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AWS Data Wrangler (beta)

Utility belt to handle data on AWS.

Contents: Use Cases | Installation | Examples

Use Cases

  • Pandas -> Parquet (S3)
  • Pandas -> CSV (S3)
  • Pandas -> Glue Catalog
  • Pandas -> Athena
  • Pandas -> Redshift
  • CSV (S3) -> Pandas
  • Athena -> Pandas
  • PySpark -> Redshift

Installation

pip install awswrangler

Runs only with Python 3.6 and beyond.

Runs anywhere (AWS Lambda, AWS Glue, EMR, EC2, on-premises, local, etc).

P.S. Lambda Layer bundle and Glue egg are available to download. It's just upload to your account and run! 🚀

Examples

Writing Pandas Dataframe to S3 + Glue Catalog

session = awswrangler.Session()
session.pandas.to_parquet(
    dataframe=dataframe,
    database="database",
    path="s3://...",
    partition_cols=["col_name"],
)

If a Glue Database name is passed, all the metadata will be created in the Glue Catalog. If not, only the s3 data write will be done.

Reading from AWS Athena to Pandas

session = awswrangler.Session()
dataframe = session.pandas.read_sql_athena(
    sql="select * from table",
    database="database"
)

Reading from S3 (CSV) to Pandas

session = awswrangler.Session()
dataframe = session.pandas.read_csv(path="s3://...")

Typical Pandas ETL

import pandas
import awswrangler

df = pandas.read_...  # Read from anywhere

# Typical Pandas, Numpy or Pyarrow transformation HERE!

session = awswrangler.Session()
session.pandas.to_parquet(  # Storing the data and metadata to Data Lake
    dataframe=dataframe,
    database="database",
    path="s3://...",
    partition_cols=["col_name"],
)

Loading Pyspark Dataframe to Redshift

session = awswrangler.Session(spark_session=spark)
session.spark.to_redshift(
    dataframe=df,
    path="s3://...",
    connection=conn,
    schema="public",
    table="table",
    iam_role="IAM_ROLE_ARN",
    mode="append",
)