Ready to get started?

Learn more about the CData Python Connector for Couchbase or download a free trial:

Download Now

Extract, Transform, and Load Couchbase Data in Python

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

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

Connecting to Couchbase Data

Connecting to Couchbase 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 connect using the Login method, set User, Password, and Server to the credentials for the account and the address of the server you want to connect to.

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

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

cnxn = mod.connect("User=myuseraccount;Password=mypassword;Server=http://mycouchbaseserver;")

Create a SQL Statement to Query Couchbase

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

sql = "SELECT FirstName, TotalDue FROM Customer WHERE FirstName = 'Bob'"

Extract, Transform, and Load the Couchbase Data

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

Loading Couchbase Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Couchbase

table1 = [ ['FirstName','TotalDue'], ['NewFirstName1','NewTotalDue1'], ['NewFirstName2','NewTotalDue2'], ['NewFirstName3','NewTotalDue3'] ]

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

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

cnxn = mod.connect("User=myuseraccount;Password=mypassword;Server=http://mycouchbaseserver;")

sql = "SELECT FirstName, TotalDue FROM Customer WHERE FirstName = 'Bob'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['FirstName','TotalDue'], ['NewFirstName1','NewTotalDue1'], ['NewFirstName2','NewTotalDue2'], ['NewFirstName3','NewTotalDue3'] ]

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