Azure Data Engineering Course in Gurgaon
Professionals seeking to gain expertise in cloud-based data platforms and enterprise analytics often seek Azure Data Engineering courses in Gurgaon. Learners in these courses gain practical skills for designing, building and managing scalable pipelines using Microsoft Azure services – top institutes offer industry-aligning programs tailored towards modern cloud and big data requirements.
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- English, Hindi
Upcoming Batch Weekdays!!!
Starting from Upcoming Weekend!
09:00 pm – 11:00 pm Weekends
Fully Interactive Classroom Training
- 90 Hours Online Classroom Sessions
- 11 Module 04 Projects 5 MCQ Test
- 6 Months Complete Access
- Access on Mobile and laptop
- Certificate of completion
65,000 Students Enrolled
What we will learn in Azure Data Engineering course Gurgaon in Palin analytics
- Cloud Computing & Microsoft Azure
- Azure Data Engineering Architecture
- Data Storage on Azure
- Data Ingestion using Azure Data Factory
- Data Processing with Azure Databricks & Spark
- Data Warehousing with Azure Synapse Analytics
- SQL & Python for Azure Data Engineering
- Data Security, Governance & Monitoring
- Performance Optimization & Cost Management
At our Azure Data Engineering course in Gurgaon, you will gain hands-on experience creating secure, scalable and high-performing cloud data pipelines. Starting with fundamentals to advanced tools and real world implementations – this course covers them all!
Who Can Go for an Azure Data Engineering Course in Gurgaon
This Azure Data Engineering course in Gurgaon is suitable for engineering students, IT professionals, data analysts, software developers and aspiring data engineers looking to transition into cloud-based roles. Experience with SQL or programming concepts would be beneficial; however beginners will be guided from foundational concepts.
Need Assistance Planning Your Path to Becoming an Azure Data Engineer in Gurgaon?
Are you seeking to embark on or switch careers into Azure-based data engineering?
Our experts can assist in creating a tailored roadmap, covering Azure skills, hands-on projects, certification preparation, career transition guidance and interview readiness to ensure you become a successful Azure Data Engineer.
Advantages
Countless Batch Access
Industry Expret Trainers
Shareable Certificate
Learn from anywhere
Career Transition Guidance
Real-Time Projects
Industry Endorsed Curriculum
Interview Preparation Techniques
Class recordings
Course Mentor
Kushal Dwivedi
- 10 + Batches
- 4.8 Star Rating
- 859 Students Trained
- 450+ Successfully Placed
Hi, I’m Kushal Dwivedi, and I’m excited that you’re here.
Professionally, I am a Data Engineering mentor with strong industry exposure and hands-on experience in building scalable data solutions. I have successfully delivered 10+ batches and trained 859+ students, helping them understand data engineering concepts from fundamentals to advanced levels. With a 4.8-star rating and 450+ successful placements, I focus on practical learning, real-time tools, and industry use cases. In this course, you’ll learn how I combine real-world experience with structured, step-by-step teaching to help you build job-ready data engineering skills.
what is Course Content of Azure Data Engineering Course Content Gurgaon
Introduction to Programming
Basics of programming logic
Understanding algorithms and flowcharts
Overview of Python as a programming language
Setting Up Python Environment
Installing Python
Working with Python IDEsÂ
(Integrated Development Environments)
Writing and executing the first Python script
Python Basics
Variables and data types
Basic operations (arithmetic, comparison, logical)
Input and output (print, input)
Control Flow
Conditional statements (if, elif, else)
Loops (for, while)
Break and continue statements
Functions in Python
Defining functions
Parameters and return values
Scope and lifetime of variables
Lists and Tuples
Creating and manipulating lists
Slicing and indexing
Working with tuples
Dictionaries and Sets
Understanding dictionaries
Operations on sets
Use cases for dictionaries and sets
File Handling
Reading and Writing Files
Opening and closing files
Reading from and writing to files
Working with different file formats (text, CSV)
Error Handling and Modules
Error Handling
Introduction to exceptions
Try, except, finally blocks
Handling different types of errors
Overview of Microsoft Azure
History and evolution of Azure
Azure services and products
Azure global infrastructure
Getting Started with Azure
Creating an Azure account
Azure Portal overview
Azure pricing and cost management
Azure Core Services
Azure Virtual Machines (VMs)
Azure Storage (Blobs, Files, Queues, Tables)
Azure Networking (Virtual Network, Load Balancer, VPN Gateway)
Azure Database Services
Azure SQL Database
Azure Cosmos DB
Azure Storage
Azure Data Lake Storage
Introduction to Azure Data Factory
Overview of Azure Data
Factory and its features
Comparison with other data integration services
Getting Started with Azure Data Factory
Setting up an Azure Data Factory instance
Exploring the Azure Data Factory user interface
Data Movement in Azure Data Factory
Copying data from various sources to destinations
Transforming data during the copy process
Data Orchestration in Azure Data Factory
Creating and managing data pipelines
Monitoring and managing pipeline runs
Data Integration with Azure Data Factory
Using datasets and linked services
Building complex data integration workflows
Data Transformation in Azure Data Factory
Using data flows for data transformation
Transforming data using mapping data flows
Integration with Azure Services
Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
Using Azure Data Factory with Azure Databricks for advanced data processing
Monitoring and Management
Monitoring pipeline and activity runs
Managing and optimizing data pipelines for performance
SQL Advance Queries
SQL Data Models
SQl
Overview of Azure Data
Factory and its features
Comparison with other data integration services
Getting Started with Azure Data Factory
Setting up an Azure Data Factory instance
Exploring the Azure Data Factory user interface
Data Movement in Azure Data Factory
Copying data from various sources to destinations
Transforming data during the copy process
Data Orchestration in Azure Data Factory
Creating and managing data pipelines
Monitoring and managing pipeline runs
Data Integration with Azure Data Factory
Using datasets and linked services
Building complex data integration workflows
Data Transformation in Azure Data Factory
Using data flows for data transformation
Transforming data using mapping data flows
Integration with Azure Services
Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
Using Azure Data Factory with Azure Databricks for advanced data processing
Monitoring and Management
Monitoring pipeline and activity runs
Managing and optimizing data pipelines for performance
Data Modeling: Designing the structure of the data warehouse, including defining dimensions, facts, and relationships between them.
ETL (Extract, Transform, Load): Processes for extracting data from source systems, transforming it into a format suitable for analysis, and loading it into the data warehouse.
Dimensional Modeling: A technique for designing databases that are optimized for querying and analyzing data, often used in data warehousing.
Star and Snowflake Schema: Common dimensional modeling schemas used in data warehousing to organize data into a central fact table and related dimension tables.
Data Mart: A subset of the data warehouse that is designed for a specific department or business function, providing a more focused view of the data.
Fact Table: A table in a data warehouse that contains the primary data for analysis, typically containing metrics or facts that can be analyzed.
Dimension Table: A table in a data warehouse that contains descriptive information about the data, such as time, location, or product details.
ETL Tools: Software tools used to extract data from various sources, transform it into a usable format, and load it into the data warehouse.
Data Quality: Ensuring that data is accurate, consistent, and reliable, often through processes such as data cleansing and validation.
Data Governance: Policies and procedures for managing data assets, ensuring data quality, and ensuring compliance with regulations and standards.
Data Warehouse Architecture: The overall structure and components of a data warehouse, including data sources, ETL processes, storage, and access layers.
Introduction to Azure Databricks
Overview of Azure Databricks and its features
Benefits of using Azure Databricks for data engineering and data science
Getting Started with Azure Databricks
Creating an Azure Databricks workspace
Overview of the Azure Databricks workspace interface
Apache Spark Basics
Introduction to Apache Spark
Understanding Spark RDDs, DataFrames, and Datasets
Working with Azure Databricks Notebooks
Creating and managing notebooks in Azure Databricks
Writing and executing Spark code in notebooks
Data Exploration and Preparation
Loading and saving data in Azure Databricks
Data exploration and basic data cleaning using Spark
Data Processing with Spark
Performing data transformations using Spark SQL and DataFrame API
Working with structured and semi-structured data
Advanced Analytics with Azure Databricks
Running machine learning algorithms using MLlib in Azure Databricks
Visualizing data and results in Azure Databricks
Optimizing Performance
Best practices for optimizing Spark jobs in Azure Databricks
Understanding and tuning Spark configurations
Integration with Azure Services
Integrating Azure Databricks with Azure Storage (e.g., Azure Blob Storage, Azure Data Lake Storage)
Using Azure Databricks in conjunction with other Azure services (e.g., Azure SQL Database, Azure Cosmos DB)
Collaboration and Version Control
Collaborating with team members using Azure Databricks
Using version control with Azure Databricks notebooks
Real-time Data Processing
Processing streaming data using Spark Streaming in Azure Databricks
Building real-time data pipelines
Introduction to Azure Synapse Analytics
What is Synapse Analytics Service?
Create Dedicated SQL Pool Explore Synapse Studio V2
Analyse Data using Apache Spark Notebook
Analyse Data using Dedicated SQL Pool
Monitor Synapse Studio
Apache Spark
Introduction of Spark
Spark Architecture
PySpark
What Our Students Say About Us
Palin Analytics Gurgaon
Palin Analytics of Gurgaon, is a premier analytics and cloud training institute committed to closing the gap between academic learning and real-world industry needs. Through hands-on cloud labs, real-time projects and expert mentorship services, Palin Analytics empowers its learners with skills necessary for building successful careers in Azure Data Engineering.
FAQ's
Azure Data Engineering courses focus on developing cloud-based data pipelines using Microsoft Azure. Topics covered in such courses include Azure Data Factory, Databricks and Synapse Analytics as well as data storage concepts like SQL and Python as well as real-time processing concepts.
Students, IT professionals, data analysts, software developers and aspiring data engineers in Gurgaon should sign up. The course provides invaluable skillsets needed for transitioning into cloud data engineering roles using Azure.
While basic knowledge of SQL, databases and programming concepts is recommended for beginners who wish to join this course, beginners can still join since its first day begins with fundamental Azure concepts before gradually progressing toward advanced data engineering topics.
After successfully completing an Azure Data Engineering course, graduates may apply for roles such as Azure Data Engineer, Cloud Data Engineer, Big Data Engineer, Analytics Engineer or Data Platform Engineer depending upon experience and skill level.
Azure Data Engineering course fees typically range between Rs25,000 and Rs60,000 in Gurgaon depending on length, depth of curriculum, hands-on labs and certification preparation support services provided during training.