KNIME advances the democratization of data science by collaborating with Snowflake

KNIME, which offers a no-code/low-code toolset that – so to speak – fits square data sets into round memory holes, announced a new partnership with cloud data lake provider Snowflake to democratize access to data analytics across business line roles within a company.

With KNIME, Snowflake customers can now use any data stored in a Snowflake node—structured or unstructured—to build predictive machine learning models in a low-code/no-code environment, Paul Treichler, VP of Global Partnerships at KNIME, VentureBeat said. Business users can access Snowflake data and build models without writing code. But data scientists and advanced users have the flexibility to also code in Python, R, or JavaScript if they choose through built-in integrations with KNIME, Treichler said.

This is important news for users of Snowflake’s Data Cloud, which has more than 6,300 enterprise customers, including 506 of the Forbes Global 2000, and continues to grow rapidly.

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

Data centralization a big trend

As data warehouses and data lakes become more common in the current trend for organizations to centralize their data stores, there is a growing opportunity for organizations to unlock the value that was hidden in their data. However, not every team within an organization has developers or data scientists to unlock this value. Visual programming or low/no-code tools like KNIME help bridge that gap and democratize access and use of the data locked in giant platforms like Snowflake, Treichler told VentureBeat.

The KNIME Analytics Platform is built on open source code and used by 250,000 community members and 4,000 commercial organizations in 60 countries. It connects Snowflake to thousands of other features, and it’s free to use. The same features would require multiple tools elsewhere or come at a cost of thousands of dollars per user, Treichler said. KNIME’s business model is based on enterprise services that it sells on top of the main software platform.

Data creates business value

Understanding data is critical to creating business value. With the global data analytics market worth more than $200 billion, there is a need for as many people as possible, across all functions, departments and industries, to have access to analytics in their daily work to increase overall productivity, said Treichler.

“Many of our customers rely on Snowflake to power virtually any data workload at scale and use KNIME to derive value from that data,” said Treichler.

How do users facilitate the integration of these two tools?

“In the KNIME language, we have a ‘node,’ which is a single piece of functionality that can be dragged and dropped into a data process,” Treichler said. “This node contains the connectors between KNIME and Snowflake. This means you don’t have to code any connections and you can instantly access everything you have in Snowflake in a visual environment. You can explore your data, prepare and merge it, use it to train a machine learning model, or create a web service, visualization, or data app that non-technical team members can contribute to or consume.”

KNIME product manager Tobias Kötter wrote a blog post on Medium discussing these processes.

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

KNIME is flexible and extensible, giving data professionals the freedom to work in their preferred environment. Users can create sophisticated analysis models in its low-code/no-code environment or write custom algorithms in a language of their choice with built-in integrations with R, Python, Java and others, Treichler said.

Headquartered in Zurich with offices in Austin, Texas and Berlin, KNIME competes in a market that includes Alteryx, RapidMiner, Orange, R Studio, SAS and SPSS.