Use SQLAlchemy ORMs to Access Epicor Kinetic Data in Python

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

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

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

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

Connecting to Epicor Kinetic Data

Connecting to Epicor Kinetic 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 successfully connect to your ERP instance, you must specify the following connection properties:

  • Url:the URL of the server hosting your ERP instance. For example,
  • ERPInstance: the name of your ERP instance.
  • User: the username of your account.
  • Password: the password of your account.
  • Service: the service you want to retrieve data from. For example, BaqSvc.

In addition, you may also set the optional connection properties:

  • ApiKey: An optional key that may be required for connection to some services depending on your account configuration.
  • ApiVersion: Defaults to v1. May be set to v2 to use the newer Epicor API.
  • Company: Required if you set the ApiVersion to v2.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Epicor Kinetic 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("epicorkinetic:///?Service=Erp.BO.CustomerSvc&ERPInstance=MyInstance&URL=")

Declare a Mapping Class for Epicor Kinetic 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 Customers 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 Customers(base): __tablename__ = "Customers" CustNum = Column(String,primary_key=True) Company = Column(String) ...

Query Epicor Kinetic 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("epicorkinetic:///?Service=Erp.BO.CustomerSvc&ERPInstance=MyInstance&URL=") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Customers).filter_by(CompanyName="CompanyName"): print("CustNum: ", instance.CustNum) print("Company: ", instance.Company) 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

Customers_table = Customers.metadata.tables["Customers"] for instance in session.execute( == "CompanyName")): print("CustNum: ", instance.CustNum) print("Company: ", instance.Company) print("---------")

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

Insert Epicor Kinetic Data

To insert Epicor Kinetic 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 Epicor Kinetic.

new_rec = Customers(CustNum="placeholder", CompanyName="CompanyName") session.add(new_rec) session.commit()

Update Epicor Kinetic Data

To update Epicor Kinetic 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 Epicor Kinetic.

updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.CompanyName = "CompanyName" session.commit()

Delete Epicor Kinetic Data

To delete Epicor Kinetic 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(Customers).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 Epicor Kinetic Python Connector to start building Python apps and scripts with connectivity to Epicor Kinetic data. Reach out to our Support Team if you have any questions.