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Extract, Transform, and Load Redshift Data in Python

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

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

Connecting to Redshift Data

Connecting to Redshift 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 to Redshift, set the following:

  • Server: Set this to the host name or IP address of the cluster hosting the Database you want to connect to.
  • Port: Set this to the port of the cluster.
  • Database: Set this to the name of the database. Or, leave this blank to use the default database of the authenticated user.
  • User: Set this to the username you want to use to authenticate to the Server.
  • Password: Set this to the password you want to use to authenticate to the Server.

You can obtain the Server and Port values in the AWS Management Console:

  1. Open the Amazon Redshift console (http://console.aws.amazon.com/redshift).
  2. On the Clusters page, click the name of the cluster.
  3. On the Configuration tab for the cluster, copy the cluster URL from the connection strings displayed.

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

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

cnxn = mod.connect("User=admin;Password=admin;Database=dev;Server=examplecluster.my.us-west-2.redshift.amazonaws.com;Port=5439;")

Create a SQL Statement to Query Redshift

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

sql = "SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'"

Extract, Transform, and Load the Redshift Data

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

Loading Redshift Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Redshift

table1 = [ ['ShipName','ShipCity'], ['NewShipName1','NewShipCity1'], ['NewShipName2','NewShipCity2'], ['NewShipName3','NewShipCity3'] ]

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

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

cnxn = mod.connect("User=admin;Password=admin;Database=dev;Server=examplecluster.my.us-west-2.redshift.amazonaws.com;Port=5439;")

sql = "SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ShipName','ShipCity'], ['NewShipName1','NewShipCity1'], ['NewShipName2','NewShipCity2'], ['NewShipName3','NewShipCity3'] ]

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