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KNIME for Snowflake Users

SnowflakeDBDatabaseETLELT

Enable anyone in the organization - whether data experts, data analysts, business experts, scripters or non-scripters - to access and make sense of data in Snowflake, and virtually anywhere else, with KNIME’s low-code/no-code capabilities and dedicated nodes such as the Snowflake Connector node.

KNIME Snowflake Extension Guide
This guide describes how to work with Snowflake from within KNIME Analytics Platform.
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Getting Started with Snowflake Workflows

Utilizing Snowflake data within KNIME requires a connection to a Snowflake Data Warehouse using the KNIME Database Framework. Once connected with the Snowflake Connector node, you will be able to use all KNIME DB nodes for visual query generation.
Workflow
Two ways to run a simple Snowflake query in KNIME
SnowflakeDB
This example shows two ways to execute a Snowflake query within KNIME.
Snowflake ExamplesBasic Snowflake Workflow
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Workflow
Extract and visualize Snowflake data in KNIME without coding
SnowflakeDBDatabase
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This workflow is an example of how to get started with using Snowflake from within KNIME Analytics Platform.
Examples40_Partners07_Snowflake00_Getting_started_with_Snowflake
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ETL vs. ELT with KNIME and Snowflake

KNIME makes it easy for you to get your data into the right form within Snowflake whether you want to do ETL or ELT. By using the KNIME DB extension, you can visually create database queries to push execution to the database or warehouse.You can design your workflow based on where you would like the execution of different pieces of the workflow to take place.
Workflow
Preparing and Exploring Customer Data with KNIME and Snowflake (ETL)
SnowflakeETLCustomer Intelligence
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An example of how to read and transform different data files within KNIME prior loading the manipluated data into Snowflake.
Examples40_Partners07_Snowflake01b_Snowflake_ETL
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Workflow
Preparing and Exploring Customer Data with KNIME and Snowflake (ELT)
SnowflakeELTCustomer Intelligence
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An example of how to read different data files in KNIME and upload it to Snowflake.
Examples40_Partners07_Snowflake01a_Snowflake_ELT
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In-Database Model Inferencing

Powered underneath by the Snowpark integration, you can now create a model within KNIME and push it to your Snowflake Data Warehouses. Once the model is placed in the warehouse, you can then perform the model inferencing steps at the data (Snowflake) layer.
Workflow
Training and Testing a Customer Churn Predictor with KNIME and Snowflake
SnowflakeCustomer IntelligenceCI
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This workflow is an example of how to train a machine learning model in KNIME with data sampled from Snowflake.
Examples40_Partners07_Snowflake02_Snowflake_Model_Learning
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Customer Churn Predictor Deployment with KNIME and Snowflake
SnowflakeCustomer IntelligenceCI
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This workflow is an example of how to deploy a customer churn predictor as Snowflake User-Defined Function.
Examples40_Partners07_Snowflake03_Snowflake_Model_Deployment
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Data Apps powered by KNIME and Snowflake

With KNIME Business Hub, a user has the ability to create and deploy workflows across the business that are powered by Snowflake and KNIME. You can schedule the workflow to run at specific times , deploy it as a REST endpoint, or create a visual data app. Below you can find an example of a workflow which uses Snowflake data to create a sales KPI dashboard. Take a look at the KNIME Data Apps Beginners Guide in the additional material section.
Workflow
Providing an Interactive Data App with KNIME and Snowflake
SnowflakeCustomer IntelligenceCI
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An example of how to turn Snowflake data into an interactive Data App that can be consumed by business users.
Examples40_Partners07_Snowflake04_Snowflake_Dynamic_Data_App
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