How Can Data Engineers in 2025 Efficiently Build and Orchestrate Scalable ETL Pipelines Using AWS Services Like Glue, Lambda, and Step Functions?

 This question explores how modern data engineers can:

  • Leverage AWS Glue for serverless ETL processing with schema inference and job scheduling.

  • Use AWS Lambda for lightweight, event-driven transformations and triggers.

  • Orchestrate with AWS Step Functions to build resilient workflows with error handling, retries, and parallel executions.

  • Optimize costs and performance for large-scale data ingestion, transformation, and loading in near real-time or batch modes.

🔍 Tip for bloggers:
Include real-world architecture diagrams, use cases (e.g., IoT data processing or clickstream analytics), and hands-on tutorials with sample code to demonstrate orchestration across these services.

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How Can Data Engineers Leverage AWS Services Like Glue, Redshift, and EMR in 2025 to Build Scalable and Cost-Efficient Data Pipelines?

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