Featuring Geospatial Analytics, Further Enhancements to New UX/UI, and Tighter Python Integration
At KNIME our aim is to build an open source platform that provides analytical sophistication for all. In KNIME Software, this means continuously extending the range of available analytics techniques, as well as continuing to make the platform intuitive and easy to use.
This article highlights three of the biggest features of the v4.7 release. The new Geospatial Analytics extension allows easy, efficient, and replicable geospatial analysis without Geographic Information Systems expertise or programming skills. Enhancements to the UI/UX further ease the adoption of advanced analytics by individuals from all disciplines. Developments in the KNIME-Python Integration improve the experience of using both KNIME and Python.
Geospatial extension opens up spatial analytics to non-experts
More and more organizations are using geospatial analysis to improve operations from supply chain logistics, to deployment of equipment in emergency management, to location-based fraud detection, and more. Previously, this type of analysis has required niche expertise and coding skills. Harvard’s Center for Geographic Analysis and KNIME have collaborated to open up spatial analytics to non-expert users. The new Geospatial Analytics Extension for KNIME enables users to access, blend, and analyze geospatial data without the need to code.
This first version of this extension supports the most common vector data types such as points, lines, polygons, and collections of those. Users can download example workflows using the new Geospatial Extension in Harvard’s Geospatial Analytics Examples space from the KNIME Community Hub.
Enhancements to modern UI/UX ease onboarding of new users
Updates to the new user interface, which you can explore in the KNIME Modern UI Preview extension, further improve onboarding and usability, with many enhancements to the modern UI/UX developed based on feedback provided by the KNIME community.
For instance, the new UI now includes recommendations on what steps to take next and in what order, so users can build solutions even faster, a new table view enables users to browse and scroll large amounts of data, and much more.
Tighter Python integration improves experience for Python scripters & Python extension developers
KNIME continues to improve the experience of using both Python and KNIME by moving a number of features into the production-ready KNIME-Python Integration.
The most notable features are:
Data transfer is faster, with Python now just as fast in KNIME as it is anywhere else,
Improvements to the Python API make it easier to process large amounts of data, and
The KNIME-Python Integration now includes bundled Python environments, enabling users to start scripting without having to install or configure Python packages.
Development of custom functionality in pure Python is also now easier with more elegant management of dependencies and versioning support. Python developers can preview these improvements in the KNIME Python Extension Development (Labs).
Additional Python improvements include the ability to produce a new interactive Python visualization. The new node integrates with other visualizations and enables users to build custom, interactive views – using Python – and deploy them as browser-based data apps to other business users across the enterprise.
Read more about new visualization nodes, more efficient connectivity and file handling, and more, in What’s New in KNIME Analytics Platform 4.7.