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Azure Data Engineer Course Training Options

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Group Classes

Join in-class training in Chennai, Bangalore and Hyderabad that promotes cooperation, peer guidance, and project discussions. Course offerings are available on weekdays and weekends.

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One-to-One Mentorship

Schedule your personalized sessions at our leading Azure Data Engineer training institute in India, which is designed according to your learning speed and goals, allowing focused attention and customized guidance.

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Live Online Sessions

Engaging virtual classes can be taken at home with reputed industry experts. You will receive materials and sessions that are recorded for you to review at any time.

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Corporate Team Training

Help your team elevate their skills with the best Azure Data Engineer training in India, which includes sessions designed for your organization’s specific needs, goals, and objectives. Ideal for organizations looking to improve their Azure Data Engineer capabilities.

The Best Azure Data Engineer Online Training Institute in India offers 100% job-oriented courses in Chennai, Bangalore, Hyderabad, Pune, Mumbai, Coimbatore, Noida, Delhi, Kochi, Kolkata, Ahmedabad and Gurgaon.

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Azure Data Engineer Course Content

Programming Foundations & Data Modeling

  • Module 1: Python for Data Engineering — Python fundamentals, setup & environment, variables, data types, functions, modular code, loops, conditions and logic building.
  • Data Structures — Lists, tuples, sets, dictionaries and iteration techniques.
  • Pandas & NumPy — Series & DataFrames, data reading/writing, cleaning, filtering, sorting, aggregation and numerical operations.
  • Scripting & Automation — Running scripts, best practices and building reusable components.
  • Module 2: Data Handling with Python — Working with CSV, Excel and JSON, handling missing data and type conversions.
  • Data Cleaning & Transformation — Filtering, grouping, merging, joining and reshaping data.
  • Data Analysis Essentials — EDA, summary statistics, Matplotlib and Seaborn visualization.
  • Module 3: SQL Fundamentals — SELECT, WHERE, JOINs, filtering and sorting.
  • Query Writing & Optimization — Subqueries, indexing and execution plans.
  • Database Design Fundamentals — Normalization, keys and relationships.
  • Hands-on Practice — Real-world query-writing drills and exercises.

Cloud & Security

  • Module 4: Cloud & Microsoft Azure Foundations — Cloud computing basics, public/private/hybrid cloud models and real-world use cases.
  • Azure Fundamentals — Navigating Azure Portal and understanding key Azure services.
  • Cloud Service Models — IaaS, PaaS and SaaS with real-world examples.
  • Azure Environment Setup — Creating an account, first resource group and hands-on resource launch.
  • Module 5: Secure Identity & Access Management in Azure — Identity & access management, authentication vs authorization.
  • Microsoft Entra ID — Tenants, users, groups, roles and Role-Based Access Control (RBAC).
  • SPN & Managed Identity — Service principals and when to use each identity approach.
  • Security Best Practices — Secure access to storage, databases and Azure services.

Storage & Integration

  • Module 6: Azure Storage & Data Lake Fundamentals — Storage account basics and architecture for modern data platforms.
  • Blob Storage Essentials — Containers, folder structures, block blobs, append blobs and page blobs.
  • Advanced Storage Concepts — Metadata storage, audit logs and configuration management.
  • Queue Storage & Workflows — Table storage and queue storage for data engineering workflows.
  • Module 7: Azure Data Lake Storage Gen2 — ADLS Gen2 architecture and hierarchical namespace.
  • Working with Data Lake — File systems, directories, files and configuration.
  • Data Ingestion & Management — Ingesting and accessing data in ADLS Gen2.
  • Data Lake Best Practices — Design concepts, performance optimization and cost optimization.
  • Module 8: REST APIs & Data Integration Fundamentals — REST APIs in data engineering and REST vs SOAP.
  • API Integration Concepts — Real-world data integration scenarios.
  • Working with APIs — Testing tools and CRUD operations.
  • API Data Handling — Data extraction and error management.
  • Module 9: Azure Data Factory — ADF architecture and pipeline execution flow.
  • Pipelines, Datasets & Integration Runtime — Linked services and dataset types.
  • Data Integration & Orchestration — Connecting Storage, SQL and REST services, scheduling and triggers.
  • Monitoring & Best Practices — Debugging, optimization and production-ready pipelines.

Processing (Azure Data Engineering)

  • Module 10: PySpark Foundations — Introduction to Databricks & Spark, SparkSession, execution flow, transformations vs actions and lazy evaluation.
  • Core Concepts & Data Structures — RDDs, DataFrames and Spark SQL.
  • Data Ingestion in PySpark — CSV, JSON and Parquet with schema inference.
  • Data Output in PySpark — DataFrame APIs, save modes and partitioning.
  • Module 11: Data Processing with PySpark — Joins, aggregations, data cleaning and type conversions.
  • Advanced Analytics with Spark SQL — Window functions and complex queries.
  • Performance Optimization — Partitioning, shuffle, caching and persistence.
  • Monitoring & Tuning — Spark UI, stages, tasks, executors and tuning best practices.
  • Module 12: Databricks Platform, Delta Lake & Governance — Databricks architecture, workspace, notebooks and clusters.
  • Data Connectivity & Storage — Connecting to ADLS Gen2, mounting and secure access.
  • Delta Lake Essentials — Delta architecture, Delta tables and ACID transactions.
  • Governance & Unity Catalog — Permissions, access control and data governance.
  • Module 13: Databricks Lakehouse Architecture & Pipelines — Lakehouse and Medallion architecture with Bronze, Silver and Gold layers.
  • Building Data Pipelines — Batch and real-time streaming pipelines.
  • Advanced Pipeline Concepts — Incremental processing and Change Data Capture (CDC).
  • Use Cases & Best Practices — Cost optimization and scalable pipeline design.

Microsoft Fabric

  • Module 14: Introduction to Microsoft Fabric — What Microsoft Fabric is and how it differs from Azure Data Engineering.
  • Microsoft Fabric Components — Unified platform for Data Engineering, Data Science and analytics.
  • Real-time Analytics in Fabric — Benefits for modern data engineering.
  • Microsoft Fabric Eventhouse — Real-time data ingestion and processing.
  • Module 15: Data Integration & Orchestration with Fabric — Dataflows Gen2 and data integration.
  • Pipelines for Data Engineering — Pipeline concepts and templates.
  • Run & Monitor Pipelines — Pipeline execution and performance tracking.
  • Real-time Data Processing — Ingest, transform, store, query and visualize data.
  • Module 16: Lakehouse, Delta Tables & Warehousing in Fabric — Lakehouse and Medallion architecture using Bronze, Silver and Gold layers.
  • Delta Tables in Fabric — Creating and handling Delta tables using Spark and structured streaming.
  • Data Warehousing in Fabric — Fact tables, dimensions and loading data using T-SQL.
  • Dataflows & Data Management — Loading, transforming, managing and protecting data.
  • Module 17: CI/CD, Deployment & Governance in Fabric — Data loading and pipeline strategies for Fabric Warehouse.
  • CI/CD in Microsoft Fabric — Git version control and deployment pipelines.
  • Automation with APIs & KQL — Automating CI/CD tasks and using KQL.
  • Advanced SQL & Analytics — Materialized views and stored functions.

Snowflake

  • Module 18: Snowflake Fundamentals & Architecture — Snowflake basics, use cases and why Snowflake is used for modern data platforms.
  • Setup & Environment — Creating a Snowflake account and navigating the Web UI.
  • Architecture & Storage Layers — Cloud services, compute and storage layers with micro-partitioning.
  • Data Types & Tables — VARIANT data type and permanent, temporary and transient tables.
  • Module 19: Data Loading & Transformation in Snowflake — Time Travel, Fail-safe and Zero Copy Cloning.
  • Stages & COPY Command — File formats and COPY command usage.
  • Streams, Tasks & Automation — SQL-based ETL and scheduling tasks.
  • Real-time ETL & Integrations — Data Lake integration and BI tool connections.
  • Module 20: Performance Optimization & Warehousing — Virtual warehouse sizing, auto-suspend and auto-resume.
  • Performance Tuning — Clustering keys and query profiling.
  • Caching & Optimization — Compute resource management.
  • Schema Design & Best Practices — Star vs snowflake schema and data modeling.
  • Module 21: Security, Governance & Data Sharing — Authentication, RBAC and sensitive data access.
  • Security & Monitoring — Encryption at rest/in transit and auditing.
  • Data Sharing & Masking — Secure data sharing features.
  • Governance Best Practices — Compliance and operational best practices.

Orchestration & Streaming

  • Module 22: Apache Airflow — Airflow fundamentals and components overview.
  • Installation & Setup — Installing Airflow and working with the Web UI.
  • DAGs, Operators & Tasks — Building workflows and managing dependencies.
  • Scheduling & Jobs — Creating, triggering and monitoring jobs.
  • Module 23: Apache Kafka — Kafka fundamentals, its need in data engineering and core concepts.
  • Kafka Architecture — Kafka use cases and cluster components.
  • Kafka Cluster Management — Brokers, topics, partitions, replication, producers and consumers.
  • Reliability & Best Practices — High availability and fault-tolerant Kafka architectures.

Capstone Project

  • Industry-Grade Capstone Project — Apply the concepts learned throughout the curriculum to build an end-to-end data engineering solution.
  • End-to-End Data Engineering — Work with data ingestion, storage, processing, transformation and analytics components.
  • Cloud Data Engineering — Implement a production-oriented solution using relevant Azure and modern data engineering technologies.
  • Portfolio Development — Build a project that demonstrates practical, industry-ready Data Engineer skills.
  • Real-World Scenario — Solve a business-focused data engineering problem using appropriate architecture and engineering practices.

Power BI & Business Intelligence

  • Module 26: Power BI & Business Intelligence — Connecting Power BI to Azure, Snowflake and Databricks data sources.
  • Data Modeling & DAX Basics — Relationships, calculated columns and measures.
  • Building Dashboards & Reports — Visuals, interactivity and drill-throughs.
  • Row-Level Security & Publishing — Securing, publishing and sharing Power BI reports.

DevOps

  • Module 28: DevOps — CI/CD concepts and automation using build and release pipelines.
  • Docker & Kubernetes — Containerization fundamentals and introduction to orchestration.
  • Infrastructure as Code — Automated environment provisioning and infrastructure management.
  • Real-world DevOps Projects — Applied hands-on practice with DevOps tools and workflows.

Upcoming Batch Schedule for Azure Data Engineer Online Course

  • 12-12-2025 Mon (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per SessionGet Fees
  • 15-12-2025 Thu (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per SessionGet Fees
  • 10-12-2025Sat (Sat - Sun)Weekend Batch11:00 AM (IST) (Class 3Hrs) / Per SessionGet Fees
Can’t find a batch you were looking for? Request a Batch

Projects in Azure Data Engineer Training

Sales pipeline Dashboard
Project 1

End-to-End ETL Pipeline (ADF)

Build a complete pipeline in Data Factory that extracts data from Blob Storage, transforms it using Data Flows, and loads it into Azure SQL. Learn mapping, scheduling, and pipeline monitoring.

Sales Growth Dashboard
Project 2

Data Lakehouse with Synapse

Create structured layers in ADLS Gen2, set up external tables, optimize file formats, and learn how enterprises design large-scale warehousing systems in Azure.

Healthcare Data Dashboard
Project 3

Real-Time Streaming with Databricks + Event Hub

Capture streaming events through Azure Event Hub, process data using Spark in Databricks, and store results in Delta Lake. Learn real-time analytics and checkpointing techniques.

Why Our Azure Data Engineer Course Is Trusted in India?

Practical Projects

Hands-on projects based on real industry use cases.

Skilled Instructors

Trainers with strong experience in ADF, Synapse, Databricks, and Data Lake.

Support for Certification

DP-900 & DP-203 certification guidance included.

100% Placement Support

100% placement assistance with structured interview preparation.

Mock interviews

Free practice sessions for SQL, Python, and scenario-based questions

Adjustable Batch Schedules

Flexible batches: weekdays, weekends, classroom, and online formats.

Azure Data Engineer Trainer Profile

Azure Instructor

Instructor:Arun Prakash

Experience:9+ Years Experience

Expertise in: Data Factory, Synapse, Databricks, ADLS, SQL, Python, and Spark

Trainer Profile of Azure Data Engineer Course in India

Arun brings real-world cloud experience from projects in Chennai and Bangalore. He specializes in Data Factory, Synapse, Databricks, ADLS, SQL, Python, and Spark. His background spans BFSI, retail, and healthcare domains, giving learners practical insight into how Azure solutions work in enterprise environments. Arun has mentored more than 700 learners preparing for DP-900 and DP-203, and his teaching style focuses on clarity, hands-on practice, and real project workflows.

How Important Is Azure Data Engineering?

Azure has become essential for organizations handling growing amounts of data. Industries such as finance, retail, healthcare, logistics, and e-commerce depend on Azure-based pipelines to collect, clean, and process data efficiently. Azure Data Engineers build and manage these systems, ensuring data flows smoothly into dashboards, applications, and machine learning models. As companies scale, they rely on Azure to automate data movement, maintain data quality, support real-time analytics, and ensure that business teams have reliable, up-to-date insights for faster decision-making.

Tools like Data Factory, Synapse, Databricks, and Data Lake simplify large-scale data workflows. With cloud adoption rising across India and globally, companies increasingly look for professionals who can work confidently in Azure environments. This has made Azure Data Engineering one of the most future-ready and stable career paths today — especially for learners taking an Azure Data Engineering Certification track or a structured Azure Data Engineer Certification Course.

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Placement Support for Azure Data Engineer Course

Our placement support is designed to help you move into the cloud job market with confidence. You’ll receive:

  • 100% placement assistance through a dedicated job portal
  • Resume-building sessions tailored for data engineering roles
  • Mock interviews conducted by industry professionals
  • Job referrals through hiring partners across India
  • One-to-one mentoring to help you choose the right cloud career path

Whether you’re a fresher or switching domains, the support ensures you're well-prepared for real technical interviews and project discussions.

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Student Feedback for Azure Data Engineer Course

Before joining this Azure Data Engineer Course, I only knew basic cloud concepts. Here, I learned to design full ingestion pipelines using ADLS Gen2, integration runtimes, and ADF flows. The trainer tied each topic to real DP-900 DP-203 Training, which helped me answer architecture questions confidently during interviews.

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Sandeep

Azure Data Engineer Aspirant

I took this program as part of my Azure Data Engineering Certification preparation. The Databricks modules were a game-changer—especially learning Spark clusters, Delta Lake, and notebook-based transformations. The trainer consistently connected the lessons to what’s expected from someone doing an Azure Data Engineer Certification Course, making the learning highly practical.

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Divya

Cloud Data Engineering Trainee

I chose this institute mainly for its Azure Data Engineering Course with Placement. The trainer explained Synapse pipelines, serverless SQL pools, and external tables in a simple, structured way. Building a mini lakehouse project helped me clearly explain end-to-end cloud architecture during interviews, which boosted my confidence.

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Naveen

Data Engineering Career Switcher

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