AWS Data Wrangler

About AWS Data Wrangler

A utility belt for handling data on AWS using Python. AWS Data Wrangler extends Pandas with connectors to AWS services like S3, Glue, Athena, Redshift, and more, simplifying data ingestion and extraction in AWS-based pipelines.

Key Features

• 1AWS-integrated pandas extension for reading from and writing to AWS services
• 2Reads Parquet, CSV, and JSON from S3 directly into pandas DataFrames
• 3Writes DataFrames to S3, Glue Data Catalog, Redshift, and Athena
• 4Query Athena and return results as a DataFrame with one function call
• 5Handles partitioning, compression, and catalog registration automatically

How Python Data Engineers Use AWS Data Wrangler

AWS Data Wrangler (now called `awswrangler`) is the standard tool for AWS-native Python data pipelines. Engineers replace `boto3` + `pandas` boilerplate with single calls: `wr.s3.read_parquet('s3://bucket/prefix/')` reads all files into a DataFrame, and `wr.s3.to_parquet(df, 's3://bucket/output/', dataset=True)` writes back with Glue catalog registration and partitioning.