AWS Data Engineering Course in Delhi
Professionals interested in becoming data engineers often attend AWS Data Engineering courses in Delhi, Tilak Nagar to build careers utilizing cloud-based data platforms and big data analytics. AWS Data Engineering training equips learners with job-ready skills necessary for designing, creating and managing scalable pipelines using Amazon Web Services – leading institutes offer industry-align programs tailored specifically for today’s cloud infrastructure requirements 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 Delhi
- 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 Delhi, you’ll gain a solid grounding in building scalable, secure, and cost-effective cloud data pipelines. Starting with AWS fundamentals and evolving to advanced data engineering services and real world implementation projects – you will leave our AWS Data Engineering course equipped to build effective cloud pipelines!
Who Can Go for an AWS Data Engineering Course in Delhi
An AWS Data Engineering Course in Delhi is ideal for engineering students, IT professionals, data analysts and software developers in the cloud environment as well as anyone planning a transition into cloud-based roles – even beginners! While prior knowledge of SQL or programming would prove helpful but we will guide participants starting from core fundamentals.
Want to Discuss Your Roadmap to Become an AWS Data Engineer in Delhi?
Looking forward to embarking upon or further developing a career in AWS Cloud Data Engineering? Having taken this journey yourself or seeking someone else who could provide guidance.
Our experts work closely with you to develop a tailored learning roadmap covering Amazon Web Services (AWS), hands-on cloud projects, certification guidance and career transition support as well as interview prep to ensure 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, Delhi
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
About Palin Analytics
Palin Analytics in Delhi is an analytics and cloud training institute focused on connecting academic knowledge to real world needs in industry. Through hands-on labs, live projects, expert mentorship and personalized student attention, Palin Analytics equips its learners for successful careers in AWS Data Engineering.
FAQ's
Assuming This Is True (50 Words Each), What Topics/Services Will Be Covered Within This AWS Data Engineering Training in Delhi?
This course covers AWS fundamentals, S3, RDS, DynamoDB and Redshift with AWS Glue EMR Kinesis IAM security data security ETL/ELT pipeline workflow orchestration monitoring cost optimization projects using real life cloud data engineering projects as examples.
Basic knowledge of SQL, databases or programming concepts may be advantageous but isn’t mandatory – beginners are welcome as the course begins with AWS fundamentals before moving towards advanced data engineering services.
Most courses typically last 3-6 months depending on learning mode and batch type; there are weekday and weekend schedule options to accommodate both students and working professionals.
Yes! Our course features hands-on labs and real world AWS projects as well as case studies designed to develop practical experience building cloud based data pipelines, warehouses and streaming solutions in preparation for job readiness.
Yes, learners receive a shareable course completion certification upon successful completion of this program. This credential validates AWS data engineering skills while broadening job prospects within cloud or data engineering roles.