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1. Overview of AWS Data Services
Understanding S3, Redshift, Glue & More

2. Agenda
Introduction to AWS Data Services
Amazon S3
Amazon Redshift
AWS Glue
Other AWS Data Services
Use Cases & Architectures
Q&A

3. What Are AWS Data Services?
Suite of cloud-based tools for storing, processing, analyzing, and moving
data
Fully managed, scalable, pay-as-you-go
Commonly used in:
-Data lakes
-Analytics pipelines
-ETL workflows
-Real-time processing

4. Amazon S3 (Simple Storage Service)
Object storage service for virtually unlimited data
Durable (99.999999999%) and available
Use cases:
Backup and restore
Data lake storage
Static website hosting
Supports: versioning, lifecycle policies, encryption
Integrates with: Athena, Redshift Spectrum, Glue, etc.

5. Amazon Redshift
Fully managed data warehouse
Columnar storage, optimized for analytics
Supports SQL, connects with BI tools (Tableau, Power BI)
Features:
Redshift Spectrum: query data in S3
Concurrency Scaling
Materialized Views
Use Cases: BI, analytics dashboards, reporting

6. AWS Glue
Serverless data integration & ETL service
Automates discovery, cataloging, and transformation
Components:
Glue Data Catalog
Glue Crawlers
Glue Jobs (Python or Spark)
Use cases:
Data preparation for analytics
Building data pipelines
Schema inference & metadata management

7. AWS Athena
Interactive query service for S3 data
SQL-based, serverless
Pay-per-query model
Works well with S3, Glue Catalog
Use cases: Ad hoc analysis, logs analysis, quick reports

8. AWS Lake Formation
Simplifies setting up secure data lakes on S3
Manages:
Data ingestion
Access control
Schema definitions
Centralized governance of data lake

9. AWS Kinesis
Real-time data streaming service
Kinesis Data Streams, Kinesis Firehose, Kinesis Analytics
Use cases:
Real-time analytics
Log & clickstream processing
IoT telemetry data

10. Sample Architecture: Modern Data Lake
Scalable, flexible, and cost-efficient architecture for analytics and ML

11. When to Use What?
Service Primary Use Case
S3 Storage for raw/processed data
Redshift Complex analytics on structured data
Glue ETL workflows, data discovery
Athena Ad-hoc SQL on S3
Kinesis Real-time data processing
Lake Formation Data lake setup & security

12. Summary
AWS provides end-to-end data tools: storage, transformation, analytics
Choose services based on use case: real-time, batch, ad-hoc
Integration between services is seamless
Great for building scalable and secure data architectures
Questions & Discussion
Let’s dive deeper into anything you’re curious about!