AWS Data Engineering Course in Patiala
Cloud computing and big data technologies are revolutionizing how organizations manage and analyze their data. An Amazon Web Services (AWS) Data Engineering Course in Patiala equips professionals to construct secure, scalable data pipelines using this service at Amazon Web Services (AWS).
- 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 Patiala
- 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
In our AWS Data Engineering course in Patiala, you will begin by learning AWS fundamentals before progressing toward advanced data engineering services and cloud pipeline design as well as real world implementation projects.
Who Can Go for an AWS Data Engineering Course in Patiala?
The course is ideal for:
- Engineering Students
- IT Professionals and Software Developers,
- Data Analysts,
- Python Developers and Cloud Enthusiasts.
- Professionals looking to transition their career into cloud data roles
Some knowledge of SQL or programming would be advantageous, however it isn’t essential – this course starts from basic AWS fundamentals so as to provide a smooth learning journey.
Want to Discuss Your Roadmap to Become an AWS Data Engineer in Patiala?
Our mentors collaborate closely with you to develop a tailored roadmap, including: AWS Skill Development; Hands-On Cloud Projects, Certification Prep and Resume and Interview Guidance, Career Transition Support as well as Placement Assistance.
With unlimited batch access, expert trainers, and flexible learning options available to you, AWS Cloud Data Engineering is an incredible career path. Request a Call Back Now!
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, Patiala
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 is a professional cloud and analytics training institute dedicated to connecting academic learning with real-world industry needs. Through hands-on labs, live projects, and expert mentorship we equip learners for successful careers in AWS Data Engineering and cloud analytics.
FAQ's
Patiala offers one of the finest AWS Data Engineering courses with hands-on cloud projects, expert trainers, industry-align curriculum and strong placement assistance. Programs including AWS certification guidance, real world case studies and interview prep for optimal career outcomes.
Duration can range between 3 and 6 months depending on batch type (weekday or weekend), fees vary based on curriculum depth, cloud project exposure, certification support and placement assistance; please reach out directly for updated fee details.
Absolutely, beginners may enroll. While basic knowledge of programming or SQL would be beneficial, the course covers AWS fundamentals before moving onto more complex cloud data engineering concepts with structured guidance.
Once complete, you receive a shareable course certification. Furthermore, AWS certifications such as Solutions Architect or Data Analytics Specialty could improve job prospects significantly.
Once completed, you can pursue roles such as AWS Data Engineer, Cloud Data Engineer, Big Data Engineer or ETL Developer. Salaries vary based on experience and skillset but typically AWS professionals tend to attract competitive packages in IT and analytics industries.