Use SQLAlchemy ORMs to Access AWS Management Data in Python

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AWS Management Python Connector

Python Connector Libraries for Amazon AWS Management Data Connectivity. Integrate Amazon AWS Management with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

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

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

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

Connecting to AWS Management Data

Connecting to AWS Management 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 authorize AWSDataManagement requests, provide the credentials for an administrator account or for an IAM user with custom permissions:

  1. Set AccessKey to the access key Id.
  2. Set SecretKey to the secret access key.
  3. Set Region to the region where your AWSDataManagement data is hosted.

Note: Though you can connect as the AWS account administrator, it is recommended to use IAM user credentials to access AWS services.

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

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

engine = create_engine("awsdatamanagement:///?AccessKey=myAccessKey&Account=myAccountName&Region=us-east-1")

Declare a Mapping Class for AWS Management 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 NorthwingProducts 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 NorthwingProducts(base):
	__tablename__ = "NorthwingProducts"
	PartitionKey = Column(String,primary_key=True)
	Name = Column(String)

Query AWS Management 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("awsdatamanagement:///?AccessKey=myAccessKey&Account=myAccountName&Region=us-east-1")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(NorthwingProducts).filter_by(Id="1"):
	print("PartitionKey: ", instance.PartitionKey)
	print("Name: ", instance.Name)

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

NorthwingProducts_table = NorthwingProducts.metadata.tables["NorthwingProducts"]
for instance in session.execute( == "1")):
	print("PartitionKey: ", instance.PartitionKey)
	print("Name: ", instance.Name)

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

Insert AWS Management Data

To insert AWS Management 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 AWS Management.

new_rec = NorthwingProducts(PartitionKey="placeholder", Id="1")

Update AWS Management Data

To update AWS Management 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 AWS Management.

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

Delete AWS Management Data

To delete AWS Management 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(NorthwingProducts).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

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