About AZURE Data Engineer
Faculty Profile
Course Syllabus

Classroom-Based Azure Data Engineering Training in Hyderabad

Want to pursue a great career in cloud data engineering? When you are the type of person who prefers to learn within a physical setting, then our Azure Data Engineering Training in Hyderabad is what you need. It is a classroom course that aims to provide you with practical skills, with hand-on experience, and with an expert guidance all within the real-time learning environment.

Are you looking to further your career in IT or are you just starting out? This program assists you in acquiring skills that are in demand and makes you ready to take the DP-203 certification exam provided by Microsoft.

Why Go for Offline Azure Data Engineering?

Online training can be advantageous but there are a lot of learners who receive more advantages when it comes to training in a classroom setup. Face-to-face training provides you with the opportunity to communicate face-to-face with the trainer with real-time interaction, you also have a clean learning environment free of distractions. That is precisely what our Azure Data Engineering course in Hyderabad provides you with an immersive learning experience supplemented with the live Q&A, group work, and practical sessions.

You will use real-life use cases and projects, which will help you to comprehend the application of Azure data services in the industry much easier.

Learn Azure Data Engineering from Industry Experts at Version IT

At Version IT, we’ve built a strong reputation as one of the most trusted Azure Data Engineering Training Institutes in Hyderabad . Our offline classes are led by experienced trainers who’ve worked on real Azure projects and know exactly what’s expected in the job market.

Here’s what makes our classroom program stand out:

  • In-depth sessions taught by certified Azure professionals
  • Fully equipped labs with real-time project practice
  • Job placement support and soft skills training
  • Small batches to ensure personal attention
  • Interview preparation and resume guidance’

Who Should Join This Azure Data Engineering Training?.

This offline training is ideal for:

  • Freshers looking to get into cloud or data careers
  • Analysts and software developers
  • IT professionals who prefer classroom learning

No matter what your background, our structured approach helps you build skills step by step, from basics to advanced concepts.

More Than Just Training – We Help You Get Hired.

Our focus isn’t just on teaching tools — we want to help you succeed. Along with technical skills, you’ll receive:

  • Interview coaching and mock sessions
  • Resume building and LinkedIn profile support
  • Guidance for clearing the DP-203 exam
  • Referrals to hiring partners and top companies

Many of our students have gone on to work at companies like TCS, Infosys, Wipro, and Deloitte after completing our training.

Take Part in Best Training Program for Azure Data Engineering.

If you’re serious about a future in cloud data engineering, join our offline Azure Data Engineering Course in Hyderabad at Version IT. Our classroom training combines expert instruction, hands-on labs, and career support to give you everything you need to succeed.

Contact us today or drop by our institute to learn more about batch timings, fees, and upcoming demo sessions. Your future in cloud starts here.

Sri Ram Sr.Consultant

SRI RAM is a versatile mentor having Through knowledge in Azure Data Engineer. With his vast Experience he served in top most MNC's .With his unique teaching methods sriram trained hundreds of students and professionals in Azure.He Guided many aspirants by assisting them to get the job Opportunities.

Foundations of Data Engineering

1. Fundamentals of Cloud Computing

  • What is Cloud Computing?
  • Cloud Deployment Models
    • Private Cloud
    • Public Cloud
    • Hybrid Cloud
  • Cloud Service Models
    • IaaS – Infrastructure as a Service
    • PaaS – Platform as a Service
    • SaaS – Software as a Service

2. Overview of Major Cloud Providers

  • Introduction to Leading Cloud Platforms
    • Microsoft Azure
    • Amazon Web Services (AWS)
    • Google Cloud Platform (GCP)

3. Getting Started with Microsoft Azure

  • Introduction to Azure and Its Ecosystem
    • Navigating the Azure Portal
    • Understanding Subscriptions
    • Creating and Managing Resource Groups
  • Azure Resources Overview

4. Core Azure Services for Data Engineering

  • Azure Data Factory
  • Azure Databricks
  • Azure Blob Storage & Data Lake Gen1 / Gen2
  • Azure SQL Server & Azure SQL Database
  • Azure Key Vault for Secrets Management

5. Introduction to Big Data Concepts

  • What is Data vs Big Data?
  • Common Sources of Big Data
  • The 5 V’s of Big Data:
  • Volume, Variety, Velocity, Veracity, Value
  • Types of Data:
  • Structured
  • Semi-Structured
  • Unstructured

6. Python Programming Essentials

  • Python Syntax and Structure
  • Core Concepts:
    • Variables and Data Types
    • Operators
    • Collections – Lists, Tuples, Sets, Dictionaries
    • Functions and Parameters
    • If-Else, For and While Loops

7. SQL for Data Handling

  • SQL Language Fundamentals
    • DQL – SELECT
    • DDL – CREATE, ALTER, DROP, TRUNCATE
    • DML – INSERT, UPDATE, DELETE, MERGE
  • Filtering and Conditions – WHERE, AND, OR, NOT, IN, BETWEEN, LIKE, IS NULL, CASE WHEN
    • Sorting and Limiting – ORDER BY, ASC, DESC, LIMIT, FETCH FIRST
  • Working with Joins – INNER, LEFT, RIGHT, FULL, CROSS, SELF JOIN
    • Aggregate Functions – SUM, AVG, COUNT, MIN, MAX
  • Grouping and Filtering
    • GROUP BY, HAVING

Azure Data Factory

1. Introduction to ADF

  • What is Azure Data Factory?
  • Use Cases and Benefits
  • ADF Architecture Overview
  • Navigating the ADF Interface
  • Key Concepts
    • Pipelines, Activities, Linked Services
    • Datasets, Triggers, Data Flows
    • Integration Runtimes

2. Building Pipelines

  • Purpose and Usage
  • Creating Linked Services for
    • Azure Blob, SQL, ADLS Gen1 / Gen2, Oracle, PostgreSQL
  • Parameterizing Linked Services

3. Linked Services

  • Purpose and Usage
  • Creating Linked Services for
    • Azure Blob, SQL, ADLS Gen1 / Gen2, Oracle, PostgreSQL
  • Parameterizing Linked Services

4. Working with Datasets

  • Creating Datasets for File Formats
    • Avro, Binary, CSV, Excel, JSON, ORC, Parquet, XML
  • Datasets for Tabular Sources
    • SQL Server, Azure SQL, Oracle
  • Parameterization in Datasets

5. Activities in ADF

  • Core Activities
    • Wait, Variable (Create / Set / Append)
    • Copy Activity
    • General Settings, Source / Sink, Mapping
    • File Copy – Single, Multiple, Recursive
    • Format Conversions (CSV, Parquet, Avro)
  • Integration Activities
    • Databricks, Azure Functions, Stored Procedures
    • Lookup, Get Metadata, Delete, Execute Pipeline
  • Iteration and Conditionals
    • Filter, ForEach, If Condition, Switch, Until

6. Triggers

  • Introduction to Triggers
  • Trigger Types
    • Schedule
    • Tumbling Window
    • Event-Based (Blob Events)
  • Trigger Parameterization

7. Integration Runtime (IR)

  • Azure Auto-Resolve IR
  • Managed Virtual Network IR
  • Self-Hosted IR and Linked IR

8. Source Control and CI / CD

  • Integrating with Git (Azure DevOps / GitHub)
  • Using ARM Templates – Exporting, Importing
  • Source Control Best Practices

9. Global Parameters

  • Creating and Using Global Parameters

10. Monitoring and Alerts

  • Monitoring Pipelines and Activities
  • Setting up Alerts and Notifications

11. Data Flows

  • Creating Mapping Data Flows
    • Flatten, Parse
    • Alter Row, Assert, Flowlet
  • Using Data Flow Debug
  • Transformations
    • Filter, Aggregate, Join
    • Conditional Split, Derived Column
    • Exists, Union, Lookup, Sort
    • Group By, Pivot, Unpivot
  • Schema Validation and Drift Handling
  • Duplicate Removal

Azure Databricks & PySpark

1. Introduction to Apache Spark

  • Spark Architecture and Internals
  • RDDs and DataFrames
  • Spark Streaming Basics
  • Spark vs Hadoop Comparison

2. Getting Started with Databricks

  • What is Databricks?
  • Architecture and Workspace Overview
  • Using the Notebook Interface
  • Introduction to DBFS
  • File Handling with dbutils

3. Core Spark with Databricks – RDDs

  • RDD Programming and Transformations
    • Narrow vs Wide Transformations
    • Lazy Evaluation
  • Key-Value RDDs

4. Spark SQL & DataFrames

  • Creating and Transforming DataFrames
  • DataFrame Actions and Queries
  • User Defined Functions (UDFs)
  • Execution Internals

5. File Formats in Databricks

  • Reading and Writing File Formats
    • CSV, JSON, Parquet, Excel, ORC
  • Schema Inference and Handling

6. Databricks Utilities

  • dbutils Usage
  • Credentials, File System (FS), Notebooks, Secrets, Widgets

7. Cluster Management

  • Creating and Configuring Clusters
  • Cluster Modes
    • Job vs All-Purpose
    • Standard vs High Concurrency
  • Autoscaling and Runtime Versions

8. Batch Processing Workloads

  • Historical vs Incremental Loads
  • Data Transformations, Joins and Aggregations
  • Using Unions and Window Functions

9. Integration with Azure Services

  • Blob Storage and ADLS Gen2
  • Azure SQL, Azure Synapse and Azure Key Vault

10. Structured Streaming

  • Streaming Architecture and Use Cases
  • PySpark Stream Processing
  • Handling Bad Records and Stream to Table

11. Delta Lake and Lakehouse

  • Delta Architecture and Features
  • Delta Table Creation, Merge and DML Operations
  • SCD Type 1 and Type 2
  • Duplication Handling and Streaming Integration

12. Medallion Architecture

  • Bronze Layer – Raw Data
  • Silver Layer – Cleansed and Enriched Data
  • Gold Layer – Curated Data for Analytics

13. Job Orchestration

  • Using Databricks Workflows
  • Job Scheduling and Monitoring

14. Unity Catalog

  • Implementing Data Governance with Unity Catalog

Microsoft Fabric

1. Introduction to Microsoft Fabric

  • What is Microsoft Fabric?
  • Key Features and Architecture
  • Core Components
    • Data Engineering
    • Data Factory
    • Synapse Integration
    • OneLake Storage
  • Microsoft Fabric vs Azure Synapse vs Databricks
  • Creating a Fabric Workspace

2. Data Ingestion

  • Batch vs Streaming
  • Connecting to Sources
    • Azure Blob, ADLS, SQL, REST APIs
  • ETL with Fabric Pipelines
  • Real-Time Ingestion with Event Streams
  • Handling Structured and Unstructured Data

3. OneLake – Unified Storage

  • What is OneLake?
  • OneLake vs ADLS
  • Lakehouses and Delta Table Management
  • Security and Access Control

4. Spark in Fabric

  • Spark Engine Overview
  • Creating and Running Notebooks
  • PySpark Transformations
  • Performance Tuning Best Practices
  • Job Monitoring and Scheduling

5. Dataflow (Low-Code)

  • Dataflow Gen2 Introduction
  • Power Query and M Language
  • Managing and Automating Dataflows
  • Integration with Fabric Data Factory
  • Debugging and Monitoring

6. SQL Analytics & Modeling

  • Fabric SQL Data Warehouse Overview
  • Query Writing and Optimization
  • Implementing SCD Type 1 & Type 2
  • Materialized Views and Incremental Processing

7. Orchestration, Automation & CI/CD

  • Microsoft Fabric DataFactory Overview
  • Pipeline Scheduling and Monitoring
  • Integrating with Synapse & Power BI
  • Error Handling and Logging
  • CI/CD Deployment Best Practices

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