AWS Data Engineering Course in Gurgaon
AWS Data Engineering courses in Gurgaon are in-demand among professionals seeking careers in cloud-based data platforms and big data analytics. AWS Data Engineering training equips learners with job-ready skills for designing, building, and managing scalable data pipelines using Amazon Web Services – leading institutes offer AWS Data Engineering programs specifically tailored to modern cloud infrastructure and big data requirements.
- English
- English, Hindi
Upcoming Batch Weekdays!!!
09:00 am – 1:00 pm Weekends
09:00 am – 1:00 pm Weekdays
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 AWS Data Engineering course in Gurgaon
- Amazon S3
- Amazon RDS
- Amazon Redshift
- Amazon Dynamo
- Amazon Glue
- Amazon EMR
- Amazon Kinesis
- AWS Pipeline
- Amazon Athena
- Amazon Lambda
- AWs Data Sync
- Amazon Step Function
At our AWS Data Engineering course in Gurgaon, you will gain a firm foundation in creating scalable, secure, and cost-effective cloud data pipelines. Beginning with AWS fundamentals and progressing through to advanced data engineering services and real world cloud projects.
Who Can Go for an AWS Data Engineering Course in Gurgaon
This AWS Data Engineering course in Gurgaon is ideal for engineering students, IT professionals, data analysts, software developers and aspiring data engineers seeking to transition into cloud-based roles. Prior knowledge of SQL or programming concepts may be beneficial; however beginners will be taught from core fundamentals.
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Our experts help you create a customized learning roadmap–spanning AWS skills, hands-on cloud projects, certification guidance, career transition support, and interview preparation–that will set you up for success as an AWS 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 AWS Data Engineering Course in Palin Analytics
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
- Amazon S3 (Simple Storage Service) for scalable object storage
- Amazon RDS (Relational Database Service) for managing relational databases
- Amazon DynamoDB for NoSQL database storage
- Amazon Redshift for data warehousing and analytics
- AWS Glue for ETL (Extract, Transform, Load) and data preparation
- Amazon EMR (Elastic MapReduce) for processing large amounts of data using Hadoop, Spark, or other big data frameworks
- Amazon Kinesis for real-time data streaming and processing
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
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
- Amazon Athena for querying data in S3 using SQL
- Amazon QuickSight for business intelligence and data visualization
- Implementing security best practices for data on AWS
- Managing data governance policies on AWS
- Monitoring data pipelines and optimizing performance and costs
- Using AWS tools for monitoring and optimizing data processing
- Hands-on experience with AWS services for data engineering
- Building data pipelines, processing data, and analyzing data using AWS
What Our Students Say About Us
Data Analytcs Demo classes in Palin Analytics
Palin Analytics of Gurgaon is an analytics and cloud training institute focused on connecting academic learning with real-world industry needs. Through hands-on labs, real-time projects, and expert mentoring services, Palin Analytics enables its learners to launch successful careers in AWS Data Engineering.
FAQ's
Course fees depend on factors like curriculum depth, hands-on labs and certification preparation support provided by institute.
While $500,000 salaries are rare among data engineers at leading global tech firms or in leadership roles, those who possess significant AWS or cloud expertise could garner considerable compensation packages that rival that of top salaries.
Yes, as organizations increasingly migrate data platforms to the cloud. Skilled professionals with knowledge in designing scalable AWS pipelines are being sought across industries.
Yes, data engineers frequently utilize Amazon Web Services like S3, Redshift, Glue and EMR in their efforts to design, implement and optimize cloud-based data pipelines and analytics systems.
AWS Data Engineering may seem complex at first, given all its services and cloud architecture concepts; however, with structured training sessions, hands-on labs, and real project experience it quickly becomes manageable and highly rewarding.
In three months, you can master cloud fundamentals and the fundamental AWS data engineering services. Practice, projects, and real-world exposure will ensure job-readiness as a cloud data engineer.