How to Build an ETL App for Aweber 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 API Driver for Python and the petl framework, you can build Aweber-connected applications and pipelines for extracting, transforming, and loading Aweber data. This article shows how to connect to Aweber with the CData Python Connector and use petl and pandas to extract, transform, and load Aweber data.

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

Connecting to Aweber Data

Connecting to Aweber 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.

Start by setting the Profile connection property to the location of the Aweber Profile on disk (e.g. C:\profiles\Aweber.apip). Next, set the ProfileSettings connection property to the connection string for Aweber (see below).

Aweber API Profile Settings

Register a free developer account with AWeber and create an app to receive your client ID and secret.

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

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

cnxn = mod.connect("Profile=C:\profiles\Aweber.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Create a SQL Statement to Query Aweber

Use SQL to create a statement for querying Aweber. In this article, we read data from the Accounts entity.

sql = "SELECT Id, IntegrationsCollectionLink FROM Accounts WHERE Id = '1'"

Extract, Transform, and Load the Aweber Data

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

Loading Aweber Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Aweber 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 API Driver for Python to start building Python apps and scripts with connectivity to Aweber 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Aweber.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

sql = "SELECT Id, IntegrationsCollectionLink FROM Accounts WHERE Id = '1'"

table1 = etl.fromdb(cnxn,sql)

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

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