Advantages of utilising Amazon EMR

Amazon EMR offers cost-effective options like Spot Instances, which can be up to 90% cheaper than On-Demand Instances, and Reserved Instances for further savings

Improper instance selection or under-provisioned clusters can lead to performance bottlenecks or slow task execution

Setting up and optimizing EMR clusters requires familiarity with underlying frameworks and AWS infrastructure

EMR supports frameworks like Hadoop, Spark, Hive, and Pig, enabling diverse data processing workloads

Users can manage EMR clusters via the AWS Management Console, CLI, SDKs, or Web Service API, offering flexibility for different skill levels

EMR integrates with CloudWatch for performance monitoring and allows log file archiving on Amazon S3 for debugging

EMR uses IAM for permissions, security groups for traffic control, and encryption (server-side and client-side) for data protection

EMR monitors cluster nodes, automatically replacing failed instances and offering termination protection to prevent data loss

EMR supports HDFS for intermediate data and EMRFS for decoupling computation and storage using Amazon S3

Clusters can be scaled up or down based on workload demands, with support for multiple instance groups using Spot and On-Demand Instances