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

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


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Google 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 Google Data Catalog and the SQLAlchemy toolkit, you can build Google Data Catalog-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Google Data Catalog data to query Google Data Catalog data.

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

Connecting to Google Data Catalog Data

Connecting to Google 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.

Google Data Catalog uses the OAuth authentication standard. Authorize access to Google APIs on behalf on individual users or on behalf of users in a domain.

Before connecting, specify the following to identify the organization and project you would like to connect to:

  • OrganizationId: The ID associated with the Google Cloud Platform organization resource you would like to connect to. Find this by navigating to the cloud console.

    Click the project selection drop-down, and select your organization from the list. Then, click More -> Settings. The organization ID is displayed on this page.

  • ProjectId: The ID associated with the Google Cloud Platform project resource you would like to connect to.

    Find this by navigating to the cloud console dashboard and selecting your project from the Select from drop-down. The project ID will be present in the Project info card.

When you connect, the OAuth endpoint opens in your default browser. Log in and grant permissions to the application to completes the OAuth process. For more information, refer to the OAuth section in the Help documentation.

Follow the procedure below to install SQLAlchemy and start accessing Google 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 Google 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 Google 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("googledatacatalog:///?ProjectId=YourProjectId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Google 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 Schemas 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 Schemas(base): __tablename__ = "Schemas" Type = Column(String,primary_key=True) DatasetName = Column(String) ...

Query Google 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("googledatacatalog:///?ProjectId=YourProjectId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Schemas).filter_by(ProjectId="bigquery-public-data"): print("Type: ", instance.Type) print("DatasetName: ", instance.DatasetName) 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

Schemas_table = Schemas.metadata.tables["Schemas"] for instance in session.execute(Schemas_table.select().where(Schemas_table.c.ProjectId == "bigquery-public-data")): print("Type: ", instance.Type) print("DatasetName: ", instance.DatasetName) 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 Google Data Catalog to start building Python apps and scripts with connectivity to Google Data Catalog data. Reach out to our Support Team if you have any questions.