Situation: I've built data platforms across AWS, Azure, GCP. Task: Deliver scalable, cost-efficient data solutions. Action: AWS—S3, Glue, Athena, EMR, Redshift; Step Functions + Lambda. Azure—ADF, Synapse, Databricks, Event Hub. GCP—BigQuery, Dataflow, Composer. Common patterns: Lakehouse, medallion layers, IaC (Terraform), CI/CD. Result: Migrated 3 orgs to cloud; reduced infra cost 25% via right-sizing and lifecycle....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like JIO. The answer also includes follow-up discussion points that interviewers commonly explore.
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