Got Snowflake? Here’s Where KNIME Comes In

For many, Snowflake is the foundation of their modern data architecture.

As Snowflake adoption expands, a familiar pattern emerges: more people want to work with the data than can realistically write SQL or build custom scripts. Analysts, domain experts, and business teams all want to explore data, and automate analyses. That’s where KNIME comes in.

KNIME provides a visual analytics layer that complements Snowflake and helps make Snowflake data more accessible, reusable, and operational across a wider range of users.

“KNIME offers Snowflake users an intuitive way to analyze and work with their data via no-code/low-code visual workflows.” Snowflake on KNIME.

Snowflake is designed around SQL, which works extremely well for engineers and advanced analysts. But not everyone who needs data access wants, or has time, to write queries.

KNIME removes this barrier by opening up Snowflake data to a wider audience through native Snowflake connectors and visual workflows that don’t require SQL. Analysts and business users can explore, prepare, and analyze Snowflake data independently, while Snowflake continues to serve as the governed, single source of truth.

KNIME AI Assistant (K-AI) makes this even more approachable. Anyone can interact with Snowflake data using conversational prompts to K-AI to ask questions, generate workflow steps, and understand what each step does directly within KNIME. Over time, this means more people across the organization can engage with Snowflake data confidently, without putting extra pressure on the data engineering team.

Access to data alone doesn’t guarantee insight. Snowflake users often need additional tools for exploration and analysis. KNIME keeps the entire journey in one place and helps you move from raw data to real answers very quickly without exporting files or switching tools.

Watch Data-as-a-Service with Snowflake and KNIME and learn how SIEMENS Healthineers provide CRM data to a large number of stakeholders.

Tips for build out your analytics even faster:

• Instead of starting from a blank canvas, use KNIME’s AI Assistant, K-AI, to suggest analytical approaches and build workflows to work with Snowflake data for you.
• Get clear explanations of any workflow from K-AI. When you’re collaborating with colleagues, this helps you quickly understand what a workflow does and makes it easier to adapt and reuse.

You can also go a step further and build machine learning models and even orchestrate AI agents on Snowflake.

Learn more about building AI agents with KNIME in this video:

In most organizations, data teams are made up of people with very different technical backgrounds or preferences.

KNIME allows SQL-based and visual approaches to coexist in the same workflow. Experienced users can write SQL where it makes sense, while others use visual nodes to filter, join, and transform data. This flexibility makes collaboration easier and reduces bottlenecks between teams..

Watch Snowflake + KNIME: How Business Users Can Adopt the Cloud

Snowflake data often needs to be enriched with information from other systems, such as cloud storage, APIs, operational databases, or real-time services.

KNIME makes this easy by allowing users to blend Snowflake data with 100+ sources, using built-in connectors as well as Python and R integrations, or via REST APIs.

This is especially important for AI-driven use cases, for example, as it ensures AI agents have access to all the data they need.

Tip: You can agentify REST APIs with KNIME’s agentic framework. Meet Agent Hubert!

Because KNIME is open source at its core, teams can quickly adopt emerging technologies and frameworks as they evolve, integrating new tools into Snowflake-centric workflows without delay.

Many Snowflake users start with ad-hoc queries and analyses that work well initially but can be hard to scale or reuse.

KNIME workflows turn those one-off analyses into reusable, automated processes. Data preparation, validation, and reporting logic can be shared across teams and scheduled to run reliably, helping teams maintain consistency as usage grows.

Read How to Connect to Snowflake and Automate Sales Reporting with KNIME.

The foundation of AI is data quality. As more data sources flow into Snowflake, ensuring consistent data quality becomes increasingly important.

KNIME is often used alongside Snowflake to handle data processing as part of automated workflows. By formalizing these steps, teams can improve trust in their data and ensure a solid foundation for AI projects, while keeping Snowflake as the governed, centralized data platform.

Watch Fabian Jogschies from Snowflake discuss how a Snowflake and KNIME Stack Modernizes Data for the Enterprise AI Flywheel.

As more teams rely on Snowflake as their central data platform, new needs naturally arise around accessibility, analytics, automation, and reuse.

KNIME addresses those needs by acting as a complementary analytics layer on top of Snowflake. Together, they allow organizations to scale not just data infrastructure, but data usage, enabling more people to turn Snowflake data into reliable, repeatable insights.

In that sense, KNIME helps more people benefit from Snowflake.