A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practice

Using HDFS as a persistent data store for frequently accessed data within a long-running EMR cluster improves data locality and read/write performance for Spark jobs, potentially reducing overall job execution time and thus compute costs while maintaining performance.

While Amazon S3 is excellent for durable, cost-effective external data storage, using HDFS for hot data within a long-running EMR cluster can offer better performance due to data locality and lower latency, which can lead to faster job completion and cost savings in compute hours.

x86-based instances are generally less cost-effective than Graviton instances for many EMR workloads when considering price-performance.

AWS Graviton instances offer a superior price-performance ratio compared to x86-based instances for many workloads, including Apache Spark on EMR, making them a cost-effective choice for core and task nodes while maintaining or improving performance.

Using Spot Instances for primary (master) nodes is not recommended for high reliability as interruptions can lead to cluster instability, data loss, and operational overhead, failing the "high reliability" requirement.