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

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How to use SQLAlchemy ORM to access Veeva Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Veeva data.

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

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

Connecting to Veeva Data

Connecting to Veeva 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 are ready to connect after specifying the following connection properties:

  • Url: The host you see in the URL after you login to your account. For example: https://my-veeva-domain.veevavault.com
  • User: The username you use to login to your account.
  • Password: The password you use to login to your account.

Follow the procedure below to install SQLAlchemy and start accessing Veeva 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 Veeva Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Veeva 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("veevavault:///?User=myuser&Password=mypassword&Server=localhost&Database=mydatabase")

Declare a Mapping Class for Veeva 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 NorthwindProducts 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 NorthwindProducts(base): __tablename__ = "NorthwindProducts" ProductId = Column(String,primary_key=True) ProductName = Column(String) ...

Query Veeva 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("veevavault:///?User=myuser&Password=mypassword&Server=localhost&Database=mydatabase") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(NorthwindProducts).filter_by(CategoryId="5"): print("ProductId: ", instance.ProductId) 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

NorthwindProducts_table = NorthwindProducts.metadata.tables["NorthwindProducts"] for instance in session.execute(NorthwindProducts_table.select().where(NorthwindProducts_table.c.CategoryId == "5")): print("ProductId: ", instance.ProductId) 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 Veeva Data

To insert Veeva 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 Veeva.

new_rec = NorthwindProducts(ProductId="placeholder", CategoryId="5") session.add(new_rec) session.commit()

Update Veeva Data

To update Veeva 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 Veeva.

updated_rec = session.query(NorthwindProducts).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.CategoryId = "5" session.commit()

Delete Veeva Data

To delete Veeva 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(NorthwindProducts).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 CData Python Connector for Veeva to start building Python apps and scripts with connectivity to Veeva data. Reach out to our Support Team if you have any questions.