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Extract, Transform, and Load Cassandra Data in Python

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

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

Connecting to Cassandra Data

Connecting to Cassandra 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 Server, Port, and Database connection properties to connect to Cassandra. Additionally, to use internal authentication set the User and Password connection properties.

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

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

cnxn = mod.connect("Database=MyCassandraDB;Port=7000;Server=127.0.0.1;")

Create a SQL Statement to Query Cassandra

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

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

Extract, Transform, and Load the Cassandra Data

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

Loading Cassandra 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 Cassandra

table1 = [ ['City','TotalDue'], ['NewCity1','NewTotalDue1'], ['NewCity2','NewTotalDue2'], ['NewCity3','NewTotalDue3'] ]

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

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

cnxn = mod.connect("Database=MyCassandraDB;Port=7000;Server=127.0.0.1;")

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

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['City','TotalDue'], ['NewCity1','NewTotalDue1'], ['NewCity2','NewTotalDue2'], ['NewCity3','NewTotalDue3'] ]

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