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Python Connector Libraries for Azure Data Catalog Data Connectivity. Integrate Azure Data Catalog with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

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How to use SQLAlchemy ORM to access Azure Data Catalog Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Azure Data Catalog data.

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 Catalog and the SQLAlchemy toolkit, you can build Azure Data Catalog-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Azure Data Catalog data to query Azure Data Catalog data.

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

Connecting to Azure Data Catalog Data

Connecting to Azure Data Catalog 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.

You can optionally set the following to read the different catalog data returned from Azure Data Catalog.

    CatalogName: Set this to the CatalogName associated with your Azure Data Catalog. To get your Catalog name, navigate to your Azure Portal home page > Data Catalog > Catalog Name

Connect Using OAuth Authentication

You must use OAuth to authenticate with Azure Data Catalog. OAuth requires the authenticating user to interact with Azure Data Catalog using the browser. For more information, refer to the OAuth section in the help documentation.

Follow the procedure below to install SQLAlchemy and start accessing Azure Data Catalog 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 Catalog 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 Catalog 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("azuredatacatalog:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Azure Data Catalog 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 Tables 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 Tables(base): __tablename__ = "Tables" DslAddressDatabase = Column(String,primary_key=True) Type = Column(String) ...

Query Azure Data Catalog 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("azuredatacatalog:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Tables).filter_by(Name="FactProductInventory"): print("DslAddressDatabase: ", instance.DslAddressDatabase) print("Type: ", instance.Type) 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

Tables_table = Tables.metadata.tables["Tables"] for instance in session.execute(Tables_table.select().where(Tables_table.c.Name == "FactProductInventory")): print("DslAddressDatabase: ", instance.DslAddressDatabase) print("Type: ", instance.Type) 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 Catalog to start building Python apps and scripts with connectivity to Azure Data Catalog data. Reach out to our Support Team if you have any questions.