Use SQLAlchemy ORMs to Access Amazon S3 Data in Python

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Amazon S3 Python Connector

Python Connector Libraries for Amazon S3 Data Connectivity. Integrate Amazon S3 with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Amazon S3 enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Amazon S3 data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Amazon S3 and the SQLAlchemy toolkit, you can build Amazon S3-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Amazon S3 data to query Amazon S3 data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Amazon S3 data in Python. When you issue complex SQL queries from Amazon S3, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Amazon S3 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Amazon S3 Data

Connecting to Amazon S3 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 Amazon S3 requests, provide the credentials for an administrator account or for an IAM user with custom permissions. Set AccessKey to the access key Id. Set SecretKey to the secret access key.

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

For information on obtaining the credentials and other authentication methods, refer to the Getting Started section of the Help documentation.

Follow the procedure below to install SQLAlchemy and start accessing Amazon S3 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 Amazon S3 Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Amazon S3 data.

engine = create_engine("amazons3:///?AccessKey=a123&SecretKey=s123")

Declare a Mapping Class for Amazon S3 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 ObjectsACL 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 ObjectsACL(base):
	__tablename__ = "ObjectsACL"
	Name = Column(String,primary_key=True)
	OwnerId = Column(String)
	...

Query Amazon S3 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("amazons3:///?AccessKey=a123&SecretKey=s123")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(ObjectsACL).filter_by(Name="TestBucket"):
	print("Name: ", instance.Name)
	print("OwnerId: ", instance.OwnerId)
	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

ObjectsACL_table = ObjectsACL.metadata.tables["ObjectsACL"]
for instance in session.execute(ObjectsACL_table.select().where(ObjectsACL_table.c.Name == "TestBucket")):
	print("Name: ", instance.Name)
	print("OwnerId: ", instance.OwnerId)
	print("---------")

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

Free Trial & More Information

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