Knime adds new UI, tighter Snowflake bond to analytics suite
The vendor's new platform update is aimed at end users and features a revamped user interface, more Snowflake integration and a dedicated Python environment for writing scripts.
A modernized user interface, new environment for Python users and deeper integration with Snowflake highlight the latest Knime analytics platform release.
Knime, an open source analytics vendor founded in 2004 and based in Zurich, Switzerland, launched version 4.6 of its platform in a blog post on June 15. A second update this year is expected in the fall.
The update unveiled last week is targeted at end users, including the many self-service users who have begun using Knime's analytics tools over the past couple of years, according to Phil Winters, strategic advisor at Knime.
In the fall, the vendor is planning a release aimed at adding enterprise-scale capabilities.
"This particular release has to do with the analytic depth and breadth and usability for individuals," Winters said. "There are lots of things added for people at different levels. This release is mostly designed to help all the different types of end users move forward."
For example, new visualizations can be consumed by any user, while the environment for Python users and integration with cloud data vendor Snowflake are aimed at more sophisticated customers.
Underlying all Knime releases, meanwhile, is the vendor's open source approach, Winters continued.
"At the core is this concept of openness," he said. "Everything for the individual is open source, and that means they can benefit from the community and benefit quickly because we don't have to rewrite their code. The other thing, because we will never be able to do absolutely everything ourselves, we have to make it as easy as possible to mix and match."
Given the Open Source architecture of the Knime analytics platform, communication and connection between Knime and other systems is critical. To aid that communication and connection, the vendor uses nodes, which are essentially connectors that enable its users to easily write, read and move their work.
It's that network of nodes that makes Knime appealing to many organizations that operate multiple systems, according to Mike Leone, an analyst at Enterprise Strategy Group (ESG).
"Knime doesn't care what a customer's environment looks like," he said. "They recognize that customers have different needs and requirements based on their use of different tools in different environments. That level of flexibility is being embraced by organizations that have struggled to transform due to the sheer complexity of their massively distributed data environments."
A sample screenshot from Knime displays the analytics vendor's new user interface.
New capabilities
The first thing Knime users will soon see when using the vendor's analytics capabilities is a modernized user interface that is now in preview.
Knime's current opening screen -- the launch point for all Knime workflows -- is starting to look old, according to Winters. The new UI will be web-based, and in addition to a new look and feel, its node repository comes with stronger searching and filtering capabilities.
"Our interaction screen for building workflows was starting to look a little old in the tooth," Winters said. "It had fantastic capabilities, but it was not sparkling and modern."
Leone noted that the modernized UI will appeal particularly to Knime's growing number of self-service analytics users.
The vendor historically catered to data scientists, but recent updates have made its platform more accessible to business users as well, and the new UI continues that trend.
"While some of the traditional BI players are focused on expanding more into the data science space, Knime is and has been there," Leone said. "With Knime's new modern UI, they're working toward providing a similar visual experience as the traditional BI players but with a far more robust data science offering."
They recognize that customers have different needs and requirements based on their use of different tools in different environments. That level of flexibility is being embraced by organizations that have struggled to transform due to the sheer complexity of their massively distributed data environments.
Mike LeoneAnalyst, Enterprise Strategy Group
In addition to the modernized look, the web-based new UI completes the decoupling of Knime's front end from its back end and makes Knime a "headless" platform that enables users to compose their own analytics workflows.
Beyond the upgraded interface, users will see improved Python scripting capabilities and the ability to work with models directly in Snowflake.
Both of those features are now generally available.
Python is a popular programming language used by engineers and developers to build analytics capabilities. To better enable their ability to work with Python, the Knime Python Extension -- one of the vendor's nodes -- now contains its own Python environment so users can immediately start writing scripts without first having to install new software.
In addition, when users create their own nodes using Python, they can now more easily share their creations with the rest of the Knime community by dragging and dropping it into the Knime Hub where users can upload and share their analytics creations.
The aim of the new Python environment is to add ease of use for both end users and managers, making coding and sharing simple while centralizing it in a controlled setting.
"Having pure-Python Knime nodes will be incredibly valuable," Leone said. "Users no longer have to leave Knime to utilize and incorporate Python capabilities. It gives the technical folks more agility by leveraging Knime for all orchestration, while the business folks gain the control they need for improved sharing of Python code and collaboration."
The deeper integration with Snowflake also addresses ease of use.
Before the release of Knime 4.6, the vendor's users were able to build predictive models using data from Snowflake in concert with H20.ai's low-code/no-code capabilities, but to do so they had to move their data out of Snowflake and into Knime, develop their models in Knime, and then return their data to Snowflake for storage.
Now, through its enhanced integration with Snowflake, Knime users can develop and execute H20.ai machine learning models directly through Snowflake without having to move their data back and forth.
The result is added speed and flexibility.
More capabilities and future plans
In all, the latest Knime platform update includes more than 10 new analytics capabilities.
Beyond the new UI and enhancements aimed at making the use of Python and Snowflake simpler, the update comes with new visualization nodes for building and exploring data applications, enhancements to the Knime database framework that enables users to work with data where it resides by building SQL statements within Knime and easier interaction with Microsoft's Azure Synapse Analytics.
Knime's next update, due in fall 2022, will focus more on the enterprise than the end user, according to Winters. No specifics are yet available, but Winters did note that the Knime Hub will feature prominently.
The Knime Hub has grown significantly over the past couple of years with about 10,000 user-created analytics assets now available compared with about 2,500 in 2020.
"When people use these, they want to be able to use them within their companies," Winters said. "They want to be able to have teams sharing them and collaborating. So, what you're going to see coming from us in the fall are the first of our paid services so that people can start looking at all this."
Enterprise Strategy Group (ESG) is a division of TechTarget.
