Manual Excel cleanup of plate‑reader data takes forever
A 45-minute manual Excel clean-up is replaced by a reproducible, one-click pipeline any lab member can run.
Track 11 · Advanced
11.1
KNIME
Visual data science — no code needed
1Overview
Made by KNIME AG, first released in 2006 — one of the longer-established names in visual data tools, keeping every transformation step visible as you build it.
KNIME Analytics Platform is a free, open-source visual workbench: build data pipelines by connecting drag-and-drop nodes on a canvas — read a CSV, filter rows, join tables, run a statistical test, plot a chart — no programming required. With 300+ connectors and built-in machine learning, it is the tool for turning a messy lab instrument export (mass-spec, NGS, flow-cytometry CSVs) into a reproducible pipeline you can share with a collaborator as a portable "recipe" — no Python environment needed. → Unlike n8n (which wires apps and AI agents together), KNIME is built for the data-cleaning and analysis step itself.
1.1After this chapter you can
→Install KNIME and run your first no-code workflow, node by node
→Read the canvas: nodes, ports and the traffic-light states
→Join and clean two messy lab exports into one table, then visualise it
→Add a Learner/Predictor branch for a first no-code machine-learning model
1.2Best for
Turning raw instrument output — mass-spec, NGS, flow-cytometry data — into something a non-programming collaborator can rerun and share on their own, no separate Python setup required.
1.3Watch out
Memory-hungry on large datasets — over 1 GB can slow or crash the desktop app — and team collaboration/scheduling needs a paid Hub subscription.
1.4Free vs paid
The desktop Analytics Platform is fully free and open-source. KNIME Community Hub adds a paid Pro tier (~$19/month) and Team tier (~$99/month for up to 3 users); Business Hub pricing is on request.
2Lessons 5
2.1 Filter a CSV file using KNIME
KNIME Analytics Platform is an open‑source visual workbench where you build data pipelines by wiring nodes on a canvas, each performing a single action such as reading a CSV or filtering rows.
Filter rows from a CSV file using two connected nodes
Try
Create a new workflow called Demo, drag a CSV Reader node onto the canvas and set its file to C:/data/sample.csv, then add a Row Filter node, connect the CSV Reader output to the Row Filter input, configure the filter to keep rows where the column Score > 50, and click the green play button to run.Paste this instruction into the KNIME Workflow Editor while building the workflow. Make sure the file path points to an existing CSV on your machine and that the column name matches exactly; otherwise the nodes will report configuration errors.
- Create a new workflow by clicking Create new workflow on the entry page
- Drag a CSV Reader node onto the canvas and configure it to point at your file
- Add a Row Filter node, connect the CSV Reader’s output port to the Row Filter’s input port
- Configure the Row Filter to keep the desired rows
- Execute the workflow by clicking the Execute button on the Row Filter
- You'll see A data table view shows only the rows that match the filter criteria
- Takeaway Node wiring lets you apply transformations without writing code
- Check Which node’s output port must be connected to the Row Filter’s input port to supply the data for filtering?
2.2 Read node status lights and ports on the canvas
Each node displays a coloured traffic‑light icon indicating its configuration and execution state, and triangular ports that show the data type flowing in or out.
Do this first Filter a CSV file using KNIME
Observe how node colours change as you configure and run them
Try
Open the workflow named "TwoNodeDemo" in KNIME, select the CSV Reader node, set its file path to "data/customers.csv", click Apply, then right‑click the node and choose **Execute**.Paste this into the KNIME workspace by opening the File > Open Workflow dialog, loading "TwoNodeDemo.knwf", and follow the steps on the canvas. Watch the CSV Reader’s traffic light change from red → yellow → green as you configure and run it.
- Open an example workflow or place CSV Reader and Row Filter nodes on the canvas
- Notice the traffic‑light icons appear red on new nodes
- Configure the CSV Reader and watch its light switch to yellow
- Execute the CSV Reader by clicking its Execute button and see the light turn green, with a table appearing at its output port
- Hover over the input and output ports to view the data type information
- You'll see Node icons change from red to yellow to green and ports display small triangles indicating data types
- Takeaway Traffic‑light colours instantly tell you whether a node is ready, configured or executed
- Check When a node has been configured but not yet run, what colour does its traffic‑light icon show?
2.3 Combine two data exports into a single table
Loading multiple sources onto one canvas lets you align common columns for joining or stacking without writing code.
Do this first Read node status lights and ports on the canvas
Load two files and combine them into a single table
Try
Add a CSV Reader node to the canvas, open its configuration, set File/URL to C:/knime/data/sales_q1.csv, press OK and execute the node. Then add an Excel Reader node, configure it with File/URL C:/knime/data/sales_q2.xlsx, press OK and execute that node.Paste each file path into the File/URL field of the respective reader’s configuration dialog, click OK, then right‑click the node and choose Execute. Watch for the green light indicating successful loading.
- Add a CSV Reader node and configure it for the first export file
- Add another CSV Reader (or an Excel Reader) node and configure it for the second export file
- Execute both reader nodes so their status lights turn green
- Identify a common column in both tables and, if needed, rename columns using Column Renamer to make them match
- Connect the two reader outputs to a Concatenate node to stack the rows
- Execute the Concatenate node to produce the merged table
- You'll see The Concatenate node turns green and its output view shows a single table containing rows from both source files
- Takeaway Using Concatenate lets you merge tables with identical column structures without code
- Check Which node would you use to stack two tables that share the same columns after ensuring their column names match?
2.4 Explore your data with a histogram
The Histogram node creates an interactive chart from a numeric column of a data table.
Do this first Combine two data exports into a single table
Generate a histogram for a selected numeric column
Try
Configure the Histogram node: set ‘Column Selection’ to ‘Age’, keep default bin settings, and press ‘OK’.In the Histogram Node Configuration window that appears after double‑clicking the node, select the Age column, then click OK, execute the node, and open its view to see the histogram.
- Select the cleaned data table on the canvas
- Add a Histogram node and connect the table to its input port
- Open the Histogram configuration dialog and choose the numeric column to plot
- Execute the Histogram node by clicking its Execute button
- Open the node view to display the chart
- You'll see An interactive histogram of the chosen column appears in the node’s view panel
- Takeaway The Histogram node visualises distributions directly within a workflow
- Check After configuring a Histogram node, which action displays the plotted distribution on screen?
2.5 Train and apply a model using Learner and Predictor nodes
Machine‑learning pipelines are built by wiring a Table Partitioner, a Learner and a Predictor node, allowing you to train a model and generate predictions without code.
Do this first Explore your data with a histogram
Train a model and generate predictions in a single visual pipeline
Try
Add a Table Partitioner node to your clean table, set the partition ratio to 0.8 (80% training, 20% test), then connect its training output to a Decision Tree Learner node. Connect the learner’s model output and the partitioner’s test output to a Decision Tree Predictor node. Execute all three nodes.In the KNIME Workflow Editor, use the Node Repository to drag each node onto the canvas, configure the Table Partitioner’s ratio in its dialog, and wire the ports as described. After execution, open the Predictor’s view – watch for the green status indicating successful prediction.
- Add a Table Partitioner node to split the clean table and configure an 80/20 ratio
- Connect the training partition to a Decision Tree Learner (or another learner) node to build the model
- Insert a Predictor node, wiring the trained model output from the Learner and the test partition into it
- Execute the branch containing the Partitioner, Learner and Predictor
- Open the Predictor node’s view to see the output table with predictions alongside actual values
- You'll see The Predictor node turns green and its output table displays predicted values next to the test data
- Takeaway Partioner, Learner and Predictor nodes together create a full machine‑learning workflow without scripting
- Check Which node provides the final table that contains both the original test data and the model’s predicted values?
3You’ll know it worked 21 checkable outcomes in this chapter
- ✓Lab members run the workflow and see a tidy Excel file with bar charts
- ✓Report displays mean and SD per Likert scale after filtering incomplete responses
- ✓Finance receives an Excel file with monthly spend bars and >15% change flags
- ✓The base of the new node displays a red circle
- ✓All three nodes display green lights after execution and the final output table matches expectations
- ✓The output table contains the rows of both inputs in a single view
- ✓A .knwf file appears in your workspace and can be reopened with all nodes intact
- ✓After execution, the node's status turns green and a model artifact appears in its output port
21 outcomes in all — one per recipe below.
4FAQ, Tips & How-to 54
one problem, one solution, one action
Research & data tools3
Need to add functional info to many FASTA entries
Hundreds of sequences are annotated overnight without writing a single line of Python.
Need a weekly QC report that flags visit values
Every data transfer is quality-checked consistently and a report is ready before the weekly data review call.
Forms, surveys & feedback1
Survey responses that are mixed up or incomplete
Data cleaning that usually takes a day in SPSS or R is a reusable, auditable KNIME workflow.
Dashboards & analytics3
Get batch predictions from CSV without coding
A working predictive model is built and validated without writing any code, and retraining is a one-click re-run.
Need fast boardroom‑ready spend analysis
Finance produces a boardroom-ready spend analysis in one click instead of spending an afternoon in Excel pivot tables.
Monthly headcount and attrition reporting takes half a day
The monthly people-metrics pack that previously took a half-day in Excel is produced in one workflow run, consistently and without formula errors.
Need to get documents from a MongoDB collection
Learn how to pull raw JSON records from a MongoDB collection into KNIME
JSON records are stuck in one column
Turn raw JSON records into a tabular format for further processing
Know the exact node sequence to bring MongoDB data into KNIME for processing
Rows contain empty clean content
Filter out irrelevant or incomplete data before vectorization
Need to keep API keys safe
Store sensitive credentials once and reuse them across workflows
Need an API key inside your workflow
Inject a stored API key into the workflow as a variable
Need to pass your OpenAI key securely
Securely pass the OpenAI key to downstream nodes
Can't decide which embedding model to use
Select a lightweight OpenAI model for generating embeddings
Want to run data workflows on your computer without any sign‑in
You can run KNIME locally without any account or sign-in
Need a fresh canvas for wiring nodes
A fresh canvas lets you wire nodes together to form a reproducible data pipeline
Need to load a CSV into your workflow
You can bring any CSV into KNIME without writing code
A newly added node starts in an unconfigured state, shown by a red light
After you fill in the required parameters, the node turns yellow, indicating it is ready to run
Execute a configured node
Executing a configured node turns its light green and makes the output port contain a data table
Tooltips on ports reveal what kind of data each expects or produces, helping you debug connections
Add a third step and verify it works
You can extend a workflow step-by-step, using traffic-light feedback to ensure each new node is correctly configured and executed before proceeding
Want to pull CSV, Excel and database files together
You can bring any combination of CSV, Excel, database or cloud files into the same KNIME canvas
Running the readers verifies that the files are correctly read and available for downstream processing
Finding a common column lets you decide how to combine the tables
Multiple tables need to be combined into one view
A Concatenate node merges tables by appending rows, creating one larger table
After combining tables the column headers are a mess
Renaming columns makes the combined table readable and downstream-compatible
Want to reuse the same join and transform steps on new data
Saving the KNIME workflow lets you reuse the exact join-and-transform steps on new exports
Need to view a column’s distribution in your workflow
You can plot a column's distribution directly in KNIME without leaving the workflow
Not sure which numbers to visualise
Choosing the correct numeric column lets you see its distribution instantly
Need to run a step and explore data
Running the node generates a chart you can explore directly in KNIME
Need separate training and test tables
You can create separate training and test tables without writing code, enabling proper model evaluation
I have a training dataset and need a decision‑tree model
The learner builds a decision-tree model that captures patterns in your training data
Need predictions for my test set
You obtain predicted values for each row in the test set, enabling direct comparison with actual outcomes
You can quantitatively compare how well a model predicts by viewing its accuracy score and confusion matrix
Running two separate Learner-Predictor pipelines lets you pick the better algorithm based on their Scorer results
Want an analytics platform without signing up
Get KNIME installed locally without needing an account
Need to load a CSV file
Load a CSV file into a KNIME workflow
Two tables have the same columns and I want them combined
Combine datasets with identical columns into a single table by stacking rows
Two tables need to be merged on a common column
Merge two tables based on a common column, selecting the desired join type
Need to train and evaluate a model without coding
Build a reproducible ML workflow in KNIME using only nodes
What is KNIME and what is it used for?
KNIME Analytics Platform is a free, open-source desktop workbench for building data pipelines visually. Instead of writing code, you drag "nodes" onto a canvas and wire them together: one node reads a CSV, another filters rows, another trains a model, another draws a chart. Chained together, those nodes form a workflow that runs top-to-bottom and reproduces the same result every time. It is used for data cleaning, blending multiple sources, exploratory analysis, machine learning, and reporting. KNIME has a strong life-sciences pedigree and is widely used in drug discovery, NGS, and clinical data pipelines, but it suits any field that wrangles tabular data without a programming environment.
Does my data stay on my machine, or does it go to the cloud?
With the desktop KNIME Analytics Platform, your data stays local. Workflows execute on your own machine, files are read from and written to your own disk, and no account or upload is needed to build and run a pipeline. That makes it well suited to unpublished research or sensitive data that should not leave your environment. Data only goes to the cloud if you deliberately use a cloud connector (for example an S3 or Snowflake reader), publish a workflow to KNIME Community Hub, or call a hosted AI node such as OpenAI. The AI/LLM extension also lets you wire in local models via Ollama, keeping inference on-premises too.
What are KNIME's main limitations?
KNIME is memory-hungry: large datasets (roughly over 1 GB held in memory) can slow or crash the desktop app, and it is not a drop-in replacement for distributed engines like Spark or Dask out of the box. The visual canvas, which is a strength on small workflows, becomes hard to read once you have many branches and dozens of nodes ("node sprawl"); metanodes and components help by collapsing sections. Team sharing, scheduling, and web execution all require a paid Hub plan rather than the free desktop. And despite the no-code promise, discovering the right node among thousands and learning the configuration dialogs still carries a real initial learning curve.
How is KNIME different from a workflow tool like n8n?
Both let you wire nodes on a canvas, but they aim at different jobs. KNIME is a data-science workbench: its nodes read, clean, blend, model, and visualise tabular data, and a workflow runs as a batch analysis you execute and re-run for reproducible results. n8n is an automation and integration tool: its nodes connect apps and APIs and run on triggers or schedules to move data and fire actions between services. Reach for KNIME when the goal is analysing or modelling a dataset and producing a clean table, figure, or prediction; reach for n8n when the goal is automating a process that passes small payloads between SaaS tools. They overlap at data movement but optimise for opposite ends.
Is KNIME really free, and is it open source?
Yes. The desktop KNIME Analytics Platform is fully free and open source, and KNIME's own download page describes it as "a free and open source low-code/no-code software." You install it, build workflows, and run them locally with no licence fee and no usage limits on the desktop app. What costs money is the optional cloud collaboration layer (KNIME Community Hub paid plans and the enterprise Business Hub) used for team sharing, scheduling, and web deployment. For an individual learning data science or running analyses on their own machine, nothing about the core platform needs to be paid for.
How do I install KNIME and do I need an account?
Go to knime.com/downloads and pick the installer for your operating system. The download page shows a short registration form (email, company, location, role) that you complete before the download starts, but the installed Analytics Platform itself needs no sign-in: you build and run workflows locally with no account. A KNIME account is only required later if you want to publish workflows to KNIME Community Hub. After installing, launch the app, create a new workflow, and you are on an empty canvas ready to drag your first node from the node repository.
What operating systems does KNIME run on, and how much RAM do I need?
KNIME Analytics Platform runs on Windows (10, 11, and Server 2016/2019/2022), Linux (Ubuntu 20.04/22.04 LTS, RHEL/CentOS/Rocky 8 and 9), and macOS (Sonoma and Sequoia, on both Intel and Apple Silicon). It ships with the runtime it needs, so there is no separate Java install to manage. Memory is the setting most worth tuning: the default heap allocation is just 1024 MB, and KNIME recommends raising it to roughly half your available system RAM by editing the -Xmx value in the knime.ini file. On a 16 GB machine that means setting it to around 8 GB, which keeps larger tables responsive.
What does KNIME Hub cost, and what do the paid plans add?
KNIME Community Hub has a free Personal plan that connects to 300+ data sources, includes 20 K-AI interactions per month, and allows local sharing only. The Pro plan is $19/month (also EUR 19): it adds manual workflow runs, deployment as data apps, secrets storage, 500 K-AI interactions, and 120 execution credits (then $0.025 per vCore-minute) for one user. The Team plan is $99/month and adds private collaboration spaces for 3 members (extra members $49/month, up to 10 users) with centralised billing. Scheduling, REST-API deployment, and enterprise authentication live in Business Hub, which is priced on request. The desktop platform stays free regardless of which Hub tier you choose.
How do I build my first workflow in KNIME?
The smallest useful workflow is two nodes. Create a new workflow, drag a CSV Reader node from the node repository onto the canvas, and point it at a CSV file. Then drag a Row Filter node next to it and draw a connection from the CSV Reader's output port (the triangle on its right) to the Row Filter's input port. Double-click the Row Filter to configure which rows to keep, then press the green play button to execute. Right-click the Row Filter and open its output table to see your filtered rows. You have built a real, reproducible pipeline without writing a single line of code.
What do the red, yellow, and green lights on a node mean?
Every node carries a small traffic light at its base that tells you its state at a glance. Red means the node is not yet configured, so it cannot run. Yellow means it is configured and ready but has not executed. Green means it executed successfully and its output is available to the next node. The triangles on a node's sides are its ports: data enters on the left and leaves on the right. Reading these together is the core debugging skill in KNIME: a chain that stops at a yellow or red node shows you exactly where execution halted and which step still needs attention.
How do I combine or join two data files in KNIME?
Add a reader node for each source (for example a CSV Reader and an Excel Reader), point each at its file, and execute both so they turn green. To stack rows from two tables with the same columns, wire both into a Concatenate node. To match rows by a shared key (like an ID column), use a Joiner node instead, which performs inner, left, right, or outer joins. A Column Renamer node helps tidy mismatched column names so the tables line up first. Because each step is a visible node on the canvas, anyone opening the workflow can trace exactly how the messy inputs became one clean, analysis-ready table.
Can I do machine learning in KNIME without coding?
Yes. Machine learning is built from the same wired nodes as everything else. The standard pattern is: a Partitioning node splits your clean table into a training set and a test set (say 80/20); a Learner node (Decision Tree, Random Forest, or XGBoost Tree Ensemble) trains a model on the training set; and the matching Predictor node applies that model to the held-out test set. Wire a Scorer node onto the Predictor's output to get an accuracy figure and confusion matrix. To compare two algorithms fairly, put each on its own branch with its own Scorer and read the numbers side by side. The whole pipeline stays on the canvas, reproducible and shareable, with no code written.
The same set on /recipes, filtered by tool and role.
5Videos 3
KNIME Tutorial for Beginners EP 01 | No-Code Data Analytics Explained | Assignment On Click
The start of a structured beginner series. Watch if you want to learn KNIME step by step across short, focused episodes.
Get Started with Nodes & Workflows in KNIME
Straight from KNIME. The authoritative first 7 minutes before any third-party course.
KNIME 08: KNIME Interface Explained: Nodes, Workflow & Execution | KNIME Tutorial | Data Analytics
The academic, no-rush walkthrough. Good if you want the interface mapped before cleaning real data.
6FAQ 12
What is KNIME and what is it used for?
KNIME Analytics Platform is a free, open-source desktop workbench for building data pipelines visually. Instead of writing code, you drag "nodes" onto a canvas and wire them together: one node reads a CSV, another filters rows, another trains a model, another draws a chart. Chained together, those nodes form a workflow that runs top-to-bottom and reproduces the same result every time. It is used for data cleaning, blending multiple sources, exploratory analysis, machine learning, and reporting. KNIME has a strong life-sciences pedigree and is widely used in drug discovery, NGS, and clinical data pipelines, but it suits any field that wrangles tabular data without a programming environment.
Does my data stay on my machine, or does it go to the cloud?
With the desktop KNIME Analytics Platform, your data stays local. Workflows execute on your own machine, files are read from and written to your own disk, and no account or upload is needed to build and run a pipeline. That makes it well suited to unpublished research or sensitive data that should not leave your environment. Data only goes to the cloud if you deliberately use a cloud connector (for example an S3 or Snowflake reader), publish a workflow to KNIME Community Hub, or call a hosted AI node such as OpenAI. The AI/LLM extension also lets you wire in local models via Ollama, keeping inference on-premises too.
What are KNIME's main limitations?
KNIME is memory-hungry: large datasets (roughly over 1 GB held in memory) can slow or crash the desktop app, and it is not a drop-in replacement for distributed engines like Spark or Dask out of the box. The visual canvas, which is a strength on small workflows, becomes hard to read once you have many branches and dozens of nodes ("node sprawl"); metanodes and components help by collapsing sections. Team sharing, scheduling, and web execution all require a paid Hub plan rather than the free desktop. And despite the no-code promise, discovering the right node among thousands and learning the configuration dialogs still carries a real initial learning curve.
How is KNIME different from a workflow tool like n8n?
Both let you wire nodes on a canvas, but they aim at different jobs. KNIME is a data-science workbench: its nodes read, clean, blend, model, and visualise tabular data, and a workflow runs as a batch analysis you execute and re-run for reproducible results. n8n is an automation and integration tool: its nodes connect apps and APIs and run on triggers or schedules to move data and fire actions between services. Reach for KNIME when the goal is analysing or modelling a dataset and producing a clean table, figure, or prediction; reach for n8n when the goal is automating a process that passes small payloads between SaaS tools. They overlap at data movement but optimise for opposite ends.
Is KNIME really free, and is it open source?
Yes. The desktop KNIME Analytics Platform is fully free and open source, and KNIME's own download page describes it as "a free and open source low-code/no-code software." You install it, build workflows, and run them locally with no licence fee and no usage limits on the desktop app. What costs money is the optional cloud collaboration layer (KNIME Community Hub paid plans and the enterprise Business Hub) used for team sharing, scheduling, and web deployment. For an individual learning data science or running analyses on their own machine, nothing about the core platform needs to be paid for.
How do I install KNIME and do I need an account?
Go to knime.com/downloads and pick the installer for your operating system. The download page shows a short registration form (email, company, location, role) that you complete before the download starts, but the installed Analytics Platform itself needs no sign-in: you build and run workflows locally with no account. A KNIME account is only required later if you want to publish workflows to KNIME Community Hub. After installing, launch the app, create a new workflow, and you are on an empty canvas ready to drag your first node from the node repository.
What operating systems does KNIME run on, and how much RAM do I need?
KNIME Analytics Platform runs on Windows (10, 11, and Server 2016/2019/2022), Linux (Ubuntu 20.04/22.04 LTS, RHEL/CentOS/Rocky 8 and 9), and macOS (Sonoma and Sequoia, on both Intel and Apple Silicon). It ships with the runtime it needs, so there is no separate Java install to manage. Memory is the setting most worth tuning: the default heap allocation is just 1024 MB, and KNIME recommends raising it to roughly half your available system RAM by editing the -Xmx value in the knime.ini file. On a 16 GB machine that means setting it to around 8 GB, which keeps larger tables responsive.
What does KNIME Hub cost, and what do the paid plans add?
KNIME Community Hub has a free Personal plan that connects to 300+ data sources, includes 20 K-AI interactions per month, and allows local sharing only. The Pro plan is $19/month (also EUR 19): it adds manual workflow runs, deployment as data apps, secrets storage, 500 K-AI interactions, and 120 execution credits (then $0.025 per vCore-minute) for one user. The Team plan is $99/month and adds private collaboration spaces for 3 members (extra members $49/month, up to 10 users) with centralised billing. Scheduling, REST-API deployment, and enterprise authentication live in Business Hub, which is priced on request. The desktop platform stays free regardless of which Hub tier you choose.
How do I build my first workflow in KNIME?
The smallest useful workflow is two nodes. Create a new workflow, drag a CSV Reader node from the node repository onto the canvas, and point it at a CSV file. Then drag a Row Filter node next to it and draw a connection from the CSV Reader's output port (the triangle on its right) to the Row Filter's input port. Double-click the Row Filter to configure which rows to keep, then press the green play button to execute. Right-click the Row Filter and open its output table to see your filtered rows. You have built a real, reproducible pipeline without writing a single line of code.
What do the red, yellow, and green lights on a node mean?
Every node carries a small traffic light at its base that tells you its state at a glance. Red means the node is not yet configured, so it cannot run. Yellow means it is configured and ready but has not executed. Green means it executed successfully and its output is available to the next node. The triangles on a node's sides are its ports: data enters on the left and leaves on the right. Reading these together is the core debugging skill in KNIME: a chain that stops at a yellow or red node shows you exactly where execution halted and which step still needs attention.
How do I combine or join two data files in KNIME?
Add a reader node for each source (for example a CSV Reader and an Excel Reader), point each at its file, and execute both so they turn green. To stack rows from two tables with the same columns, wire both into a Concatenate node. To match rows by a shared key (like an ID column), use a Joiner node instead, which performs inner, left, right, or outer joins. A Column Renamer node helps tidy mismatched column names so the tables line up first. Because each step is a visible node on the canvas, anyone opening the workflow can trace exactly how the messy inputs became one clean, analysis-ready table.
Can I do machine learning in KNIME without coding?
Yes. Machine learning is built from the same wired nodes as everything else. The standard pattern is: a Partitioning node splits your clean table into a training set and a test set (say 80/20); a Learner node (Decision Tree, Random Forest, or XGBoost Tree Ensemble) trains a model on the training set; and the matching Predictor node applies that model to the held-out test set. Wire a Scorer node onto the Predictor's output to get an accuracy figure and confusion matrix. To compare two algorithms fairly, put each on its own branch with its own Scorer and read the numbers side by side. The whole pipeline stays on the canvas, reproducible and shareable, with no code written.
7Glossary 20 terms
Show the 20 terms
KNIME
Workflow- A collection of connected nodes arranged on the editor canvas that together carry out a complete data analysis from reading in data to producing a result.
Node- A single building block in a KNIME workflow — a colored box that performs one task, such as reading a file, filtering rows, or training a model.
Node Repository- The searchable panel listing every node available in your KNIME installation; you drag nodes from here onto the workflow canvas.
Workflow Editor- The central canvas where you place and connect nodes to build a workflow.
Port- A connection point on the side of a node — input ports (left) receive data and output ports (right) send data to the next node.
Data Port- A port that passes a data table between nodes; shown as a black triangle and can only connect to another data port.
Model Port- A port that passes a trained machine-learning model from one node (e.g. a trainer) to another (e.g. a predictor).
Flow Variable- A named value (like a file path or a number) that travels between nodes through flow-variable ports and can change how a node is configured without editing it by hand.
Node Status- A traffic-light indicator beneath each node: red means not yet configured, yellow means configured and ready, green means successfully executed.
Component- A reusable, shareable group of nodes packaged as a single custom node with its own configuration dialog; can be published to KNIME Hub for others to use.
Metanode- A group of nodes collapsed into one box purely to keep the canvas tidy; unlike a Component it cannot be shared or given a custom dialog.
Workspace- The folder on your computer where KNIME stores all your workflows, node settings, and any data the workflow produces.
KNIME Hub- The online repository where you can store, share, and download KNIME workflows and components; available as a free Community Hub or an enterprise Business Hub.
K-AI- The built-in KNIME AI assistant that can answer questions about the platform (Q&A mode) or automatically extend your workflow by suggesting and adding nodes (Build mode).
Space Explorer- The file-browser panel in KNIME where you navigate and manage your workflows, folders, components, and data files.
Node Monitor- The bottom panel that shows the output table, statistics, or flow variables produced by a selected node after it has been executed.
Workflow Annotation- A free-text box you can place anywhere on the workflow canvas to document what a section of the workflow does; supports basic markdown formatting.
Columnar Backend- An optional KNIME execution engine that stores table data column-by-column using Apache Arrow, which reduces memory use on large datasets.
RowID- A unique identifier automatically assigned to every row in a KNIME data table, similar to a row number in a spreadsheet.
Execution- The act of running a node or an entire workflow so that it processes its input data and produces output; triggered by pressing F7 or clicking the Execute button.
8See also
💬 Discuss this chapter
Ask, share, or report — over on the Heidelberg AI community forum.