You need to resample irregular, partly missing time-series data to daily frequency and export it for modeling with the least implementation effort. Which tool is the simplest option?

Amazon SageMaker Studio Data Wrangler is the simplest option because it provides a visual interface for data preparation, including built-in transformations for time-series data like resampling and handling missing values. This significantly reduces the implementation effort compared to coding. While Amazon EMR Serverless with PySpark offers powerful distributed processing, it requires writing and managing code. A SageMaker Studio notebook with pandas is also code-based, demanding manual scripting for resampling and imputation. AWS Glue DataBrew is a good option for data preparation but Data Wrangler is more tightly integrated with SageMaker Studio and specifically designed for ML data preparation workflows, often making it more intuitive for this specific task within the SageMaker ecosystem.