Day 1: Foundations of Big Data and AWS Services

Module 1: Introduction to Big Data

• What is Big Data? Characteristics and Challenges
• The Role of Cloud in Big Data

Module 2: AWS Big Data Ecosystem

• Overview of Key Services: S3, EMR, Redshift, Kinesis, and Athena

• Big Data Architecture on AWS

Module 3: Storage and Data Ingestion

• Data Storage: Deep Dive into Amazon S3
• Ingestion Services: AWS Glue, Kinesis, and Data Pipelines
• Hands-On: Setting Up Data Ingestion

Day 2: Data Processing and Analysis

Module 4: Processing Big Data on AWS

• Amazon EMR for Data Processing
• Introduction to Apache Spark and Hadoop on AWS
• Hands-On: Running a Data Processing Job on EMR

Module 5: Data Warehousing with Redshift

• Setting Up and Managing Redshift Clusters

• Optimizing Redshift for Query Performance
• Hands-On: Querying Data with Redshift

Module 6: Data Visualization and Analysis

• Using Amazon QuickSight for Business Intelligence
• Hands-On: Creating Dashboards

Day 3: Advanced Topics and Best Practices

Module 7: Streaming Data Processing

• Real-Time Analytics with Amazon Kinesis
• Hands-On: Building a Real-Time Data Pipeline

Module 8: Security and Governance

• Best Practices for Data Security on AWS
• Compliance and Data Governance

Module 9: Cost Management and Optimization

• Cost-Effective Strategies for Big Data Workloads
• Using AWS Cost Management Tools

Module 10: Capstone Project

End-to-End Workflow: Ingest, Process, Store, and Analyze Data

• Presenting Findings and Solutions

Module 11: Review and Wrap-Up

• Key Takeaways and Resources for Further Learning
• Q&A Session

Training material provided: Yes (Digital format)