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Extract, Transform, and Load SendGrid Data in Python

The CData Python Connector for SendGrid enables you to create ETL applications and pipelines for SendGrid 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 SendGrid and the petl framework, you can build SendGrid-connected applications and pipelines for extracting, transforming, and loading SendGrid data. This article shows how to connect to SendGrid with the CData Python Connector and use petl and pandas to extract, transform, and load SendGrid data.

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

Connecting to SendGrid Data

Connecting to SendGrid 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 make use of all the available features, provide the User and Password connection properties.

To connect with limited features, you can set the APIKey connection property instead. See the "Getting Started" chapter of the help documentation for a guide to obtaining the API key.

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

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

cnxn = mod.connect("User=admin;Password=abc123;")

Create a SQL Statement to Query SendGrid

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

sql = "SELECT Name, Clicks FROM AdvancedStats WHERE Type = 'Device'"

Extract, Transform, and Load the SendGrid Data

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

Loading SendGrid Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to SendGrid

table1 = [ ['Name','Clicks'], ['NewName1','NewClicks1'], ['NewName2','NewClicks2'], ['NewName3','NewClicks3'] ]

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

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

cnxn = mod.connect("User=admin;Password=abc123;")

sql = "SELECT Name, Clicks FROM AdvancedStats WHERE Type = 'Device'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','Clicks'], ['NewName1','NewClicks1'], ['NewName2','NewClicks2'], ['NewName3','NewClicks3'] ]

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