KNIME Release Data Talks

KNIME users can build data apps as frontends for their workflows, allowing anyone in the organization to run and interact with their models. Now, you, as the app creator can build in event-based execution, so a specific action in the data app’s UI prompts the workflow to rerun.

Instead of connecting workflows via standardized but limiting JSON-based APIs, you can now connect directly using KNIME-native API endpoints. This means you can share many more KNIME data types: a trained model, an image, or text document, for example. This makes collaboration much more convenient.

Python is now just as fast inside KNIME as it is anywhere else. The new Python Script (Labs) node features a new backend that enables performance improvements of up to 50x compared to previous versions. It includes a new API with two ways to access the data coming from KNIME: PyArrow and Pandas DataFrame. This video shows an example of data normalization and compares the speed of the old and new Python Integration.

Explore and discover new nodes, components, and workflows more easily on the KNIME Hub. Now, filter your search with tags, instantly search previous items, and find your way to related resources in the new sidebar.

Soon KNIME Analytics Platform will have a new look! Get a sneak peek of the new design, rebuilt based on customer feedback, featuring a more intuitive way of getting around the application. Note that this video shows only a preview, this UX is not yet publicly available.

Data and IT teams can now apply models “at the edge” - minimizing latency, enabling cost savings, and deployment of inference services in multiple geographic regions. This video shows a practical example of building a fraud detection model, capturing the parts of the model to be used as the inference service, and deploying that service via KNIME Edge.

See a demo of two new features for data wranglers. The new Excel Cell Updater node allows you to update a cell while maintaining the formatting. The new Local File System Connector provides a connection to a local file system and enables you to create complex file handling workflows with dummy data locally before connecting to the final file system; the Generic S3 Connector enables you to connect to any S3 compatible system.

Find out how Single Sign-on works in KNIME Analytics Platform. The demo takes you through the simple process of connecting to PostgreSQL and Microsoft SQL Servers, with a Kerberos ticket as your authentication method. See how, when you execute the workflow, KNIME takes care of logging you into the databases.

Watch a demo highlighting the new nodes in the MongoDB integration. We look at the MongoDB Aggregation node, which enables you to perform aggregation operations within MongoDB. This means the raw data stays in MongoDB and you only extract what you need for further processing in KNIME. See how to execute MongoDB commands from within KNIME, for example, to manage users or database storage or retrieve information.