Data preparation is the process of collecting, cleaning, and transforming raw data to make it suitable for insight extraction through machine learning (ML) and analytics. Data preparation is crucial for ML and analytics pipelines. Your model and insights will only be as reliable as the data you use for training them. Flawed data will produce poor results regardless of the sophistication of your algorithms and analytical tools.
Amazon SageMaker Data Wrangler is a service to help data scientists and data engineers simplify and accelerate tabular and time series data preparation and feature engineering through a visual interface. You can import data from multiple data sources, such as Amazon Simple Storage Service (Amazon S3), Amazon Athena, Amazon Redshift, Snowflake, and DataBricks, and process your data with over 300 built-in data transformations and a library of code snippets, so you can quickly normalize, transform, and combine features without writing any code. You

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