Ready to get started?

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

Download Now

Extract, Transform, and Load HPCC Systems Data in Python

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

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

Connecting to HPCC Systems Data

Connecting to HPCC Systems 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, set the following connection properties: Set URL to the machine name or IP address of the server and the port the server is running on, for example, https://server:port. The User and Password are required to authenticate to the HPCC Systems cluster specified in the URL. Note that LDAP authentication is not currently supported by our ODBC driver.

Set Version to the WsSQL Web server version. Note that if you have not already done so, you will need to install the WsSQL service on the HPCC Systems server. The WsSQL Web service is used to interact with the underlying HPCC Systems platform.

Set Cluster to the target cluster.

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

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

cnxn = mod.connect("URL=http://127.0.0.1:8510;User=test;password=xA123456;Version=1;Cluster=hthor;")

Create a SQL Statement to Query HPCC Systems

Use SQL to create a statement for querying HPCC Systems. In this article, we read data from the hpcc::test::orders entity.

sql = "SELECT CustomerName, Price FROM hpcc::test::orders WHERE ShipCity = 'New York'"

Extract, Transform, and Load the HPCC Systems Data

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

Loading HPCC Systems Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

etl.tocsv(table2,'hpcc::test::orders_data.csv')

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

cnxn = mod.connect("URL=http://127.0.0.1:8510;User=test;password=xA123456;Version=1;Cluster=hthor;")

sql = "SELECT CustomerName, Price FROM hpcc::test::orders WHERE ShipCity = 'New York'"

table1 = etl.fromdb(cnxn,sql)

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

etl.tocsv(table2,'hpcc::test::orders_data.csv')