A manufacturing company stores last year’s production volume data in a PostgreSQL database. Business analysts (who do not write code) must be able to prepare the data and build a model to forecast future production volume. Which end-to-end solution requires the least effort and no coding?

This solution is optimal because it leverages fully managed, low-code/no-code services. AWS Glue DataBrew allows business analysts to visually prepare data from PostgreSQL without writing code, directly addressing the "no coding" requirement. Amazon SageMaker Canvas is a no-code machine learning service designed for business analysts to build, train, and deploy models, including forecasting, with a visual interface. This combination requires the least effort and no coding. The other options are less suitable: Using Amazon EMR requires coding skills (e.g., Spark, Hive) and significant effort to manage. Using AWS Glue for data preparation typically involves writing PySpark scripts, which violates the "no coding" constraint for business analysts. While AWS Glue DataBrew is good for preparation, using Amazon SageMaker Studio for modeling usually implies coding (e.g., Python notebooks) for model development, which is not suitable for analysts who "do not write code.