How to Build an ETL App for Redis Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Redis and the petl framework, you can build Redis-connected applications and pipelines for extracting, transforming, and loading Redis data. This article shows how to connect to Redis with the CData Python Connector and use petl and pandas to extract, transform, and load Redis data.

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

Connecting to Redis Data

Connecting to Redis 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 a Redis instance:

• Server: Set this to the name or address of the server your Redis instance is running on. You can specify the port in Port.
• Password: Set this to the password used to authenticate with a password-protected Redis instance , using the Redis AUTH command.

Set UseSSL to negotiate SSL/TLS encryption when you connect.

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

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

cnxn = mod.connect("Server=127.0.0.1;Port=6379;Password=myPassword;")

Create a SQL Statement to Query Redis

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

sql = "SELECT City, CompanyName FROM Customers WHERE Country = 'US'"

Extract, Transform, and Load the Redis Data

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

Loading Redis Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Redis

table1 = [ ['City','CompanyName'], ['NewCity1','NewCompanyName1'], ['NewCity2','NewCompanyName2'], ['NewCity3','NewCompanyName3'] ]

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

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

cnxn = mod.connect("Server=127.0.0.1;Port=6379;Password=myPassword;")

sql = "SELECT City, CompanyName FROM Customers WHERE Country = 'US'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['City','CompanyName'], ['NewCity1','NewCompanyName1'], ['NewCity2','NewCompanyName2'], ['NewCity3','NewCompanyName3'] ]

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