Amazon EMR offers a serverless deployment option called Amazon EMR Serverless, eliminating the need for cluster management. Traditional EMR requires configuring and maintaining clusters, but EMR Serverless automatically provisions resources and scales as needed.
Key Differences: EMR vs. EMR Serverless
Cluster Management – Traditional EMR requires manual cluster setup; EMR Serverless handles it automatically.
Scalability – EMR Serverless scales dynamically, while traditional EMR requires manual scaling.
Cost Optimization – EMR Serverless only charges for compute resources used, reducing idle costs.
Use Cases
Traditional EMR: Ideal for persistent workloads requiring cluster control.
EMR Serverless: Best for on-demand, event-driven workloads.
When to Use EMR Serverless
For ad-hoc data processing without managing clusters.
When running Spark or Hive workloads with unpredictable scaling needs.
If you want cost-efficient, automatic scaling for big data applications.
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