How to Build an ETL App for Amazon S3 Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Amazon S3 and the petl framework, you can build Amazon S3-connected applications and pipelines for extracting, transforming, and loading Amazon S3 data. This article shows how to connect to Amazon S3 with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData Amazon S3 Connector, follow the procedure below to install the other required modules and start accessing Amazon S3 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 Amazon S3 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.amazons3 as mod

You can now connect with a connection string. Use the connect function for the CData Amazon S3 Connector to create a connection for working with Amazon S3 data.

cnxn = mod.connect("AccessKey=a123;SecretKey=s123;")

Create a SQL Statement to Query Amazon S3

Use SQL to create a statement for querying Amazon S3. In this article, we read data from the ObjectsACL entity.

sql = "SELECT Name, OwnerId FROM ObjectsACL WHERE Name = 'TestBucket'"

Extract, Transform, and Load the Amazon S3 Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Amazon S3 data. In this example, we extract Amazon S3 data, sort the data by the OwnerId column, and load the data into a CSV file.

Loading Amazon S3 Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Amazon S3, you can work with Amazon S3 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 CData Python Connector for Amazon S3 to start building Python apps and scripts with connectivity to Amazon S3 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.amazons3 as mod

cnxn = mod.connect("AccessKey=a123;SecretKey=s123;")

sql = "SELECT Name, OwnerId FROM ObjectsACL WHERE Name = 'TestBucket'"

table1 = etl.fromdb(cnxn,sql)

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

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