Learn AWS cloud basics, data engineering concepts, and set up your AWS environment for hands-on labs.

• Day 1 – AWS Cloud Fundamentals

  read

  • AWS global infrastructure and core services
  • Regions, Availability Zones, and Edge Locations
  • Overview of AWS Management Console

• Day 2 – Data Engineering Overview

  read

  • What is data engineering?
  • ETL vs ELT processes
  • Batch vs real-time data pipelines

• Day 3 – Setting Up AWS Environment

  read

  • Creating an AWS account
  • IAM users, roles, and permissions
  • Lab: Configure IAM and billing alerts

• Day 4 – AWS CLI & SDKs

  read

  • Install and configure AWS CLI
  • Using SDKs for automation
  • Lab: Manage resources via CLI

• Day 5 – AWS Storage Fundamentals

  read

  • S3 basics, buckets, and object storage
  • S3 security and lifecycle policies
  • Lab: Create and manage S3 buckets

Learn to ingest and stream data using AWS services for batch and real-time processing.

• Day 6 – AWS Data Migration Tools

  read

  • AWS Data Migration Service overview
  • Lab: Import sample dataset into AWS

• Day 7 – Introduction to AWS Kinesis

  read

  • Kinesis Data Streams and Firehose basics
  • Use cases for real-time ingestion

• Day 8 – Working with Kinesis Data Streams

  read

  • Producers, consumers, and shards
  • Lab: Create a Kinesis Data Stream

• Day 9 – AWS Firehose for Streaming Data

  read

  • Deliver data to S3, Redshift, and Elasticsearch
  • Lab: Configure Firehose delivery stream

• Day 10 – AWS Glue Data Catalog

  read

  • Cataloging data sources
  • Lab: Create Glue Data Catalog tables

• Day 11 – Batch Data Ingestion with AWS Glue

  read

  • Building ETL jobs with Glue
  • Lab: Load batch data into S3

• Day 12 – Ingestion Mini Project

  read

  • Combine Kinesis and Glue for hybrid ingestion

Store and manage structured and unstructured data using AWS Lakehouse architecture.

• Day 13 – AWS Data Lake Overview

  read

  • Data lake architecture and benefits
  • Lab: Set up a basic data lake on S3

• Day 14 – AWS Lake Formation

  read

  • Managing access control for data lakes
  • Lab: Create and secure a data lake

• Day 15 – AWS DynamoDB Basics

  read

  • NoSQL database concepts and use cases
  • Lab: Create DynamoDB tables

• Day 16 – AWS RDS for Relational Data

  read

  • Setting up RDS instances
  • Lab: Store and query data in RDS

• Day 17 – AWS Redshift Basics

  read

  • Introduction to data warehousing on AWS
  • Lab: Create a Redshift cluster

• Day 18 – Data Storage Mini Project

  read

  • Build a data lake + Redshift integration

Transform and process raw data into analytics-ready formats using AWS Glue, EMR, and Lambda.

• Day 19 – AWS Glue ETL Jobs

  read

  • Building and scheduling ETL pipelines
  • Lab: Create Glue jobs to transform data

• Day 20 – AWS Lambda for Data Processing

  read

  • Serverless processing concepts
  • Lab: Build a Lambda function for data cleaning

• Day 21 – AWS EMR Introduction

  read

  • Hadoop/Spark on AWS EMR
  • Lab: Launch and configure an EMR cluster

• Day 22 – Data Transformation with PySpark

  read

  • Using PySpark for data engineering
  • Lab: Write PySpark scripts on EMR

• Day 23 – Workflow Orchestration with Step Functions

  read

  • Automating pipelines using Step Functions
  • Lab: Build an ETL workflow

• Day 24 – Automating ETL with Glue Workflows

  read

  • Creating end-to-end Glue workflows
  • Lab: Orchestrate multiple Glue jobs

• Day 25 – Processing Mini Project

  read

  • Build a serverless ETL pipeline with Glue + Lambda

Load, query, and analyze data using AWS Redshift, Athena, and QuickSight.

• Day 26 – Redshift Data Warehousing

  read

  • Columnar storage and MPP concepts
  • Lab: Load data into Redshift tables

• Day 27 – Querying with Amazon Athena

  read

  • Serverless querying with SQL
  • Lab: Analyze S3 data using Athena

• Day 28 – Redshift Spectrum

  read

  • Querying S3 data directly from Redshift
  • Lab: Integrate Redshift with S3 data lake

• Day 29 – Building Analytics Dashboards

  read

  • Introduction to AWS QuickSight
  • Lab: Create an interactive dashboard

• Day 30 – Performance Optimization in Redshift

  read

  • Distribution styles and sort keys
  • Lab: Optimize queries in Redshift

• Day 31 – Analytics Mini Project

  read

  • Build an end-to-end analytics pipeline

Learn advanced concepts like event-driven pipelines, data governance, and cost optimization.

• Day 32 – Event-Driven Architectures

  read

  • Using EventBridge and Lambda
  • Lab: Build an event-driven pipeline

• Day 33 – Real-Time Analytics with Kinesis Analytics

  read

  • Running SQL queries on streaming data
  • Lab: Build a real-time dashboard

• Day 34 – Data Governance with AWS Lake Formation

  read

  • Fine-grained access control
  • Lab: Implement security policies

• Day 35 – Cost Optimization for Data Pipelines

  read

  • Monitoring costs using AWS Cost Explorer
  • Best practices for cost-efficient pipelines

• Day 36 – Handling Large Scale Data

  read

  • Partitioning and compression techniques
  • Lab: Optimize S3 and Redshift storage

• Day 37 – Machine Learning Integration

  read

  • Using AWS SageMaker for data engineering
  • Lab: Prepare data for ML pipelines

• Day 38 – Advanced Mini Project

  read

  • Build a scalable, event-driven data platform

Apply all skills to build a real-world AWS data engineering project and prepare for job roles.

• Day 39 – Capstone Project Planning

  read

  • Define business use case and architecture

• Day 40 – Ingestion Layer Development

  read

  • Configure Kinesis + Glue pipelines

• Day 41 – Storage & Processing Implementation

  read

  • Set up S3, Redshift, and EMR

• Day 42 – Data Transformation & Orchestration

  read

  • Build end-to-end ETL workflow

• Day 43 – Analytics & Visualization

  read

  • Create QuickSight dashboards for reporting

• Day 44 – Deployment & Optimization

  read

  • Deploy pipeline with security and cost control

• Day 45 – Project Presentation & Career Guidance

  read

  • Showcase project
  • Resume building and interview prep