AWS DATA ENGINEERING

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The AWS Data Engineering course is designed for learners who want to build modern data pipelines and manage large-scale data on the AWS cloud platform. The training covers AWS cloud fundamentals, ETL development, big data processing, data lakes, data warehousing, streaming analytics, DevOps practices, and the basics of AI and machine learning integration. Through hands-on projects and real-world scenarios, students gain practical experience in building scalable and reliable data solutions. This course is ideal for data engineers, ETL developers, cloud engineers, BI professionals, database administrators, data analysts, and freshers looking to start a career in cloud data engineering.

Duration of Training : 40 Hours
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Detailed Syllabus • Hands-on Labs • Assignments • Support-Focused • Implementation

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Overview

The Syllabus

Curriculum Designed by Experts

Course Overview

This program is designed for:

• Data Engineers
• ETL Developers
• Cloud Engineers
• BI Professionals
• Database Administrators
• Data Analysts
• Freshers entering Data Engineering Domain

The course covers:
✅AWS Cloud Fundamentals
✅Data Engineering Pipelines
✅Big Data Processing
✅ETL Development
✅Data Lakes & Warehousing
✅Streaming & Real-Time Analytics
✅DevOps for Data Engineering
✅AI/ML Integration Basics
✅Real-Time Industry Projects

Module 1: Cloud & Data Engineering Fundamentals

Topics
• Introduction to Cloud Computing
• Introduction to Data Engineering
Subtopics
• Data Engineering Lifecycle
• ETL vs ELT
• Structured vs Unstructured Data
• Data Warehousing Concepts
• Cloud Data Platforms
• AWS Global Infrastructure
📘 Assignment
• Design a modern data engineering architecture
📘 Scenario
• Enterprise data modernization strategy

Module 2: Linux & Python Basics for Data Engineering

Topics
• Linux Administration Basics
• Python for Data Engineering
Subtopics
• Linux Commands
• File Handling
• Shell Scripting
• Python Basics
• Data Processing with Python
• JSON & CSV Handling
📘 Assignment
• Automate data file processing using Python
📘 Scenario
• Data ingestion automation

Module 3: AWS Fundamentals

Topics
• AWS Core Services
Subtopics
• IAM
• EC2
• S3
• VPC Basics
• CloudWatch
• AWS CLI
📘 Assignment
• Configure AWS cloud environment
📘 Scenario
• Enterprise cloud infrastructure setup

Module 4: Amazon S3 – Data Lake Foundation

Topics
• Data Lake Storage
Subtopics
• S3 Buckets
• Versioning
• Lifecycle Policies
• Storage Classes
• Encryption
• Data Partitioning
📘 Assignment
• Build enterprise data lake storage
📘 Scenario
• Centralized enterprise data repository

Module 5: Relational Databases on AWS

Topics
• Database Services
Subtopics
• Amazon RDS
• Aurora Basics
• PostgreSQL/MySQL
• Backup & Recovery
• Replication
• Database Security
📘 Assignment
• Deploy managed databases on AWS
📘 Scenario
• Migrating enterprise databases to AWS

Module 6: AWS Glue – ETL Development

Topics
• Serverless ETL
Subtopics
• Glue Crawlers
• Glue Catalog
• ETL Jobs
• Data Transformation
• Job Scheduling
• Glue Studio
📘 Project
• Build ETL pipelines using AWS Glue
📘 Scenario
• Enterprise data transformation workflows

Module 7: Data Warehousing with Amazon Redshift

Topics
• Cloud Data Warehousing
Subtopics
• Redshift Clusters
• Schema Design
• Data Loading
• Query Optimization
• Spectrum
•Data Sharing
📘 Assignment
• Build enterprise reporting warehouse
📘 Scenario
• Analytics & BI platform implementation

Module 8: Big Data Processing with EMR

Topics
• Hadoop & Spark on AWS
Subtopics
• Amazon EMR
• Hadoop Basics
• Spark Fundamentals
• PySpark
• Distributed Data Processing
• Batch Processing
📘 Project
•Process large-scale enterprise datasets
📘 Scenario
•Big data analytics implementation

Module 9: Real-Time Data Streaming

Topics
• Streaming Data Pipelines
Subtopics
• Amazon Kinesis
• Kafka Basics
• Streaming Architecture
• Real-Time Analytics
• Event Processing
📘 Assignment
• Build real-time streaming pipeline
📘 Scenario
• Fraud detection & live analytics

Module 10: AWS Lambda & Serverless Data Engineering

Topics
• Serverless Processing
Subtopics
• Lambda Functions
• Event-Driven Processing
• API Gateway
• Step Functions
• Automation Workflows
📘 Assignment
• Automate ETL workflows using Lambda
📘 Scenario
• Serverless enterprise automation

Module 11: Data Analytics & Visualization

Topics
• Analytics Services
Subtopics
• Amazon Athena
• QuickSight
• SQL Analytics
• Dashboard Creation
• Data Visualization
📘 Assignment
• Build business analytics dashboards
📘 Scenario
• Executive reporting & BI analytics

Module 12: DevOps for Data Engineering

Topics
• CI/CD & Automation
Subtopics
• Git & GitHub
• Jenkins Basics
• Docker Basics
• Terraform Basics
• CI/CD Pipelines
📘 Assignment
• Automate data engineering deployments
📘 Scenario
• Enterprise DevOps for data platforms

Module 13: Data Security & Governance

Topics
• Security & Compliance
Subtopics
• IAM Policies
• KMS Encryption
• Data Access Control
• Compliance Basics
• Audit Logging
• Governance Policies
📘 Assignment
• Implement secure data platform
📘 Scenario
• Enterprise data governance implementation

Module 14: Monitoring & Optimization

Topics
• Performance & Monitoring
Subtopics
• CloudWatch
• Logging
• Alerts & Notifications
• Query Optimization
• Cost Optimization
📘 Assignment
• Monitor and optimize ETL workloads
📘 Scenario
• Production support & optimization

Module 15: AI/ML Integration Basics

Topics
• AI for Data Engineering
Subtopics
• SageMaker Overview
• AI Data Pipelines
• Predictive Analytics Basics
• GenAI Overview
📘 Assignment
• Build AI-enabled analytics workflow
📘 Scenario
• AI-powered business insights

Module 16: Data Migration & Hybrid Architecture

Topics
• Enterprise Migration
Subtopics
• Database Migration Service (DMS)
• Hybrid Cloud Architecture
• Data Synchronization
• Migration Strategies
📘 Assignment
• Migrate on-premise data to AWS
📘 Scenario
• Enterprise cloud migration project

Module 17: Job-Oriented Data Engineering Scenarios

Topics
• Real-Time Enterprise Use Cases
Subtopics
• Banking Data Pipelines
• Healthcare Analytics
• Retail Data Processing
• Financial Reporting
• Customer Analytics
📘 Assignment
• Solve enterprise data engineering cases
📘 Scenario
• Production troubleshooting & optimization

📘 Capstone Projects

📘 Project 1: Enterprise Data Lake Architecture
• S3 Data Lake
• Glue ETL
• Redshift Warehouse

📘 Project 2: Real-Time Banking Analytics Platform
• Kinesis Streaming
• Spark Processing
• Real-Time Dashboard

📘 Project 3: E-Commerce Analytics Pipeline
• Customer Data Processing
• ETL Workflows
• Business Intelligence Reports

📘 Project 4: Healthcare Data Engineering Platform
• Secure Data Storage
• Data Governance
• Analytics Pipeline

📘 Project 5: AI-Powered Reporting System
• SageMaker Integration
• Predictive Analytics
• Dashboard Automation

📘Hands-on Assignments

• AWS Glue ETL Labs
• Redshift Query Optimization
• Kinesis Streaming Labs
• Lambda Automation
• S3 Data Lake Management
• CloudWatch Monitoring
• Terraform Automation

📘 Real-time Job Oriented Scenarios

• Enterprise ETL Development
• Data Lake Implementation
• Real-Time Streaming Analytics
• Big Data Processing
• Cloud Migration Projects
• Production Support Scenarios
• Data Security & Governance

📘 Skills You Will Gain

✅AWS Cloud Administration
✅ETL Development
✅Data Warehousing
✅Big Data Processing
✅Streaming Analytics
✅PySpark & Hadoop Basics
✅Data Lake Architecture
✅DevOps Automation
✅Cloud Security

📘 Career Opportunities

• AWS Data Engineer
• Cloud Data Engineer
• ETL Developer
• Big Data Engineer
• Data Platform Engineer
• Analytics Engineer
• Data Warehouse Engineer
• BI Engineer

📘 Recommended Certifications

• AWS Certified Data Engineer
• AWS Cloud Practitioner
• AWS Solutions Architect Associate
• Databricks Certifications
• Snowflake Certifications

📘 Program Highlights

✅Real-Time Industry Projects
✅Hands-On AWS Labs
✅ETL & Big Data Exposure
✅Real-Time Streaming Projects
✅DevOps & Automation
✅AI/ML Exposure
✅Resume & Interview Preparation
✅Placement Assistance Support

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AWS Data Engineering Course Certification With Training In Bangalore

Q: Module 1: Cloud & Data Engineering Fundamentals

Topics • Introduction to Cloud Computing • Introduction to Data Engineering Subtopics • Data Engineering Lifecycle • ETL vs ELT • Structured vs Unstructured Data • Data Warehousing Concepts • Cloud Data Platforms • AWS Global Infrastructure 📘 Assignment • Design a modern data engineering architecture 📘 Scenario • Enterprise data modernization strategy

Q: Module 2: Linux & Python Basics for Data Engineering

Topics • Linux Administration Basics • Python for Data Engineering Subtopics • Linux Commands • File Handling • Shell Scripting • Python Basics • Data Processing with Python • JSON & CSV Handling 📘 Assignment • Automate data file processing using Python 📘 Scenario • Data ingestion automation

Q: Module 3: AWS Fundamentals

Topics • AWS Core Services Subtopics • IAM • EC2 • S3 • VPC Basics • CloudWatch • AWS CLI 📘 Assignment • Configure AWS cloud environment 📘 Scenario • Enterprise cloud infrastructure setup

Q: Module 4: Amazon S3 – Data Lake Foundation

Topics • Data Lake Storage Subtopics • S3 Buckets • Versioning • Lifecycle Policies • Storage Classes • Encryption • Data Partitioning 📘 Assignment • Build enterprise data lake storage 📘 Scenario • Centralized enterprise data repository

Q: Module 5: Relational Databases on AWS

Topics • Database Services Subtopics • Amazon RDS • Aurora Basics • PostgreSQL/MySQL • Backup & Recovery • Replication • Database Security 📘 Assignment • Deploy managed databases on AWS 📘 Scenario • Migrating enterprise databases to AWS

Q: Module 6: AWS Glue – ETL Development

Topics • Serverless ETL Subtopics • Glue Crawlers • Glue Catalog • ETL Jobs • Data Transformation • Job Scheduling • Glue Studio 📘 Project • Build ETL pipelines using AWS Glue 📘 Scenario • Enterprise data transformation workflows

Q: Module 7: Data Warehousing with Amazon Redshift

Topics • Cloud Data Warehousing Subtopics • Redshift Clusters • Schema Design • Data Loading • Query Optimization • Spectrum •Data Sharing 📘 Assignment • Build enterprise reporting warehouse 📘 Scenario • Analytics & BI platform implementation

Q: Module 8: Big Data Processing with EMR

Topics • Hadoop & Spark on AWS Subtopics • Amazon EMR • Hadoop Basics • Spark Fundamentals • PySpark • Distributed Data Processing • Batch Processing 📘 Project •Process large-scale enterprise datasets 📘 Scenario •Big data analytics implementation

Q: Module 9: Real-Time Data Streaming

Topics • Streaming Data Pipelines Subtopics • Amazon Kinesis • Kafka Basics • Streaming Architecture • Real-Time Analytics • Event Processing 📘 Assignment • Build real-time streaming pipeline 📘 Scenario • Fraud detection & live analytics

At Radical Technologies, we are committed to providing world-class Azure Data Engineer Training in Bangalore, helping aspiring data professionals master the skills needed to excel in the rapidly growing field of cloud data engineering. As the leading institute for Azure Data Engineer Course In Bangalore, we offer comprehensive, hands-on training designed to meet the demands of today’s data-driven organizations.

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