EMR Serverless runs big-data frameworks like Spark and Hive without you provisioning or managing clusters. Capacity scales automatically per job, so you pay only for the resources each job actually uses.
Serverless vs cluster EMR
Regular EMR means sizing and running a cluster; EMR Serverless removes that — submit a Spark/Hive job and AWS provisions and scales capacity for it, then tears it down. Best for variable or intermittent big-data jobs where a persistent cluster would sit idle. Steady 24/7 processing may still favor a provisioned cluster.
Test yourself
A team runs occasional large Spark jobs and doesn’t want to manage or pay for an always-running EMR cluster. Which option fits best?
- A large persistent EMR cluster
- EMR Serverless
- AWS Glue crawlers
- A single EC2 instance
👉 Click to reveal the answer & explanation
Correct answer: B. EMR Serverless provisions and scales capacity per job with no cluster to manage — ideal for occasional large Spark jobs. A persistent cluster (A) wastes money when idle; crawlers (C) only catalog schema; one EC2 instance (D) can’t handle large distributed Spark efficiently.
Related topics
Amazon EMR · AWS Glue · Glue vs EMR
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