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

• Day 1 – AWS Cloud Fundamentals

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

• Day 2 – Data Engineering Overview

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

• Day 3 – Setting Up AWS Environment

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

• Day 4 – AWS CLI & SDKs

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

• Day 5 – AWS Storage Fundamentals

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  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

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

• Day 7 – Introduction to AWS Kinesis

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

• Day 8 – Working with Kinesis Data Streams

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

• Day 9 – AWS Firehose for Streaming Data

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

• Day 10 – AWS Glue Data Catalog

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  Cataloging data sources Lab: Create Glue Data Catalog tables

• Day 11 – Batch Data Ingestion with AWS Glue

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

• Day 12 – Ingestion Mini Project

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  Combine Kinesis and Glue for hybrid ingestion

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

• Day 13 – AWS Data Lake Overview

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

• Day 14 – AWS Lake Formation

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

• Day 15 – AWS DynamoDB Basics

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  NoSQL database concepts and use cases Lab: Create DynamoDB tables

• Day 16 – AWS RDS for Relational Data

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

• Day 17 – AWS Redshift Basics

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

• Day 18 – Data Storage Mini Project

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  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

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

• Day 20 – AWS Lambda for Data Processing

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

• Day 21 – AWS EMR Introduction

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

• Day 22 – Data Transformation with PySpark

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

• Day 23 – Workflow Orchestration with Step Functions

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  Automating pipelines using Step Functions Lab: Build an ETL workflow

• Day 24 – Automating ETL with Glue Workflows

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

• Day 25 – Processing Mini Project

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  Build a serverless ETL pipeline with Glue + Lambda

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

• Day 26 – Redshift Data Warehousing

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

• Day 27 – Querying with Amazon Athena

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  Serverless querying with SQL Lab: Analyze S3 data using Athena

• Day 28 – Redshift Spectrum

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

• Day 29 – Building Analytics Dashboards

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  Introduction to AWS QuickSight Lab: Create an interactive dashboard

• Day 30 – Performance Optimization in Redshift

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  Distribution styles and sort keys Lab: Optimize queries in Redshift

• Day 31 – Analytics Mini Project

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  Build an end-to-end analytics pipeline

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

• Day 32 – Event-Driven Architectures

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

• Day 33 – Real-Time Analytics with Kinesis Analytics

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

• Day 34 – Data Governance with AWS Lake Formation

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  Fine-grained access control Lab: Implement security policies

• Day 35 – Cost Optimization for Data Pipelines

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

• Day 36 – Handling Large Scale Data

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  Partitioning and compression techniques Lab: Optimize S3 and Redshift storage

• Day 37 – Machine Learning Integration

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

• Day 38 – Advanced Mini Project

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  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

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  Define business use case and architecture

• Day 40 – Ingestion Layer Development

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  Configure Kinesis + Glue pipelines

• Day 41 – Storage & Processing Implementation

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  Set up S3, Redshift, and EMR

• Day 42 – Data Transformation & Orchestration

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  Build end-to-end ETL workflow

• Day 43 – Analytics & Visualization

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  Create QuickSight dashboards for reporting

• Day 44 – Deployment & Optimization

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  Deploy pipeline with security and cost control

• Day 45 – Project Presentation & Career Guidance

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  Showcase project Resume building and interview prep