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The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Monday.com and the SQLAlchemy toolkit, you can build Monday.com-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Monday.com data to query Monday.com data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Monday.com data in Python. When you issue complex SQL queries from Monday.com, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Monday.com and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Monday.com 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 can connect to Monday.com using either API Token authentication or OAuth authentication.
Connect to Monday.com by specifying the APIToken. Set the AuthScheme to Token and obtain the APIToken as follows:
Alternatively, you can establish a connection using OAuth (refer to the OAuth section of the Help documentation).
Follow the procedure below to install SQLAlchemy and start accessing Monday.com through Python objects.
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
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Monday.com 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("monday:///?APIToken=eyJhbGciOiJIUzI1NiJ9.yJ0aWQiOjE0MTc4NzIxMiwidWlkIjoyNzI3ODM3OSwiaWFkIjoiMjAyMi0wMS0yMFQxMDo0NjoxMy45NDFaIiwicGV")
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 Invoices 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 Invoices(base): __tablename__ = "Invoices" Id = Column(String,primary_key=True) DueDate = Column(String) ...
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.
engine = create_engine("monday:///?APIToken=eyJhbGciOiJIUzI1NiJ9.yJ0aWQiOjE0MTc4NzIxMiwidWlkIjoyNzI3ODM3OSwiaWFkIjoiMjAyMi0wMS0yMFQxMDo0NjoxMy45NDFaIiwicGV")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Invoices).filter_by(Status="SENT"):
print("Id: ", instance.Id)
print("DueDate: ", instance.DueDate)
print("---------")
Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.
Invoices_table = Invoices.metadata.tables["Invoices"]
for instance in session.execute(Invoices_table.select().where(Invoices_table.c.Status == "SENT")):
print("Id: ", instance.Id)
print("DueDate: ", instance.DueDate)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Download a free, 30-day trial of the CData Python Connector for Monday.com to start building Python apps and scripts with connectivity to Monday.com data. Reach out to our Support Team if you have any questions.
Download a Community License of the Monday.com Connector to get started:
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