How to Build an ETL App for Azure Data Lake Storage Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Azure Data Lake Storage and the petl framework, you can build Azure Data Lake Storage-connected applications and pipelines for extracting, transforming, and loading Azure Data Lake Storage data. This article shows how to connect to Azure Data Lake Storage with the CData Python Connector and use petl and pandas to extract, transform, and load Azure Data Lake Storage data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Azure Data Lake Storage data in Python. When you issue complex SQL queries from Azure Data Lake Storage, the driver pushes supported SQL operations, like filters and aggregations, directly to Azure Data Lake Storage and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Azure Data Lake Storage Data

Connecting to Azure Data Lake Storage data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Authenticating to a Gen 1 DataLakeStore Account

Gen 1 uses OAuth 2.0 in Entra ID (formerly Azure AD) for authentication.

For this, an Active Directory web application is required. You can create one as follows:

1. Sign in to your Azure Account through the
2. Select "Entra ID" (formerly Azure AD).
3. Select "App registrations".
4. Select "New application registration".
5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

• Schema: Set this to ADLSGen1.
• Account: Set this to the name of the account.
• OAuthClientId: Set this to the application Id of the app you created.
• OAuthClientSecret: Set this to the key generated for the app you created.
• TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
• Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

Authenticating to a Gen 2 DataLakeStore Account

To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

• Schema: Set this to ADLSGen2.
• Account: Set this to the name of the account.
• FileSystem: Set this to the file system which will be used for this account.
• AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
• Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

After installing the CData Azure Data Lake Storage Connector, follow the procedure below to install the other required modules and start accessing Azure Data Lake Storage through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Azure Data Lake Storage Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.adls as mod

You can now connect with a connection string. Use the connect function for the CData Azure Data Lake Storage Connector to create a connection for working with Azure Data Lake Storage data.

cnxn = mod.connect("Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Azure Data Lake Storage

Use SQL to create a statement for querying Azure Data Lake Storage. In this article, we read data from the Resources entity.

sql = "SELECT FullPath, Permission FROM Resources WHERE Type = 'FILE'"

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

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Azure Data Lake Storage data. In this example, we extract Azure Data Lake Storage data, sort the data by the Permission column, and load the data into a CSV file.

Loading Azure Data Lake Storage Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Permission')

etl.tocsv(table2,'resources_data.csv')

With the CData Python Connector for Azure Data Lake Storage, you can work with Azure Data Lake Storage data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Azure Data Lake Storage to start building Python apps and scripts with connectivity to Azure Data Lake Storage data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.adls as mod

cnxn = mod.connect("Schema=ADLSGen2;Account=myAccount;FileSystem=myFileSystem;AccessKey=myAccessKey;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT FullPath, Permission FROM Resources WHERE Type = 'FILE'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Permission')

etl.tocsv(table2,'resources_data.csv')