Version 3.4 of the Knime Analytics Platform, the open-source data science workflow toolbox, was released back in July. With that release came new integrations with Azure and Microsoft R Server, which were highlighted in a recent blog post. With Knime 3.4, you can use Microsoft R Server packages in an R node, and connect to data services in Azure.
Knime 3.4 includes a new example: predicting departure delays from the Airlines data set. To run this example, you'll need to configure Knime to make Microsoft R Client or Microsoft R Server the default R engine used by Knime. With that change made, you can access the built-in airlines data set, and used the rxLogit function in the RevoScaleR library to fit a logistic regression model, even though the data is larger than could normally be accommodated by R. You can see the process demonstrated in the video below.
This update also provides several example workflows for accessing data in various Azure services including:
- SQL Server on Azure, with in-database processing
- Azure Blob Storage
- Hive on HDInsight
- Spark on HDInsight
Knime users can find these workflows in the Knime Examples Server, and you can find further details at the link below.
Knime blog: A Touch of Azure in Your KNIME Workflow