A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates.The time-series data has irregular timestamps and is missing some values.Data scientist must build a dataset to replace the missing values.The data scientist needs a solution that resamples the data daily and exports the data for further modeling.
Which solution will meet these requirements with the LEAST implementation effort?
Use Amazon EMR Serverless with PySpark.
Use AWS Glue DataBrew.
Use Amazon SageMaker Studio Data Wrangler.
Use Amazon SageMaker Studio Notebook with Pandas.
In my experience, the answer is:
Use Amazon SageMaker Studio Data Wrangler.