Master AWS Data Engineering with Real-Time Projects

Join NVNS Software Solutions and become a job-ready data engineer by mastering AWS, SQL, Python, and big data tools with real-time project implementation designed for faster job placement.

  • Real-Time Project Implementation (End-to-End)
  • Complete AWS Data Engineering Training
  • Certification & Placement Support
  • Beginner to Advanced Level
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Why Choose NVNS Software Solutions AWS Training?

Most courses focus only on theory. Our training focuses on practical, real-time project implementation, which is critical for getting hired.

You will:

  • Work on real-world data engineering use cases
  • Build end-to-end data pipelines
  • Gain hands-on experience with AWS tools
  • Become job-ready with practical skills

👉 Our goal is simple: help you get placed faster with real skills

Course Details:

  • Duration: 3 Months
  • Mode: Online / Offline
  • Level: Beginner to Advanced

What You Will Learn

  • AWS Fundamentals & Architecture
  • Core AWS Services (EC2, IAM, VPC, RDS)
  • Data Storage with Amazon S3
  • Data Processing with AWS Glue
  • Querying with AWS Athena
  • Real-Time Streaming with AWS Kinesis
  • Serverless Compute with AWS Lambda
  • Data Warehousing with Amazon Redshift
  • Workflow Orchestration (Step Functions, Airflow)
     ⭐ Real-Time Data Engineering Project

Real-Time Project Implementation at NVNS Software Solutions

🔧 What You Will Build:

  • Real-world data engineering pipeline
  • Complete flow:
     Data Ingestion → Processing → Storage → Analytics
  • Integration using:
     AWS S3 + Glue + Kinesis + Lambda + Redshift

⚡ Advanced Concepts Covered:

  • Data transformation using PySpark
  • Streaming data processing
  • Performance optimization

🎯 Outcome:
 👉 Gain real project experience
 👉 Showcase projects in interviews
 👉 Get hired faster

Master AWS Data Engineering Curriculum

Module 1: AWS Data Engineering Introduction

  • What is Data Engineering
  • Different Data Engineering Technologies
  • AWS Data Engineering Introduction
  • Introduction of SQL in AWS Data Engineering
  • Introduction of Linux in AWS Data Engineering
  • Introduction of Python in AWS Data Engineering
  • Introduction of PySpark for AWS Data Engineering

Module 2: AWS Basic Services for Data Engineering

  • AWS Cloud Computing Introduction
  • AWS Free Account Creation & Sign-In
  • AWS Services Overview
  • AWS Free Tier
  • AWS Global Infrastructure
  • AWS EC2 Introduction
  • AWS IAM Introduction
  • AWS VPC (Default VPC)

Module 3: Linux for AWS Data Engineering

  • Linux Introduction
  • Linux Filesystem Architecture
  • Linux Installation on EC2 Instance
  • Connecting to Linux Machine
  • Basic Linux Commands
  • File & Directory Permissions
  • Linux Filter Commands

Module 4: SQL for AWS Data Engineering

  • SQL Introduction
  • SQL Language Types: DDL, DML, DCL, TCL
  • MySQL Installation
  • Data Definition Language (DDL)
  • Data Manipulation Language (DML)
  • Data Query Language (DQL)
  • Transaction Control Language (TCL)
  • Data Control Language (DCL)
  • Filtering, Sorting & Joins
  • MySQL Setup using AWS RDS

Module 5: Python for AWS Data Engineering

  • Python Introduction
  • Programming Basics
  • Interactive & Script Mode
  • Python Installation & Setup
  • Anaconda, Jupyter Notebook, Spyder
  • IDE Setup: VS Code, PyCharm
  • Language Fundamentals (Datatypes, Keywords, etc.)
  • Control Flow Statements
  • Collections (List, Tuple, Set, Dictionary)
  • Modules, Packages & Libraries
  • Functions & Lambda Functions
  • Object-Oriented Programming (OOP Concepts)

Module 6: PySpark for AWS Data Engineering

  • PySpark Foundations
  • RDD Programming (Transformations & Actions)
  • PySpark SQL (DataFrames & Tables)
  • DSL & Native SQL
  • PySpark Streaming
  • Database Integration
  • PySpark S3 Integration

Module 7: Storage with AWS S3

  • AWS S3 Introduction
  • Creating S3 Buckets (Console)
  • Uploading Datasets to S3
  • Managing Buckets & Objects
  • Version Control in S3
  • Cross-Region Replication
  • S3 Storage Classes & Tiers
  • Glacier Overview
  • AWS CLI for S3 Management
  • Integration with PySpark, Glue, Lambda, Athena

Module 8: Data Processing with AWS Glue

  • AWS Glue Components
  • Create Crawler & Catalog Tables
  • Create & Run Glue Jobs
  • Glue Triggers & Workflows
  • Run & Validate Glue Workflow
  • Spark History Server Setup
  • Glue Spark UI Container Setup
  • IAM Policy Updates
  • Catalog Database Creation
  • Crawling Multiple Folders
  • Managing Glue Catalog

Module 9: Querying with AWS Athena

  • Amazon Athena Introduction
  • Glue Catalog Integration
  • Athena Query Editor
  • Create Databases & Tables
  • Data Insertion using Athena
  • CTAS (Create Table As Select)
  • Athena Architecture
  • Hive Integration
  • Partitioned Tables
  • Query Optimization
  • Insert & Validate Partition Data
  • Drop Tables & Data Cleanup

Module 10: Real-Time Data Processing with AWS Kinesis

  • Streaming Pipeline with Kinesis
  • Log Rotation
  • Kinesis Firehose Agent Setup
  • Create Delivery Streams
  • Pipeline Planning
  • IAM User & Permissions
  • Agent Configuration & Validation
  • Streaming Pipeline with PySpark

Module 11: Serverless Compute with AWS Lambda

  • AWS Lambda Introduction
  • Hello World Example
  • Local Project Setup
  • Deployment to AWS Lambda
  • File Download using Requests
  • Using External Libraries
  • S3 Access Validation
  • File Upload to S3
  • Execution & Testing
  • Incremental File Processing
  • Bookmark Handling in S3
  • Scheduling using EventBridge

Module 12: Data Warehousing with Amazon Redshift

  • Amazon Redshift Introduction
  • Cluster Creation (Free Trial)
  • Query Editor Usage
  • Table Creation & CRUD Operations
  • Insert, Update, Delete Data
  • Saved Queries
  • Data Loading from S3
  • Copy Command Implementation
  • JSON Data Loading
  • Redshift Architecture
  • Multi-node Cluster Setup
  • Database, Schema & User Creation
  • PySpark Integration with Redshift

Module 13: Data Visualization with Amazon QuickSight

  • Data Visualization Basics
  • Benefits of Visualization
  • Use Cases
  • QuickSight Core Concepts
  • Standard vs Enterprise Edition
  • SPICE Engine
  • Data Preparation
  • Dataset Handling
  • Dashboard Creation
  • Visual Types

Module 14: Workflow Orchestration with AWS Step Functions

  • Step Functions Introduction
  • Setup & Configuration
  • States, Tasks & State Machines
  • AWS Integration
  • Workflow Development & Deployment
  • Monitoring & Logging
  • Error Handling
  • Best Practices
  • End-to-End Serverless Pipeline
  • Scaling & Cost Optimization
  • Real-Time ETL Project

Module 15: Apache Airflow for AWS Data Engineering

  • Airflow Introduction
  • Workflow Scheduling
  • DAG Creation
  • Task Dependencies
  • Integration with AWS Services

Module 16: End-to-End Real-Time Project

  • Complete Data Engineering Project
  • Real-time Pipeline Implementation
  • Resume Building
  • Interview FAQs
  • Mock Interviews 

Who Should Join This Program?

👨‍🎓 Freshers & Graduates
 📊 Data Analysts
 💻 Developers
 🔄 ETL Engineers
 ☁️ Cloud Professionals

👉 Ideal for anyone looking to build a career in AWS Data Engineering

Certification & Career Support

🎓 AWS Certification Guidance
 📄 Resume Building
 🎯 Mock Interviews
 💼 Placement Assistance

👉 Become job-ready with real-time project experience

Frequently Asked Questions

Q1. What is AWS Data Engineering?

AWS Data Engineering involves building and managing data pipelines using AWS services like S3, Glue, Kinesis, and Redshift to process large-scale data efficiently.

Yes, this course starts from basics like SQL, Python, and Linux and gradually covers advanced AWS data engineering concepts.

This course covers AWS S3, Glue, Athena, Kinesis, Lambda, Redshift, CloudWatch, Step Functions, and Apache Airflow.

The course duration is 3 months, including practical sessions and real-time project implementation.

You can apply for roles like AWS Data Engineer, ETL Developer, Big Data Engineer, and Cloud Data Engineer.

Q2. What will I learn in this AWS Data Engineering course?

You will learn AWS services such as S3, Glue, Athena, Kinesis, Lambda, and Redshift along with SQL, Python, and PySpark to build real-time data pipelines.

Yes, you will work on an end-to-end real-time project where you build a complete data pipeline using AWS tools.

Basic knowledge of computers is enough. SQL, Python, and Linux basics will be taught as part of the course.

Yes, we provide guidance for AWS certification, including important topics and preparation strategy.

Yes, we provide resume building, mock interviews, and placement support to help you get job-ready.

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