Spark is well-suited for interactive data analysis and machine learning workloads. It can be developed locally in an IDE or the Spark shell before deploying to EMR. When deploying, be sure to install Ganglia for cluster monitoring and debug Spark jobs by running parts of the code in the local Spark shell. Packaging code into a fat JAR, configuring properties like parallelism, and submitting jobs to YARN are required to run Spark programs on EMR at scale.