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Extract, Transform, and Load DigitalOcean Data in Python

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

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

Connecting to DigitalOcean Data

Connecting to DigitalOcean 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.

DigitalOcean uses OAuth 2.0 authentication. To authenticate using OAuth, you can use the embedded credentials or register an app with DigitalOcean.

See the Getting Started guide in the CData driver documentation for more information.

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query DigitalOcean

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

sql = "SELECT Id, Name FROM Droplets WHERE Id = '1'"

Extract, Transform, and Load the DigitalOcean Data

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

Loading DigitalOcean Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to DigitalOcean

table1 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, Name FROM Droplets WHERE Id = '1'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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