For predictive analytics, which AWS service is primarily used?

Amazon SageMaker is primarily used for predictive analytics as it is a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale. With SageMaker, users can take advantage of built-in algorithms and frameworks to perform advanced analytics and predictions on their data. The service supports a range of machine learning tasks, including supervised and unsupervised learning, making it well-suited for predictive analytics.

Furthermore, SageMaker encompasses various functionalities, such as data labeling, model training, tuning, and deployment, providing an end-to-end workflow. This streamlining allows users to focus on developing their models without needing to manage the underlying infrastructure, thus enhancing productivity in predictive analytics projects.

In contrast, while Amazon RDS is a relational database service that is ideal for transactional databases and AWS Glue is primarily used for data preparation and ETL (Extract, Transform, Load) processes, they do not offer the machine learning capabilities necessary for predictive analytics. Amazon Kinesis, focused on real-time data streaming and analytics, excels in processing and analyzing streaming data but does not cater specifically to the building and deployment of predictive models like SageMaker does.