What's New in KNIME Analytics Platform 4.4 and KNIME Server 4.13

• The Snowflake extension contains the Snowflake Connector node that comes with the JDBC driver. 

• Together with the existing database extensions, users can connect to their Snowflake instance and visually assemble database statements that get executed in the Snowflake database. 

• To upload massive amounts of data, you can use the DB Loader node that uploads the data to a Snowflake stage and loads the data into an existing table via the copy command.

• Example Workflow on KNIME Hub: Load Data into Snowflake

• Having the option to specify a number of retries ensures that the job still gets executed even though the original run failed

• Workflows that contain R nodes are now more portable since you can easily recreate the Conda environment used on the source machine (for example your personal computer) on the target machine (for example a KNIME Server instance).

• The addition of new connectors: The Azure Data Lake Gen 2 Connector together with the SMB Connector are the latest additions to the file handling framework. They allow you to manage, read and write your files in these file systems.

  • Manage files on Azure Data Lake Gen 2 and SMB drives such as SAMBA with the file handling nodes.

  • Read and write text, Excel and other files directly from these file systems.

  • Example Workflow on KNIME Hub: Operations on Remote File System Using SMB Connector

• The rewriting of more nodes to the new file handling framework: The new file handling framework provides a consistent user experience when managing your files across file systems whether locally, on the KNIME Server, in your data center or the cloud. For example:

  • File Reader

  • File Reader (Complex Format)

  • JSON Reader

  • Several table-based utility nodes such as

    • Transfer Files

    • Delete Files

    • Compress Files

  • Table Reader

  • Table Writer

  • Support for Path cells in the Column Expressions node

• H2O AutoML automatically optimizes the hyperparameters of different H2O models.

• Furthermore, Stacked Ensemble models are created using the trained models. H2O automatically selects and returns the best model.

• A detailed documentation of the process is provided by H2O.

• Example Workflows on KNIME Hub

  • H2O AutoML for Regression

  • H2O AutoML on Spark

• The Indexing and Searching extension allows you to create an index from your KNIME data table including complex data types such as text documents.

• Once the index is created it can be queried using advanced searching techniques such as fuzzy, range and proximity queries to find matches within milliseconds.

• With the new version you can now store the index in any supported file system, use it across multiple workflows and incrementally update an existing index.

• There are many errors that might occur: client-side errors, server-side errors or rate-limiting conditions. All of these can be individually configured, including the number of retry attempts or invocation pausing.

• Nodes have an additional tab in their configuration dialog to specify the node’s behavior in case of error.

The latest updates to variable scopes allow for the propagation of such variables, including feedback of variables in loop iterations. What previously was only possible using “Recursive Loops” is now a built-in “language feature”.

See these features in action in these example workflows on KNIME Hub:

• Modified Variables Propagation in GET Request Nested Loop

• Modified Variables Propagation in GET Request Loop

• Modified Variables Propagation in GET Request Captured in Try Catch

• Joiner Node: One of the most frequently used nodes is out of labs. You can now specify alternative matching criteria (“Match Any”) and define a default choice for the node.

• Integrated Deployment Nodes: The nodes that capture and write workflows for continuous and Integrated Deployment have been moved out of labs. New nodes, including the Workflow Reader and the Workflow Summary Extractor have been added.

• All MongoDB Nodes: MongoDB Reader, Remove, Save, Update and Writer nodes are out of labs. These now support the latest MongoDB version, include a new connector node and improved usability.

• All PowerBI Nodes: Note that the authentication with Microsoft has been moved out of the node itself. Use the “Microsoft Authentication” node. The “Send To Power BI” node now has the option to define relationships between columns of tables.