- Database and Big Data Extension
- Database GroupBy node now with database specific aggregation methods
- Drop Table (New Node)
- HP Vertica Connector (New Node)
- Impala Connector / Loader (New Nodes)
- HDFS Connector / File Permissions (New Nodes)
- Tool Integration
- New Python Integration (New Nodes)
- JSON Processing (New Category)
- Data Mining
- PMML: New nodes to implement modular PMML
- DBSCAN (New Node)
- kNN now supports more distance functions (New Node)
- Target Shuffling (New Node)
- IO
- Writer Nodes Improvements
- Other
- Quick Node Insertion with Ctrl-Space (New GUI feature)
- Table Validator (New Node)
- Column Auto-Type Cast (New Node)
- See full list of changes in changelog file
Full recording of webinar "What's new in KNIME 2.11" available at: http://youtu.be/9RkRHI32Dy8 Database and Big Data Extension | |
Database GroupByThe Database GroupBy node now offers database specific and parameterized aggregation methods. It also allows for dynamic aggregation column selection based on column name pattern or data type. | |
Drop TableThis node drops objects and tables in a database. It also provides a handful of options to gracefully handle missing tables. | |
HP Vertica ConnectorThis node connects to an HP Vertica database. The node outputs a connection to the selected HP Vertica database. |
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Impala Connector / Loader (Cloudera certified, commercial license required)The Impala Connector node connects to an Impala database. The Impala Loader node creates a new table in the Impala database. |
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HDFS Connector / File Permissions (commercial license required)The HDFS Connector node connects to the HDFS Hadoop Distributed File System. The HDFS File Permission node sets permissions for further operations on the files in HDFS.
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Tool Integration | |
New Python Integrationvideo http://youtu.be/3dHufC6iQgw This new Python integration is based on CPython (and not JPython as in the old nodes). It requires some modules in the Python installation:
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JSON Processingvideo http://youtu.be/XndgaTC3UWY Many new nodes to process JSON structures:
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Data Mining | |
PMML: New Nodes for Modular PMMLvideo http://youtu.be/yQP0NImUCes Three new nodes to implement modular PMML, that is to assemble transformations and models into a PMML structure piece by piece avoiding repetitions.
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DBSCANNew node to implement the DBSCAN (density based clustering) algorithm. The node has two input ports: one for the data and one for the distance formula. With the appropriate distance function, DBSCAN is able to cluster oddly shaped groups of data. | |
kNN now supports more distance functionsk Nearest Neighbors (Distance Function) implements kNN and supports more distance functions, besides the Euclidean function. The node provides an additional input port for the distance formula. | |
Target ShufflingThis node shuffles the values randomly inside a selected column to assess the statistical accuracy of data mining results. | |
IO | |
Writer Nodes Improvementsvideo http://youtu.be/wsL1UTzEg-0 Supported Output Locations
Configuration Dialogs
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Other | |
Quick Node Insertion with Ctrl-SpaceThere is now help to quickly find and insert one or more nodes in the workflow.
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Table ValidatorThe Table Validator node checks the input table format against missing values, out of domain values, and so on. using a reference data table to prepare data for reports and other workflows.
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Column Auto-Type CastThis new node tries to guess the most fitting type for a specified column. It is useful after a Transpose and before transforming a data cell into a flow variable. |
Many more small improvements have been made under the hood - please refer to the changelog file.