Aws Data Engineer Course | Aws Data Engineer Training
- 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!