EMR and Distributed Processing
Explore how Amazon EMR enables scalable, distributed data processing for machine learning workloads. Understand Spark's role in handling large datasets, and compare AWS Lambda and Spark Structured Streaming for real-time feature engineering. This lesson guides you to choose the right tool based on data size, velocity, and complexity, preparing you for efficient ML pipeline design on AWS.
As ML datasets scale from gigabytes to terabytes, single-node processing becomes a bottleneck in feature engineering and model training pipelines. A local Pandas job that handles 10 GB of CSV data in minutes will choke or fail entirely when faced with 500 GB of clickstream logs. This is the inflection point at which distributed processing becomes essential, and it is a recurring theme on the AWS Certified Machine Learning Engineer – Associate exam.
Amazon EMR is a managed cluster platform that runs Apache Spark, Hadoop, and other distributed frameworks on EC2 instances. It enables data ...