Ready to get started?

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

Download Now

Extract, Transform, and Load DataRobot Data in Python

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

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

Connecting to DataRobot Data

Connecting to DataRobot 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 DataRobot, the following connection properties are required: User, Password, and PredictionInstance. DataRobotKey may also be required depending on your type of DataRobot predictions instance. If using the Predictions API, DataFile is required. The CSV DataFile should include a header row as the first row of the datafile. APIKey is not required, but can be supplied. If not supplied, the driver will handle obtaining an APIKey.

User, DataRobotKey, and APIKey are the credentials for the DataRobot account.

ProjectID, DataFile, and ModelId are the parameters for the project, dataset, and model type.

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

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

cnxn = mod.connect("PredictionInstance=myinstance.orm.datarobot.com;DataFile=PATH\TO\input_file.csv;DataRobotKey=123-abc-456-def;User=username;Password=password;")

Create a SQL Statement to Query DataRobot

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

sql = "SELECT Id, Prediction1Value FROM Predictions WHERE Id = '1'"

Extract, Transform, and Load the DataRobot Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the DataRobot data. In this example, we extract DataRobot data, sort the data by the Prediction1Value column, and load the data into a CSV file.

Loading DataRobot Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Prediction1Value')

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

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

cnxn = mod.connect("PredictionInstance=myinstance.orm.datarobot.com;DataFile=PATH\TO\input_file.csv;DataRobotKey=123-abc-456-def;User=username;Password=password;")

sql = "SELECT Id, Prediction1Value FROM Predictions WHERE Id = '1'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Prediction1Value')

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