Use SQLAlchemy ORMs to Access Amazon Athena Data in Python

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Amazon Athena Python Connector

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



The CData Python Connector for Amazon Athena enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Amazon Athena 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 Athena and the SQLAlchemy toolkit, you can build Amazon Athena-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Amazon Athena data to query, update, delete, and insert Amazon Athena data.

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

Connecting to Amazon Athena Data

Connecting to Amazon Athena 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.

Authenticating to Amazon Athena

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

Obtaining the Access Key

To obtain the credentials for an IAM user, follow the steps below:

  1. Sign into the IAM console.
  2. In the navigation pane, select Users.
  3. To create or manage the access keys for a user, select the user and then select the Security Credentials tab.

To obtain the credentials for your AWS root account, follow the steps below:

  1. Sign into the AWS Management console with the credentials for your root account.
  2. Select your account name or number and select My Security Credentials in the menu that is displayed.
  3. Click Continue to Security Credentials and expand the Access Keys section to manage or create root account access keys.

Authenticating from an EC2 Instance

If you are using the CData Data Provider for Amazon Athena 2018 from an EC2 Instance and have an IAM Role assigned to the instance, you can use the IAM Role to authenticate. To do so, set UseEC2Roles to true and leave AccessKey and SecretKey empty. The CData Data Provider for Amazon Athena 2018 will automatically obtain your IAM Role credentials and authenticate with them.

Authenticating as an AWS Role

In many situations it may be preferable to use an IAM role for authentication instead of the direct security credentials of an AWS root user. An AWS role may be used instead by specifying the RoleARN. This will cause the CData Data Provider for Amazon Athena 2018 to attempt to retrieve credentials for the specified role. If you are connecting to AWS (instead of already being connected such as on an EC2 instance), you must additionally specify the AccessKey and SecretKey of an IAM user to assume the role for. Roles may not be used when specifying the AccessKey and SecretKey of an AWS root user.

Authenticating with MFA

For users and roles that require Multi-factor Authentication, specify the MFASerialNumber and MFAToken connection properties. This will cause the CData Data Provider for Amazon Athena 2018 to submit the MFA credentials in a request to retrieve temporary authentication credentials. Note that the duration of the temporary credentials may be controlled via the TemporaryTokenDuration (default 3600 seconds).

Connecting to Amazon Athena

In addition to the AccessKey and SecretKey properties, specify Database, S3StagingDirectory and Region. Set Region to the region where your Amazon Athena data is hosted. Set S3StagingDirectory to a folder in S3 where you would like to store the results of queries.

If Database is not set in the connection, the data provider connects to the default database set in Amazon Athena.

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

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

engine = create_engine("amazonathena:///?AccessKey='a123'&SecretKey='s123'&Region='IRELAND'&Database='sampledb'&S3StagingDirectory='s3://bucket/staging/'")

Declare a Mapping Class for Amazon Athena 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"
	Name = Column(String,primary_key=True)
	TotalDue = Column(String)
	...

Query Amazon Athena 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("amazonathena:///?AccessKey='a123'&SecretKey='s123'&Region='IRELAND'&Database='sampledb'&S3StagingDirectory='s3://bucket/staging/'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customers).filter_by(CustomerId="12345"):
	print("Name: ", instance.Name)
	print("TotalDue: ", instance.TotalDue)
	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(Customers_table.select().where(Customers_table.c.CustomerId == "12345")):
	print("Name: ", instance.Name)
	print("TotalDue: ", instance.TotalDue)
	print("---------")

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

Insert Amazon Athena Data

To insert Amazon Athena 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 Amazon Athena.

new_rec = Customers(Name="placeholder", CustomerId="12345")
session.add(new_rec)
session.commit()

Update Amazon Athena Data

To update Amazon Athena 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 Amazon Athena.

updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.CustomerId = "12345"
session.commit()

Delete Amazon Athena Data

To delete Amazon Athena 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

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