The provider can be used to create ETL applications and pipelines for CSV data in Python using Petl.

Install Required Modules

Install the Petl modules using the pip utility.

pip install petl

Connecting

Import the modules, including the CData Python Connector for Azure Data Lake Storage. You can then use the provider's connect function to create a connection using a valid Azure Data Lake Storage connection string. A SQLAlchemy engine may also be used instead of a direct connection.

import petl as etl
import cdata.adls as mod
cnxn = mod.connect("Schema=ADLSGen2;Account=MyStorageAccount;FileSystem=MyBlobContainer;AccessKey=myAccessKey;")

Extract, Transform, and Load the Azure Data Lake Storage Data

Create a SQL query string and store the query results in a DataFrame.

sql = "SELECT	FullPath, Permission FROM Resources "
table1 = etl.fromdb(cnxn,sql)

Loading Data

With the query results stored in a DataFrame, you can load your data into any supported Petl destination. The following example loads the data into a CSV file.

etl.tocsv(table1,'output.csv')