KNIME upgrades data science democratization through Snowflake collaboration

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KNIME, which offers a no-code / low-code toolset that – literally – enables square data sets to fit into round storage holes, announced a new partnership with cloud data lac provider Snowflake, designed to democratize access to data analytics. Is. Line-of-business roles within the enterprise.

Using KNIME, Snowflake customers can now use all the data stored in the Snowflake node – whether structured or unstructured – to create predictive machine-learning models in a low-code / no-code environment, said Paul Trechler, VP Said. Business users can access Snowflake data and create models without writing any code; However, data scientists and advanced users have the flexibility to code in Python, R or even JavaScript with the built-in integration in KNIME if they choose, Trechler said.

This is significant news for users of Snowflake’s data cloud, which has more than 6,300 enterprise customers, including 506 from Forbes Global 2000, and is growing rapidly.

“Snowflake seems to be moving the table lately,” ALTR CEO Dave Sikora told VentureBeat in a separate interview.

Data centralization is the main trend

As data warehouses and data leaks become increasingly common in the current trend of centralizing their data stores for enterprises, opportunities for organizations to use the value hidden in their data are increasing. However, not every team in the enterprise has developers or data scientists to unlock this value. Visual programming or low- / no-code tools such as KNIME help fill this gap and democratize access and use of locked data across huge platforms like Snowflake, Trechler told VentureBeat.

The KNIME Analytics platform is based on open-source code and is used by 250,000 community members and 4,000 professional organizations in 60 countries. It combines Snowflake with thousands of other capabilities, and there is no cost to use it. Elsewhere the same capabilities will require multiple tools or cost thousands of dollars per user, Trechler said. KNIME’s business model relies on enterprise services that it sells on and off major software platforms.

Data creates business value

Understanding data is important to create business value. With a global data analytics market valued at over 200 200 billion, overall productivity requires that as many people in roles, departments and industries as possible have access to analytics in their daily jobs, Trechler said.

“Many of our customers rely on Snowflake to power virtually any data workload on the scale, while they use KNIME to derive value from the data,” Trechler said.

How do users simplify the integration of these two tools?

“At KNIME Speak, we have a ‘node’, an individual piece of functionality that can be dragged and dropped in the data process,” Trechler said. “This node has connectors between KNIME and Snowflake. This means you don’t have to code any connections, and you’re instantly able to access whatever you have in Snowflake in the visual environment. You can explore your data, prepare and mix your data, use it to train machine learning models, or contribute or use web services, visualizations or data for less technical team members. Can create an application. ”

KNIME Product Manager Tobias Cotter has written a blogpost on the medium that discusses these processes.

“There is a strong global ecosystem of consulting and implementation partners who, with both KNIME and Snowflake, have domain knowledge, local capabilities and technical capabilities to help teams leverage the full potential of these tools in a broader enterprise context,” Trechler said. .

KNIME is flexible and extensible, giving data experts the freedom to work in their preferred environment. Users can create state-of-the-art analytical models in its low-code / no-code environment or create script custom algorithms in the language of their choice with built-in integration with R, Python, Java and others, Trechler said.

Headquartered in Zurich, with offices in KNIME, Austin, Texas and Berlin, it competes in markets including Alteryx, RapidMiner, Orange, R Studio, SAS and SPSS.

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