Alteryx to KNIME Migration FAQs

Choosing new data tools can be a daunting exercise for any team. But executives must stay vigilant to select the platforms that deliver positive ROI and align with their organization’s goals and priorities.

Here we answer some of your most commonly asked questions when migrating from Alteryx to KNIME.

• Onboarding and Ease of Use
  • Is KNIME a no-code or a low-code tool? 
  • Do you need to code more in KNIME vs Alteryx?
  • Is KNIME a tool for beginners or experts? 
  • Alteryx workflows typically require fewer nodes than comparable KNIME workflows. Does that mean you get less out of the box with KNIME? 
  • Can I inspect intermediate results or get an overview on how data is processed in a KNIME workflow? 
  • Can I pause execution of a workflow in KNIME?
  • Can I execute only parts of a workflow in KNIME?
  • What do the different shapes and colors of KNIME node inputs and outputs mean? 
• Workflow Flexibility
  • Can I control the order of execution in a KNIME workflow?
  • What are flow variables in KNIME?
  • Can I control which parts of a KNIME workflow execute? 
  • Does KNIME support macros? 
  • Can KNIME push down execution to databases?
  • Can I use Python and/or R within a KNIME workflow?
  • Can I work with Date & Time with time zones or more custom data types?
• Inputs and Outputs
  • Can I connect KNIME to cloud storages like SharePoint Online or Box? 
  • Can I connect KNIME to SAP?
  • Can I use KNIME to do web scraping?
  • Can I import data from custom sources? 
  • Can I build reports and export/send them as emails?
  • Can I export my KNIME workflow results to BI Tools like Tableau or Power BI? 
  • What are KNIME Data Apps?
• Tools and Nodes
  • Which KNIME nodes can I use instead of Alteryx Tools? 
  • Does KNIME do AutoML?
  • How can I do multi-row formulas in KNIME? 
  • How can I access previous rows in formulas in KNIME?
  • Can I use KNIME to execute any SQL statements?
• Workflow Documentation
  • Can I add comments in KNIME workflows? 
  • Can I organize the connections between KNIME nodes?
• Performance
  • How does KNIME execute workflows?
  • How can I set up KNIME for maximum performance? 
• Governance
  • Can I securely use credentials and passkeys with KNIME?
  • How do I centrally manage connections to databases or other data sources in KNIME?
  • Can I run KNIME in my own infrastructure?
  • How can I monitor what is going on on KNIME Business Hub? 
  • What governance features does KNIME Business Hub offer?
  • As an open source software, how is the quality of KNIME software maintained?
• KNIME Services
  • What’s the difference between KNIME Analytics Platform, KNIME Business Hub, and KNIME Community Hub?
  • Can I get KNIME training?
  • How can I migrate Alteryx flows to KNIME workflows? 
  • Can I migrate from Alteryx Scheduler? 
  • What does KNIME Business Hub offer?
  • Is there a KNIME-hosted (SaaS) offer of KNIME Hub?
  • As an open source software, how is support organized?

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It’s both. On the no-code side, in KNIME, individual tasks, such as reading/writing files, transforming data, training models, creating visualizations, etc., are represented by nodes that you drag and drop to build a flow of data. It offers more fine-grained control and flexibility by offering more distinct nodes to perform tasks.

On the low-code side, KNIME also offers powerful expressions/formulas, as well as integration of SQL, Python, R, and other programming languages. So if you want to code, you can.

Read more about KNIME’s low-code/no-code approach.

You can code as much or as little as you like in KNIME. It is possible to use KNIME entirely as a no-code tool. No coding or scripting experience is needed to create and run even advanced workflows.

KNIME offers the complete depth and breadth for advanced users, while still offering an easy on-ramp for those just getting started with data science.

As a beginner you can simply use parts of the available functionality and add more capabilities once you need them, just like you use only those tools in Alteryx that you actually need.

Experts benefit from the wide variety of nodes, and if there are no nodes for solving their specific problem, they can also integrate code.

KNIME nodes are more basic building blocks to assemble your data flow: Each node performs a distinct task. So while you might need a couple more nodes to build your workflow, it’s easier to follow what is happening to the data as it flows through the workflow.

If you have a set of nodes you always use to perform the same steps, for example, collecting data from multiple sources, you can wrap them into so-called components and re-use them in other workflows and even share them with colleagues on KNIME Business Hub. Building and sharing components for custom business logic increases consistency across workflows.

There’s often a dedicated node in KNIME for problems that would be solved by using code in a Formula tool within Alteryx. The more granular nodes in a KNIME workflow provide more explicit documentation of automated processes.

See how most used tools in Alteryx map to KNIME nodes in the Alteryx to KNIME cheat sheet.

Yes, KNIME executes workflows step-by-step. You can look at each executed node’s outputs, including intermediate nodes. You also can use these outputs to build workflows one node at a time, without the need to repeatedly execute upstream nodes.

Yes, KNIME executes nodes step-by-step. You can cancel the execution of nodes. Upstream nodes that finish execution retain their outputs.

Yes, you can select any nodes you want to execute and KNIME will only execute those nodes and all nodes that provide inputs to the selected nodes.

KNIME goes beyond data pipelining and employs a visual programming paradigm.

• Black triangles designate data tables as common across data pipelining tools.
• Flow variable connections, shown by red circles, offer much more flexibility. For example, they enable to control which parts of a workflow are executed at runtime.
• Colored squares indicate that nodes work on other kinds of data, e.g., an image, a data science model, or even a workflow.

KNIME nodes also follow a color coding:

• Orange nodes are data input nodes
• Yellow nodes are data function nodes
• Blue nodes are data visualization nodes
• Red nodes are data export/reporting nodes

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Yes, next to normal ports, you can use the flow variable ports of nodes to control the order they are executed in.

Flow variables are parameters with string, integer, double, arrays, or file-path values. These parameters can be used to avoid manually changing settings within the nodes of a workflow when a new execution with different settings is required.

As the name suggests, flow variables flow through the workflow alongside tables. However, flow variables can also be transmitted via explicit connections using the red circle ports. Flow variables are available only for the downstream nodes in the workflow. 

Flow variables can be defined from the value of a table cell or user input in a data app. Then, they can be used in calculations and to set node configurations automatically during the execution of a workflow.

Yes, you can use the Workflow Control nodes for this. For example, use IF/CASE Switch nodes controlled by flow variables, or the Empty Table Switch node to detour at runtime.

KNIME features components as a means to encapsulate and share custom business logic. For other use cases you do not need macros in KNIME, instead you can execute parts of a workflow multiple times transparently by employing loop nodes.

Yes, you can use KNIME’s “DB” nodes to perform tasks on databases. There are nodes to connect to various kinds of databases, including Microsoft SQL Server Connector, MySQL Connector, PostgreSQL Connector, Oracle Connector, Snowflake Connector, Google BigQuery Connector, Amazon Athena Connector, Create Databricks Environment, as well as a generic DB Connector to connect to custom databases.

Yes, there are nodes for embedding Python Scripts and Views in workflows, as well as R Snippet and R View (Table) respectively. The Python nodes offer a modern editor with AI integration. Furthermore, it is possible to build custom Python node extensions featuring full KNIME nodes.

Yes, KNIME supports various data types, including numbers, text, dates, images, geospatial, lists, JSON, XML, and molecule structures.

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Yes, there are nodes connecting to SharePoint, Box, Amazon S3, and more. Check out this overview of different connectors in the Connectors with KNIME Analytics Platform cheat sheet.

KNIME can directly work with SAP HANA using the DB Connector node and the database framework. However, this requires the appropriate license from SAP which allows JDBC based connections to SAP HANA. Two KNIME partners offer commercial alternatives that do not require a specific SAP license and that are compatible with other SAP products besides SAP HANA:

• SAP Reader (Theobald) requires the commercial software Xtract Universal from Theobald Software which supports extractions from various SAP ERP, SAP BW and S/4HANA environments into KNIME.
• SAP(KCS) Nodes extension requires the commercial KNIME Connector for SAP from DVW which supports extractions from various SAP ERP, SAP BW and S/4 HANA environments into KNIME.

Yes, there is a powerful Web Interaction extension to automate web browsing. In basic cases, you can also use the Webpage Retriever or the GET Request nodes.

KNIME and the community offer numerous readers and connectors. There also are nodes for accessing REST endpoints. Finally, it is possible to build custom nodes using Python or Java.

Yes, you can build reports and send them attached to emails using the Reporting extension, you can also build reports as Data Apps.

Yes, use the Send to Power BI and Send to Tableau Server nodes, or the Tableau Writer.

Data Apps are a way of interactively presenting the outcomes of your workflows and get just-in-time inputs from end users. You can build anything from consumer facing interactive reports and dashboards to forms (choosing from various input widgets) and custom applications. No coding is required and you can progress iteratively from static reports to full-fledged applications.

With data apps, you can not only collect user-defined inputs before the workflow is run, similar to Alteryx's analytic apps, but also add multiple user-interactions throughout the workflow.

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There is a Cheat Sheet showing how to get started with the most commonly used nodes. For a more detailed view, have a look at the Alteryx to KNIME book.

Our AI assistant K-AI understands Alteryx Tools and can help you find the right KNIME nodes. Last but not least, you can learn from many people who have shared their questions and experiences in the KNIME Forum.

Yes, there are AutoML components for Classification and Regression available on the Community Hub free of charge. In addition, you can use the H2O integration.

You can use the Column Expressions node and set up a window size in the Advanced tab.

See answer above.

Yes, use the DB SQL Executor node. For custom table queries, there are the DB Query, DB Query Reader, and Parameterized DB Query Reader nodes, but in most cases the nodes from the Database extension suffice – with no need to code.

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Yes, use annotations or directly add comments to nodes.

Yes, hover the cursor over a connection, then drag and drop the square that appears at the half-way point of the connection.. You can add multiple “bendpoints” to connections.

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KNIME, by default, executes node by node, meaning you can inspect the intermediate results of your workflow at any stage. If you don’t need intermediate results and want to optimize performance, you can use streaming capabilities. In any case, everything executes, no matter how large the data is – but depending on the file size it may take longer.

Configure KNIME Analytics Platform to use the columnar backend and make use of available memory/RAM to speed up performance. Our Best Practices guide shows several examples of how to build efficient workflows.

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Yes, use the Secret Store that is part of KNIME Business Hub and the Secrets Retriever node to securely use credentials or passkeys in KNIME.

You can use shared components, which do not only enable you to share single connections, but also pre-filter, augment and package connections and other data. In fact, any business logic that can be packaged can be shared. With the help of configuration nodes, you can even provide settings options to cover multiple similar use cases.

Yes, KNIME Business Hub can be run on-prem, as well as in customer-managed clouds. KNIME Analytics Platform can be used with Windows, Mac OS, and Linux.

KNIME Business Hub offers governance at multiple levels. Job instrumentation is just one example and provides records of all executions happening. The Admin resources for KNIME Business Hub collection provides details and more examples.

If you need monitoring at an infrastructure level, you can connect KNIME Business Hub to external tools like Grafana. You can also monitor user activity (logins) via Keycloak.

KNIME Business Hub can sync users and groups with corporate identity providers using SCIM. And you can use the Secret Store feature to store and securely access credentials and passkeys from your workflows.

Additionally, the Continuous Deployment for Data Science (CDDS) framework enables you to govern the deployment of workflows flexibly, meeting custom and changing demands. You can browse example workflows and further resources for learning about CDDS for KNIME Business Hub on the KNIME Community Hub.

KNIME Analytics Platform is open source, and backed by KNIME AG. KNIME develops the core software and essential extensions.

Additional extensions are contributed by partners, trusted community members, and the broader KNIME community.

KNIME Business Hub and the Team Plan on KNIME Community Hub are commercial offerings that complement KNIME Analytics Platform.

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KNIME Analytics Platform is KNIME’s open source software with an intuitive, visual interface that lets you build analyses of any complexity level – from automating spreadsheets to ETL to machine learning.

KNIME Business Hub is KNIME’s enterprise software for collaborating on, and deploying data science solutions to drive analytic insights across the organization. Within KNIME Business Hub you have a central collaboration space for all your data work, can automate workflows, and deploy them as data apps.

KNIME Community Hub powers KNIME’s SaaS offering called “Team plan”, where users pay a small monthly fee and then have a pay as you go model for workflow execution and automation. It also offers a free “Personal plan” for versioning workflows and sharing data science workflows with the community. In KNIME Community Hub you can find all of KNIME's extensions as well as countless KNIME and community-built workflows to help you get started.

Both KNIME Business Hub and KNIME Community Hub’s Team plan complement KNIME Analytics Platform.

Yes, there are several free/paid options to train people in KNIME: online courses, self-paced courses and dedicated instructor-led KNIME training sessions that can be delivered by KNIME directly or via partners. Find out more in the Learning Center.

There is currently no way to import Alteryx workflows directly into KNIME. If you only have a few workflows, you can consult our Alteryx to KNIME cheat sheet or Alteryx to KNIME book and go ahead with your migration manually. 

If you need to scale your migration process or it won’t be possible to manually re-build workflows yourself, there are competent KNIME partners who offer help in estimating the scope of efforts and support you in converting your workflows. If you would like to work with a partner to help with your migration, please contact us here and we will put you in touch with the most relevant partner to evaluate your migration options.

That depends. While KNIME does not offer a desktop-based scheduling solution, single teams/small companies can get affordable access to scheduling via the cloud-based Team plan on KNIME Community Hub.

KNIME Business Hub is an enterprise software for collaborating on, and deploying data science solutions to drive analytic insights across the organization.

Business Hub’s scalable, cloud-native architecture lets teams run any number of models and deploy to any number of users. While IT allocates seats and execution resources, team controlled execution ensures faster adoption. 

Anyone who builds analytic solutions with the low-code, no-code KNIME Analytics Platform can leverage KNIME Business Hub to scale these insights across the enterprise

With KNIME Business Hub, organizations can:

• Share knowledge publicly across their organization or privately with select users.
• Build repositories of reusable workflows for teams, departments, or the entire organization, share training materials and give others a head start in building workflows with browsable and downloadable solutions.
• Keep track of who did what with a record of workflow revisions and the ability to rollback.
• Schedule workflows to run automatically to deliver insights when needed and deploy workflows as interactive data apps, REST APIs, or reports organization-wide.

Yes, we offer a cloud-based Team plan on KNIME Community Hub. It works in combination with the free KNIME Analytics Platform, and offers features tailored to single teams/small companies who want an easy way to collaborate on, schedule, and automate workflows.

In addition to community support you’ll find in the KNIME forum, commercial support is offered by KNIME, as well as partners.