Extract, Transform, and Load Cloudant Data in Python

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Cloudant Python Connector

Python Connector Libraries for IBM Cloudant Data Connectivity. Integrate IBM Cloudant with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

Connecting to Cloudant Data

Connecting to Cloudant 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.

Set the following connection properties to connect to Cloudant:

  • User: Set this to your username.
  • Password: Set this to your password.

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

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

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

Create a SQL Statement to Query Cloudant

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

sql = "SELECT MovieRuntime, MovieRating FROM Movies WHERE MovieRating = 'R'"

Extract, Transform, and Load the Cloudant Data

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

Loading Cloudant Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Cloudant

table1 = [ ['MovieRuntime','MovieRating'], ['NewMovieRuntime1','NewMovieRating1'], ['NewMovieRuntime2','NewMovieRating2'], ['NewMovieRuntime3','NewMovieRating3'] ]

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

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

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

sql = "SELECT MovieRuntime, MovieRating FROM Movies WHERE MovieRating = 'R'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['MovieRuntime','MovieRating'], ['NewMovieRuntime1','NewMovieRating1'], ['NewMovieRuntime2','NewMovieRating2'], ['NewMovieRuntime3','NewMovieRating3'] ]

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