In this Quest, you will delve deeper into the uses and capabilities of Amazon Redshift. You will use a remote SQL client to create and configure tables, and gain practice loading large data sets into Redshift. You will explore the effects of schema variations and compression. You will explore visualization of Redshift data, and connect Redshift with Amazon Machine Learning to create a predictive data model.
Applied Machine Learning: Building Models for an Amazon Use Case (Sandbox)
Applied Machine Learning: Building Models for an Amazon Use Case
In this lab you will enable client-side at-rest encryption using AWS KMS-managed key for data stored in Amazon S3 with the EMR File System (EMRFS). Within Amazon EMR you will create security configuration to encrypt the object written to S3 with client-side encryption using the AWS KMS-managed key specified by you, and decrypt objects with the same key that was used to encrypt them. This will allow you to more easily leverage frameworks like Apache Spark, Apache Tez, and Apache Hadoop MapReduce on Amazon EMR to run big data analytics, stream processing, machine learning, and ETL workloads on confidential data.