
CI/CD in ADF

Section HAC W3.1 : SQL

SQL commands for Data Analysis (DDL & DML)
Aggregate Functions & Grouping data
SQL Joins & Set Operators
Advanced Data Analysis: Subqueries
Data Cleaning & Transformation
Window & Logical Functions
String & Date Functions
Analytical Functions

Section HAC W3.2 : Python Basics

SQL commands for Data Analysis (DDL & DML)
Aggregate Functions & Grouping data
SQL Joins & Set Operators
Advanced Data Analysis: Subqueries
Data Cleaning & Transformation
Window & Logical Functions
String & Date Functions
Analytical Functions
Variables
Datatypes in Python
Operators in Python
1. Arithmetic
2. Logical
3. Comparison
Working with Strings
Indexing
Slicing
Data Structures in Python
1. List
2. Tuples
3. Sets
4. Dictionaries
Functions
1. Built-in
2. User defined
Conditional Flow Statements
1. If condition
2. While loop
3. For iteration
Range() function
Map
Filter
Reduce
Lambda expressions

Section HAC W4.1 : Spark Introduction

Introduction to Apache Spark
Hadoop vs Spark
Understanding Spark Architecture
Spark Context
Spark APIs
Spark Data structures
1. RDDs
2. DataFrames
Introduction to Azure Databricks
Azure Databricks Free Edition
Databricks Runtime Engine

Section HAC W4.2 : Azure Databricks Introduction

Databricks Components:
- Workspace
- Cluster
- Catalog
- DBFS
- Tables
- Hive Metastore

Section HAC W4.3 : Databricks Utils

Volume
- Volume Introduction
- Creating Volume
Working with Files using DBUTILS
- Notebook Introduction
- Creating Directory
- Copy Command
- Move Command
- List Command
- Remove Command
- Use Cases

Section HAC W4.4 : RDD(PySpark)

RDD
- Integrating ADLS with Databricks
- Mounting with Access Keys
- Creating Spark RDDs
- Spark Transformations
- Spark Actions
- Lazy Evaluation
- Fault Tolerance

Section HAC W5.1 : DataFrames(PySpark)

DataFrames
- Dataframe Introduction
- Creating Spark DataFrames
- Reading data from different files
- Column manipulations
- Group by & Aggregate Functions
- Joins in PySpark
- Caching
- Schema evolution
- DAG
- Writing data to different formats
- Partitioning data using Partition by
- Window Functions

Section HAC W5.2 : Delta Lake

Spark SQL
1. Creating View
2. SQL Statements on view
Creating Tables
1. using UI
2. using Notebooks
Delta Lake
Creating Delta Tables
1. Managed tables
2. External tables
Lakehouse
Medallion Architecture
1. Bronze layer
2. Silver layer
3. Gold layer
Scheduling Databricks Jobs using ADF Pipelines
Delta Live Tables (DLT)
Unity Catalog

Section HAC W6.1 : Microsoft Fabric

Introduction to Microsoft Fabric
Fabric Analytics platform
Fabric OneLake
Fabric Data Factory
Data Pipelines
Dataflows gen2
Transforming data
Loading data to Lakehouse
Copy job
Create Pipeline in Fabric
Transform data with Dataflow gen2
ADF to Fabric migration
Fabric Synapse Data Engineering
Fabric Notebooks
Analyze data with Apache Spark
Lakehouse
Delta tables
Fabric Synapse Datawarehouse
Creating a Warehouse in Fabric
Ways to Load data into Warehouse
Creating Tables in Warehouse
Loading Data using COPY INTO Command
Loading Data using Pipeline to Warehouse
Loading Data using DataFlow Gen2
Lakehouse vs Warehouse
Microsoft Power BI in Fabric
Access data using PBI Desktop
Direct Lake
Direct Query
Row-level security
Column-level security

Section HAC W7.1 : Power BI

Introduction to Datawarehouse
OLTP vs OLAP
Star Schema vs Snowflake Schema
Overview of Power BI
Power BI Desktop
Connecting Power BI with different data sources
Power Query for Data Transformation
Data Modeling
DAX
Reports & Visualization types
Dashboards
Publishing Reports
Power BI Service

Section HAC W8.1 : Real Time Project

Overview Of Project
Creating Azure Services
Setting up Data Stores for Source
Creating Metadata tables
Masking Credentials with Key Vaults
Creating/Setting Azure DevOps Repo
Creating Working Branch
Creating PR
Merging Code with Collaboration branch
Creating Dynamic Linked Services
Creating Dynamic Datasets
Creating Metadata Driven Pipelines
Creating Framework for Bronze Layer
Creating Framework for Silver Layer
Creating Notebook for every table
Applying Required Transformations
Creating Job Cluster in Azure Data Factory
Creaing Trigger to call master pipeline
Scheduling Trigger to call master pipeline
Loading Data From Gold Layer To Power BI
Creating Dimenional Modeling
Creating Measures
Creating Report
Publishing Report to Power BI Service

FAQ

A:The HAC's Microsoft Data Engineering training provides a deep grasp of Azure data engineering, enabling you to create, deploy, and oversee data solutions on Azure. Completing the DP-700 course earns you vital skills and certifications for career advancement.

A:Beginners can easily acquaint themselves with Azure data engineering services, as it is a user-friendly, cloud-based platform. To understand its capabilities and functionality, proper guidance and a well-structured training path are essential. Those interested in pursuing a career in data engineering using Azure can enroll in our online training program to earn certificates, showcasing their expertise in this domain.

A: Our instructors are experienced industry professionals having over 20 years of experience with extensive knowledge and expertise in software testing and quality assurance. They bring real-world experience and practical insights into the classroom, ensuring that you receive high-quality training from industry experts.

A: Yes, our course is available online, allowing you to learn at your own pace and convenience. You can access course materials, lectures, and assignments from anywhere, at any time, making it convenient for those with busy schedules.

A: We provide 24/7 expert support to our students. You can reach out to our instructors for guidance, clarification, or doubt clearing at any time during the course. We are committed to ensuring that you have the necessary support to succeed in your learning journey.

A: The duration of the course may vary, but typically it is designed to be completed in three weeks. However, as a benefit, once you have enrolled in the course, you can rejoin any other batch free of cost to reinforce your learning.

A: Yes, we provide a certification upon successful completion of the course. The certification validates your skills and knowledge in manual testing and quality assurance, which can be a valuable asset in your career advancement.

A: No, prior experience in software testing or IT is not necessary. Our course is designed for beginners as well as professionals who want to enhance their skills. Our instructors will provide the necessary guidance and support to help you understand the concepts and excel in the course.

A: We strive to provide high-quality training, but if you are not satisfied with the course, we offer a refund policy. Please refer to our refund policy for details.

A: Yes, you will have lifetime access to the course materials, lectures, and assignments even after the course completion. This allows you to revisit the content and reinforce your learning whenever needed.

Enrol Now

Enrolled619
Batches50
Skill LevelBasic-Advanced
LanguageEnglish
CertificateYes
Pass Percentage95%

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HANDS-ON Job Oriented Training for Agile, Scrum, and Beyond

Hands-on Azure Data Engineer & Analyst Training

About HAC

Why Choose HAC for Azure Training?

About the Founder

Watch Our Real-Time Azure Data Engineer Demo Class

Course Overview

Course Objectives

What prior knowledge is required for enrolling in the Azure Data Engineering Course?

What are the benefits of becoming an Azure Certified Data Engineer?

What are the goals of our Azure Data Engineer Certification Course?

What skills does an Azure Data Engineer need?

Course Schedule — Key Topics Covered

Cloud & Azure Foundations

Azure Data Factory (ADF)

SQL & Python Basics

Apache Spark & Databricks Intro

PySpark DataFrames & Delta Lake

Microsoft Fabric Data Engineering

Power BI & Data Warehousing

Real-Time End-to-End Project

DATA ANALYTICS

Why choose us?

Instructor

Bhaskar

Hands-On Courses

Week-1:Courses for Week 1

Week-2:Courses for Week 2

Week-3:Courses for Week 3

Week-4:Courses for Week 4

Week-5:Courses for Week 5

Week-6:Courses for Week 6

Week-7:Courses for Week 7

Week-8:Courses for Week 8

FAQ

Q: What are the main points to remember from the HAC Azure Data Engineer Online Training?

Q:How can beginners learn Azure Data Engineering?

Q: What are the qualifications of the instructors?

Q: Is this course available online?

Q: What kind of support will I receive during the course?

Q: What is the duration of the course?

Q: Is there a certification provided upon completion of the course?

Q: Do I need any prior experience in software testing or IT to enroll in this course?

Q: Can I get a refund if I am not satisfied with the course?

Q: Can I access the course materials after the course completion?

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