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#AWS Data Engineer

AWS Data Engineering involves designing and building scalable data pipelines using Amazon Web Services. Engineers use tools like AWS Glue, Redshift, S3, and Lambda to process and transform large volumes of data. This role is in high demand and leads to careers in cloud data engineering, analytics, and big data architecture.

✅ AWS S3, Data Lake Architecture
✅ AWS Glue for ETL/ELT Pipelines
✅ Redshift: Data Warehousing, Analytics
✅ Kinesis for Real-Time Data Streaming
✅ EMR, PySpark for Big Data Process
✅ Lambda, AWS ETL, Automations
✅ IAM, KMS & Security Best Practices
✅ CI/CD Pipelines with AWS ETL
✅ End-to-End Real-Time Project
✅ 1:1 Mentorship, Interview Guidance

Master AWS Data Engineering Today – Learn S3, Redshift, Athena, ETL, and Real-Time Cloud Data Pipeline

AWS Data Engineering
Course Contents:

Module 1 : Linux Concepts

Ch 1: Linux Introduction

  • Client-Server Architecture
  • GUI vs CLI
  • Navigating through CLI
  • Basic commands
  • File System Hierarchy
  • Help commands

Ch 2: File Hierarchy System

  • Relative Path Concepts
  • Absolute Path Concepts
  • Common File Types
  • Regular files
  • Directories, Links
  • Realtime Usage

Ch 3: File Management

  • Create Files, Directories
  • touch and mkdir
  • Directory Operations
  • Commands & Usage
  • File Editing Options
  • Text Editors (vim)

Ch 4: Basic User Management

  • User Login Activity
  • Viewing login records
  • Local User Authentication
  • /etc/passwd, /etc/shadow
  • useradd, usermod, userdel
  • Custom config & Profiles

Ch 5: Adv. File Management

  • File and Directory Access
  • Permissions Management
  • chmod Realtime Usage
  • Symbolic Mode
  • Numeric Mode
  • Configuring and using sudo

Ch 6: Variables

  • Environment variables
  • Shell variables
  • Variable Substitution
  • Command Substitution
  • Using backticks & $
  • Using LINUX in AWS

Module 2 : AWS Concepts

Ch 7: Cloud Computing

  • Cloud Architecture & Use
  • Cloud Computing Concepts
  • Cloud Impl. Models
  • Public, Private, and Hybrid
  • AWS Cloud : Properties
  • AWS Cloud : Advantages
  • AWS Cloud : Usage Scope

Ch 8: AWS Concepts

  • AWS Free Tier Account
  • Account setup
  • AWS Initial Configuration
  • AWS Global Infrastructure
  • Overview of Region
  • Availability Zones, Edges
  • AWS Console Options

Ch 9: Elastic Compute (EC2)

  • Creating EC2 Instances
  • Instance types, AMIs
  • Instance Launch Options
  • Security Groups, Ports
  • SSH Overview, Key Pairs
  • Key pair creation and SSH
  • Private vs Public vs Elastic IP

Ch 10: Security & IAM

  • IAM Introduction
  • Core IAM Architecture
  • Managing Users & Groups
  • Creating and managing IAM
  • Group Policies, Inline Policies
  • Difference and use cases
  • AWS Cloud Shell, IAM

Ch 11: EC2 Instance Storage

  • EBS : Elastic Block Store
  • Managing EBS Volumes
  • Volume Usage Options
  • EBS Snapshots & Usage
  • Cross-AZ, Replication
  • EBS Encryption
  • Amazon Machine Images

Ch 12: S3 Storage Service

  • S3 Buckets and Objects
  • S3 Usage Management
  • S3 Versioning, Policies
  • Access Control
  • Static Website Hosting
  • S3 Storage Classes
  • Automation,EFS Concepts

Ch 13: Cloud Network & VPC

  • Introduction to Networking
  • CIDR : Notation, Usage
  • Public, Private Subnets
  • Subnet Creation Options
  • Public and Private VPCs
  • VPC setup & Configuration

Ch 14: Cost Management

  • AWS Budgets Overview
  • Budget Management
  • Cost Management Tools
  • AWS Cost Explorer
  • Cost / Pricing Reports
  • Price Optimization Strategies

Ch 15: CloudWatch

  • Metrics
  • Dashboards
  • Alarms
  • Logs
  • Events (basics)

Module 3 : Data Streaming, Database and Redshift

Ch 16: AWS Kinesis – 1

  • Amazon Kinesis
  • Realtime Data Streaming
  • Amazon Kinesis Data streams
  • Creating Data Stream
  • Enhanced Fan-Out
  • Lambda function & Kinesis

Ch 17: AWS Kinesis – 2

  • Kinesis Firehose
  • Data Firehose Stream
  • Firehose – Transformations
  • Firehose with Lambda
  • ETL Implementations
  • Data Streaming

Ch 18: RDS DB Database – 1

  • Database on EC2 instance
  • Introduction to RDS
  • RDS Networking and Subnet
  • Create a VPC for RDS
  • RDS Subnet Group
  • Create an RDS Instance
  • View an RDS Instance

Ch 19: RDS DB Database – 2

  • RDS Usage in OLTP
  • RDS Backups and Snapshots
  • Restore RDS from Backup
  • Share RDS Snapshots
  • RDS Encryption in Transit
  • Delete an RDS Instance

Ch 20: RDS DB Database – 3

  • Authenticating to RDS
  • Credentials, IAM
  • Secrets Manager
  • RDS Parameter Groups
  • RDS Proxy, Multi-AZ RDS
  • RDS Read Replicas

Ch 21: Amazon Redshift – 1

  • Redshift overview
  • Redshift Serverless
  • Provisioned Cluster
  • Architecture Overview
  • Clusters & Nodes
  • Create Redshift Cluster
  • Access Redshift Cluster
  • Query Editor, Node Types

Ch 22: Amazon Redshift – 2

  • Storage, Resizing Methods
  • Snapshots & Sharing
  • Resizing Snapshots
  • Redshift – VACCUM
  • Load Data From S3
  • Unload Data
  • Federated Queries
  • Redshift Spectrum

Ch 23: Amazon Redshift – 3

  • AWS RedShift Security
  • AWS RedShift Connections
  • Authentication Types
  • Optimization Options
  • Data Load Operations
  • Data Load Requirements
  • Transformations with ELT

Ch 24: Amazon Redshift – 4

  • Using ETL Tools
  • Need for AWS Lamda
  • Need for AWS Glue
  • Need for AWS Athena
  • AWS Redshift Tuning
  • AWS RedShift Connections

Module 4 : Lambda, Glue and Athena

Ch 25: Lambda Introduction

  • What is serverless
  • AWS Lambda Introduction
  • AWS Lambda for Python
  • AWS Lambda Python code
  • Packages and Deployments
  • AWS Lambda configuration
  • AWS Lamda Settings

Ch 26: Lambda Implementation

  • AWS Lambda Layers
  • Python with Lamda
  • AWS Lambda – S3
  • Event Notifications in AWS
  • API Gateway Integration
  • Alias and Versions
  • AWS Lambda – Snapstart

Ch 27: AWS Athena

  • Athena overview
  • Query data using Athena
  • Federated Queries
  • Performance and cost
  • Workgroups
  • Workgroups (Hands-on)
  • Querying with Athena

Ch 28: AWS Glue – 1

  • AWS Glue overview
  • Need for AWS Glue
  • AWS Glue Usage Scope
  • Setting up Crawler
  • AWS Glue Costs
  • AWS Budgets

Ch 29: AWS Glue – 2

  • Stateful vs Stateless
  • Stateless Data Ingesting
  • Glue Transformations (ETL)
  • Glue Data Quality
  • Glue workflow
  •  Scheduling Crawlers & ETL

Ch 30: AWS Glue – 3

  • Default Classifiers
  • Custom Classifiers
  • Glue Triggers
  • Run the pipelines using CloudFormation

SQL SCHOOL

24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.

AWS Data Engineering Training FAQ's

What is AWS Data Engineering Job Role?

An AWS Data Engineer is responsible for designing, developing, and managing cloud-based data pipelines, storage systems, and processing solutions using AWS services. The role includes handling data ingestion, transformation, storage, governance, and security at scale. AWS Data Engineers work with technologies like S3, Glue, Redshift, Lambda, Kinesis, EMR, and Athena to build reliable, optimized, and cost-efficient data architectures that support analytics and machine learning workloads.

What are the Job Roles of an AWS Data Engineer?

💼 Top Job Roles:

 

1️⃣ Design and implement scalable AWS data pipelines using Glue, Lambda, and Kinesis
2️⃣ Manage data storage solutions with S3, Redshift, and Lake Formation
3️⃣ Develop ETL/ELT workflows for structured and unstructured data
4️⃣ Optimize data architectures for cost, performance, and reliability
5️⃣ Ensure data security, compliance, and governance in AWS
6️⃣ Integrate data from diverse sources (files, databases, APIs, IoT, etc.) and more..!

What does our AWS Data Engineer Training course contain?

The course is carefully curated with below module:
👉🏻Module 1: Module 1 : Linux Concepts
👉🏻Module 2: AWS Concepts
👉🏻Module 3: Data Streaming, Database and Redshift
👉🏻Module 4:Lambda, Glue and Athena 

Who can join this course?

  • Freshers aiming for cloud data engineering jobs

  • ETL developers moving to AWS platform

  • DBAs and BI developers transitioning to cloud data solutions

  • IT professionals upgrading to AWS data services

  • Anyone interested in big data and cloud data pipelines

No prior coding experience is required. All concepts are taught from scratch

What training modes are available?

Option 1:        LIVE Online Training  (100% Interactive, step by step, assignments)

Option 2:        Self Paced Videos (100% practical, step by step with concept wise assignments)

You may choose any one of these options, same curriculum!

I (Trainer) shall be available for doubts and clarifications, assignment check and review.

Why should I choose SQL School for AWS Data Engineer training?

👉🏻 Every session is Practical, Step by Step with Concept wise FAQs !!

👉🏻 100% results with on-time practice.  Daily Tasks for every session.

👉🏻 Concept wise tasks be submitted before next class for Job Waiters / Starters.

👉🏻 Concept wise tasks due for submission by Weekends for Working Professionals.

Why Choose SQL School

  • 100% Real-Time and Practical
  • ISO 9001:2008 Certified
  • Concept wise FAQs
  • TWO Real-time Case Studies, One Project
  • Weekly Mock Interviews
  • 24/7 LIVE Server Access
  • Realtime Project FAQs
  • Course Completion Certificate
  • Placement Assistance
  • Job Support
  • Realtime Project Solution
  • MS Certification Guidance