The Best AWS Data Engineering Course - AWS Data Engineering training
                     ETL Workflows in AWS: 
A Practical Guide
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Introduction to ETL on AWS
• Beyond Traditional ETL: Shift from monolithic tools to 
cloud-native workflows
• Data as a Product: Treat data pipelines as scalable, reusable 
assets
• AWS Advantage: Elastic infrastructure and automation for 
every ETL stage
• Focus of This Guide: Hands-on strategies to build robust ETL 
in AWS
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Key AWS Services for ETL
•AWS Glue: Serverless ETL orchestration & 
transformation
•Amazon S3: Data lake storage and staging layer
•Amazon Redshift: High-performance data 
warehousing
•AWS Lambda: Event-driven processing and triggers
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Designing ETL Architecture on  AWS
•Define data sources and destinations
•Plan transformations: filtering, joining, cleansing
•Leverage Glue crawlers to catalog source data
•Choose batch vs. streaming based on use case
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Building ETL Jobs with AWS Glue
•Create Glue Jobs using PySpark or 
visual ETL editor
•Use Glue Studio for low-code job authoring
•Schedule jobs using triggers and workflows
•Debug and test jobs with built-in monitoring
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Data Transformation Best Practices
•Use Glue DynamicFrames for schema flexibility
•Optimize transformations for performance 
(e.g., partitioning)
•Incorporate business logic in the transformation 
layer
•Cleanse data before loading to downstream 
systems
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Orchestration & Workflow 
Automation
• Use AWS Glue Workflows for job sequencing
• Integrate Step Functions for complex 
orchestration
• Monitor workflows using CloudWatch & Glue 
logs
• Enable notifications via SNS for job status 
updates
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Security & Cost Optimization
•Use IAM roles to control access to ETL components
•Encrypt data at rest (S3, Redshift) and in transit
•Optimize Glue job costs by choosing right worker type
•Clean up unused resources to avoid unexpected 
charges
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Real-World Use Case + Wrap-Up
Example: Retail company ETL pipeline for customer 
insights
Flow: Ingest → Transform (Glue) → Load (Redshift)
Key benefits realized: speed, scalability, cost savings
Recap & key takeaways for designing ETL in AWS
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