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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 MailerSend-connected applications and pipelines for extracting, transforming, and loading MailerSend data. This article shows how to connect to MailerSend with the CData Python Connector and use petl and pandas to extract, transform, and load MailerSend data.
With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live MailerSend data in Python. When you issue complex SQL queries from MailerSend, the driver pushes supported SQL operations, like filters and aggregations, directly to MailerSend and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to MailerSend 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.
The MailerSend API uses API Key authentication via a Bearer token in the Authorization request header.
Your MailerSend API token is required to create a connection. To obtain your API token:
After obtaining your API token, set the following connection properties:
Profile=C:\profiles\Mailersend.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_token';
After installing the CData MailerSend Connector, follow the procedure below to install the other required modules and start accessing MailerSend through Python objects.
Use the pip utility to install the required modules and frameworks:
pip install petl pip install pandas
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 MailerSend Connector to create a connection for working with MailerSend data.
cnxn = mod.connect("Profile=C:\profiles\Mailersend.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_token';")
Use SQL to create a statement for querying MailerSend. In this article, we read data from the Activity entity.
sql = "SELECT , FROM Activity WHERE DomainId = 'domain123'"
With the query results stored in a DataFrame, we can use petl to extract, transform, and load the MailerSend data. In this example, we extract MailerSend data, sort the data by the column, and load the data into a CSV file.
table1 = etl.fromdb(cnxn,sql) table2 = etl.sort(table1,'') etl.tocsv(table2,'activity_data.csv')
With the CData API Driver for Python, you can work with MailerSend data just like you would with any database, including direct access to data in ETL packages like petl.
Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to MailerSend data. Reach out to our Support Team if you have any questions.
import petl as etl
import pandas as pd
import cdata.api as mod
cnxn = mod.connect("Profile=C:\profiles\Mailersend.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_token';")
sql = "SELECT , FROM Activity WHERE DomainId = 'domain123'"
table1 = etl.fromdb(cnxn,sql)
table2 = etl.sort(table1,'')
etl.tocsv(table2,'activity_data.csv')
Connect to live data from MailerSend with the API Driver
Connect to MailerSend