What's New in KNIME Analytics Platform 3.2, KNIME Server 4.3 and KNIME Big Data Extensions 1.6

This year's summer release, July 8, 2016, involves a major KNIME® Software update. Here, we have highlighted some of the major changes, new features, and usability improvements in both the open source KNIME Analytics Platform and the commercial KNIME products.

You can upgrade from your existing KNIME Analytics Platform 3.1 version by choosing the Update option in the File menu or downloading from scratch from the download page.

UI and Workbench

Analytics and ETL

Integration and Utility Nodes

KNIME Big Data Connectors and KNIME Spark Executor

KNIME in the Cloud and KNIME Server

See the full list of changes in the changelog and check out the video on YouTube showing all the new features in these releases.

UI and Workbench

KNIME File Extension


We now have dedicated file extensions for Workflows and Workflow groups.

  • *.knwf for KNIME Workflow Files
  • *.knar for KNIME Archive Files

Once you have registered these file extensions you can open them by double-click, which directly launches your KNIME Analytics Platform. You can now also Drag&Drop the files to the KNIME Explorer to import them into your repository.


Missing Node installation


It is no longer necessary to search for the correct plugin for a node yourself. The new version provides an automatic Missing Node Installation. Now, when you open the workflow containing missing nodes, the dialog (as shown here) tells you which plugin is missing and, if you then select it, will directly search in your active update sites for the respective plugin.


Wrapped Nodes Preview


Workflow Coach


The Workflow Coach is our brand new node recommendation engine. Based on our communities' usage statistics we can now give you hints as to which node to use next in your workflow. When applied together with our Personal Productivity Extension (which requires a purchased license) you can even use this based on your own workflow. And for our KNIME Server customers – you can also make those statistics available to your users individually.


Wrapped Metanodes


A small change with high impact: Open the context menu for a set of selected nodes and have them directly encapsulated as a Wrapped Metanode. This saves two steps when designing workflows intended for use in the KNIME WebPortal or the Streaming Executor.


Wrapped Metanode Action



We have further improved our Simple Streaming Executor and converted more nodes to be able to support the new streaming API – including popular nodes such as String Manipulation and all of the Rule Engine nodes.
What is more, the Streaming Executor is now also able to process nodes that require additional handling such as extra scans over the data before main execution. You can find more information about the Streaming
Executor on the KNIME Blog.



Analytics and ETL

Feature Selection


The new Feature Selection nodes can be used to identify important features and reduce the dimensionality of your data. The supported selection strategies are forward selection and backward elimination. You can also specify your own score variable to optimize, which provides greater flexibility. Alongside the nodes there are two preconfigured meta nodes, one for each of the selection strategies.


Ensembles of Trees and Gradient Boosted Trees


Gradient Boosted Trees – Alongside Random Forests, the new KNIME Analytics Platform includes nodes for Gradient Boosted Trees, which are a specialized version of Gradient Boosting. They are considered to be among the state-of-the-art solutions for classification and regression problems. This new set of nodes includes nodes for learning and predicting both classification and regression problems.


Enhancements for Random Forests and related nodes – A number of enhancements has been added to the Random Forest related nodes (Tree Ensemble, Gradient Boosted Trees, Simple Regression Tree and Random Forest). Most notable is the optimization of the tree building algorithm, which now enables Random Forests to learn much faster on large data sets. We have also added binary splits, which allow for more interaction between the different features in a data set and oftentimes provide better generalization properties.
Furthermore all nodes in the Decision Tree Ensemble category, as well as the Simple Regression Tree, are now able to handle missing values in the data, which was not possible before.
One last enhancement involves the translation of Random Forest regression models as well as Simple Regression Trees to PMML.


Deep Learning


Deep Learning – The KNIME Deeplearning4J Integration in KNIME Labs (developed with the Deeplearning4J library) extends KNIME Analytics Platform providing the functionality to use deep neural networks. The extension consists of a set of new nodes, which enable you to modularly assemble a deep neural network architecture, train the network on data, and then use the trained network for predictions.

Neural Word Embeddings – In addition to deep learning, the KNIME Deeplerning4J integration contains nodes to learn word embeddings from words and documents. This is accomplished using a Word Vector Learner Node. This can create meaningful numerical representations of text words that can be used in many applications.


PMML Transformation Applier


PMML is considered a standard for describing predictive models and KNIME is an excellent toolbox to create these models. The "PMML Transformation Applier" is a new node that applies all of the preprocessing operations, described in a PMML document onto a data stream. This can include normalization, replacement, discretization and many other operations.


PMML Transformation Applier

Integration and Utility Nodes

REST Service Client Nodes


Part of KNIME Labs and following up on the famous KREST community extension, KNIME Analytics Platform now provides a set of REST client nodes to integrate RESTful web services. These nodes provide a rich configuration to reach out to different services and are tightly integrated in KNIME's JSON and XML processing capabilities.


Tableau Integration​


KNIME Tableau integration is now available as part of KNIME Labs. Tableau is a powerful business intelligence solution to build highly interactive and powerful dashboards. The integration comprises two new nodes:
Tableau Writer (TDE) writes a KNIME table as a Tableau extract (TDE); and Send to Tableau Server exports the table directly to a Tableau server for further processing. Special thanks to Tableau for their assistance during development and Forest Grove Technology, our partner in Australia, for valuable feedback and testing.


Semantic Web


Have you ever wondered how Google answers queries like “When was Albert Einstein born?” These queries are powered by the Semantic Web, the Google Knowledge Graph. The new Semantic Web nodes in KNIME Labs enable access to these semantic resources, e.g DBpedia or CHEMBL, from within KNIME.


JavaScript Views


The JavaScript Table View has been rewritten to provide greater functionality and interactivity. Most notably the table offers paging, so that larger datasets can also be processed and displayed; sorting, selection, and filtering are supported too.


Parameterized Database Query


The Parameterized Database Query node allows you to parameterize a SQL query using input column values. The node also provides several options to retain the input values and comes with an improved SQL editor with syntax highlighting and edit history.


H2 Database Connector


H2 is a high-performing file-based relational database. The new H2 Connector together with the wealth of existing KNIME database nodes means you can now connect, query, and manage an existing database or create your own H2 database.


KNIME Big Data Connectors and KNIME Spark Executor
(commercial extensions)

KNIME Big Data Connectors


KNIME Big Data Connectors have been extended by two new remote file handling nodes that can access files in HDFS. The webHDFS Connection node can connect to HDFS via the webHDFS protocol, which requires network access to the HDFS NameNode and all DataNodes. The httpFS Connection node can access HDFS files via an httpFS gateway, that is installed on a cluster edge node as a single point of entry.


Multiple Apache Spark Versions Supported


KNIME Spark Executor v1.6 now allows you to run workflows with Spark nodes on clusters with Spark 1.5 and 1.6. This includes support for Hortonworks HDP (2.2, 2.3.0, 2.3.4, 2.4) and Cloudera CDH (5.3 - 5.7). You no longer need to choose the Spark version to connect to during installation, instead, a revised preference page makes it easy to switch between Spark versions.



KNIME in the Cloud and KNIME Server
(commercial extension)

KNIME Cloud Analytics Platform (on Microsoft Azure)


Get started quickly – find KNIME Analytics Platform on the Azure marketplace and launch a new machine in a matter of minutes.

Bring your analytics to cloud hosted data – KNIME Cloud Analytics Platform can be in the same data center as your data - so no more waiting when you transfer data from the cloud to your local machine.

Scale your Analytics – Azure offers machines with up to 32 cores and 448 GB main memory, far more than your laptop! Simply launch the machine suitable for your workload, and when you’re done shut it down to keep your costs under control.

See KNIME Cloud Analytics Platform in action.


KNIME Server License


KNIME Server can be configured to manage license files for enhanced Analytics Platform functionality such as the KNIME Personal Productivity package (providing Workflow Diff and LocalSpace repository) or the KNIME Big Data Extensions. This gets new users started with KNIME Server much faster and more conveniently.


KNIME Server Admin Portal


The administration pages for KNIME Server can be accessed via the KNIME WebPortal, allowing the admin user to check the Server status, see running jobs, and configure users and groups.


KNIME Server Installer


The new KNIME Server installer guides the administrator through the setup process, allowing access to advanced configuration settings where required. The installer runs on both Windows and Linux.


KNIME Server Rest API


The KNIME Server REST API has been further extended to allow upload/download of files and workflows. The following blog posts describe the REST API and its use.


Many other improvements have been made under the hood – please refer to the changelog.


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