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Extract, Transform, and Load AWS Management Data in Python

The CData Python Connector for AWS Management enables you to create ETL applications and pipelines for AWS Management data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for AWS Management and the petl framework, you can build AWS Management-connected applications and pipelines for extracting, transforming, and loading AWS Management data. This article shows how to connect to AWS Management with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData AWS Management Connector, follow the procedure below to install the other required modules and start accessing AWS Management through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for AWS Management Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.awsdatamanagement as mod

You can now connect with a connection string. Use the connect function for the CData AWS Management Connector to create a connection for working with AWS Management data.

cnxn = mod.connect("AccessKey=myAccessKey;Account=myAccountName;Region=us-east-1;")

Create a SQL Statement to Query AWS Management

Use SQL to create a statement for querying AWS Management. In this article, we read data from the NorthwingProducts entity.

sql = "SELECT PartitionKey, Name FROM NorthwingProducts WHERE Id = '1'"

Extract, Transform, and Load the AWS Management Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the AWS Management data. In this example, we extract AWS Management data, sort the data by the Name column, and load the data into a CSV file.

Loading AWS Management Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Name')

etl.tocsv(table2,'northwingproducts_data.csv')

In the following example, we add new rows to the NorthwingProducts table.

Adding New Rows to AWS Management

table1 = [ ['PartitionKey','Name'], ['NewPartitionKey1','NewName1'], ['NewPartitionKey2','NewName2'], ['NewPartitionKey3','NewName3'] ]

etl.appenddb(table1, cnxn, 'NorthwingProducts')

With the CData Python Connector for AWS Management, you can work with AWS Management data just like you would with any database, including direct access to data in ETL packages like petl.

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.



Full Source Code


import petl as etl
import pandas as pd
import cdata.awsdatamanagement as mod

cnxn = mod.connect("AccessKey=myAccessKey;Account=myAccountName;Region=us-east-1;")

sql = "SELECT PartitionKey, Name FROM NorthwingProducts WHERE Id = '1'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Name')

etl.tocsv(table2,'northwingproducts_data.csv')

table3 = [ ['PartitionKey','Name'], ['NewPartitionKey1','NewName1'], ['NewPartitionKey2','NewName2'], ['NewPartitionKey3','NewName3'] ]

etl.appenddb(table3, cnxn, 'NorthwingProducts')