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

How to use SQLAlchemy ORM to access Dynamics NAV Data in Python

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

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

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

Connecting to Dynamics NAV Data

Connecting to Dynamics NAV 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.

Before you can connect, OData Services will need to be enabled on the server. Once OData Services are enabled, you will be able to query any Services that are published on the server.

The User and Password properties, under the Authentication section, must be set to valid Dynamics NAV user credentials. In addition, you will need to specify a URL to a valid Dynamics NAV server organization root and a ServerInstance. If there is not a Service Default Company for the server, you will need to set the Company as well.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Dynamics NAV 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("dynamicsnav:///?http://myserver:7048&User=myserver\Administrator&Password=admin&ServerInstance=DYNAMICSNAV71")

Declare a Mapping Class for Dynamics NAV 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 Customer 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 Customer(base): __tablename__ = "Customer" Name = Column(String,primary_key=True) Prices_Including_VAT = Column(String) ...

Query Dynamics NAV 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("dynamicsnav:///?http://myserver:7048&User=myserver\Administrator&Password=admin&ServerInstance=DYNAMICSNAV71") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Customer).filter_by(Name="Bob"): print("Name: ", instance.Name) print("Prices_Including_VAT: ", instance.Prices_Including_VAT) 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

Customer_table = Customer.metadata.tables["Customer"] for instance in session.execute( == "Bob")): print("Name: ", instance.Name) print("Prices_Including_VAT: ", instance.Prices_Including_VAT) print("---------")

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

Insert Dynamics NAV Data

To insert Dynamics NAV 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 Dynamics NAV.

new_rec = Customer(Name="placeholder", Name="Bob") session.add(new_rec) session.commit()

Update Dynamics NAV Data

To update Dynamics NAV 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 Dynamics NAV.

updated_rec = session.query(Customer).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Name = "Bob" session.commit()

Delete Dynamics NAV Data

To delete Dynamics NAV 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(Customer).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 Dynamics NAV to start building Python apps and scripts with connectivity to Dynamics NAV data. Reach out to our Support Team if you have any questions.