Use SQLAlchemy ORMs to Access Snowflake Data in Python

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Snowflake Python Connector

Python Connector Libraries for Snowflake Enterprise Data Warehouse Data Connectivity. Integrate Snowflake Enterprise Data Warehouse with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Snowflake enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Snowflake data.

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

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

Connecting to Snowflake Data

Connecting to Snowflake 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.

To connect to Snowflake:

  1. Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
  2. Set URL to the URL of the Snowflake instance (i.e.: https://myaccount.snowflakecomputing.com).
  3. Set Warehouse to the Snowflake warehouse.
  4. (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
  5. (Optional) Set Database and Schema to restrict the tables and views exposed.

See the Getting Started guide in the CData driver documentation for more information.

Follow the procedure below to install SQLAlchemy and start accessing Snowflake through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Snowflake Data in Python

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

engine = create_engine("snowflake:///?User=Admin&Password=test123&Server=localhost&Database=Northwind&Warehouse=TestWarehouse&Account=Tester1")

Declare a Mapping Class for Snowflake 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 Products 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 Products(base):
	__tablename__ = "Products"
	Id = Column(String,primary_key=True)
	ProductName = Column(String)
	...

Query Snowflake 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("snowflake:///?User=Admin&Password=test123&Server=localhost&Database=Northwind&Warehouse=TestWarehouse&Account=Tester1")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Products).filter_by(Id="1"):
	print("Id: ", instance.Id)
	print("ProductName: ", instance.ProductName)
	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

Products_table = Products.metadata.tables["Products"]
for instance in session.execute(Products_table.select().where(Products_table.c.Id == "1")):
	print("Id: ", instance.Id)
	print("ProductName: ", instance.ProductName)
	print("---------")

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

Insert Snowflake Data

To insert Snowflake data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Snowflake.

new_rec = Products(Id="placeholder", Id="1")
session.add(new_rec)
session.commit()

Update Snowflake Data

To update Snowflake data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Snowflake.

updated_rec = session.query(Products).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Id = "1"
session.commit()

Delete Snowflake Data

To delete Snowflake data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Products).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

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