How to use SQLAlchemy ORM to access Azure Data Lake Storage Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Azure Data Lake Storage and the SQLAlchemy toolkit, you can build Azure Data Lake Storage-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Azure Data Lake Storage data to query 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 CData Connector 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.

Follow the procedure below to install SQLAlchemy and start accessing Azure Data Lake Storage through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy
pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Model Azure Data Lake Storage Data in Python

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

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("adls:///?Schema=ADLSGen2&Account=myAccount&FileSystem=myFileSystem&AccessKey=myAccessKey&InitiateOAuth=GETANDREFRESH")

Declare a Mapping Class for Azure Data Lake Storage Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Resources table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Resources(base):
	__tablename__ = "Resources"
	FullPath = Column(String,primary_key=True)
	Permission = Column(String)
	...

Query Azure Data Lake Storage Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("adls:///?Schema=ADLSGen2&Account=myAccount&FileSystem=myFileSystem&AccessKey=myAccessKey&InitiateOAuth=GETANDREFRESH")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Resources).filter_by(Type="FILE"):
	print("FullPath: ", instance.FullPath)
	print("Permission: ", instance.Permission)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Resources_table = Resources.metadata.tables["Resources"]
for instance in session.execute(Resources_table.select().where(Resources_table.c.Type == "FILE")):
	print("FullPath: ", instance.FullPath)
	print("Permission: ", instance.Permission)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

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.