Start here: install KNIME and run your first 2-node workflow
See a live, filtered data table with no code written
KNIME Analytics Platform is a free, open-source desktop workbench where you build data pipelines by wiring "nodes" on a canvas — no programming required. Each node performs one step: read a CSV, filter rows, run a test, plot a chart. You chain them into a workflow that runs top-to-bottom, reproducibly, every time. The fastest way to feel that is to build the smallest possible workflow: a CSV Reader feeding a Row Filter. Because the desktop app runs locally, your data stays on your machine and no account is needed.
- 1 Install KNIME Analytics Platform from knime.com — the download page shows a short optional registration form (email, country, role), then you download and run the installer. KNIME Analytics Platform itself is free and open-source; once installed you build and run workflows locally with no sign-in. A KNIME account is only needed if you later want to publish workflows to KNIME Hub.
- 2 Create a new workflow and drag a CSV Reader node from the node repository onto the empty canvas. Point it at a lab or research CSV (for example a mass-spec, NGS, or flow-cytometry export).
- 3 Add a Row Filter node to the canvas. Drag a connection from the CSV Reader's output port to the Row Filter's input port so the two nodes are wired together.
- 4 Configure the Row Filter to keep only the rows you care about (for instance, rows where one column is above a threshold).
- 5 Hit the green play button to execute the workflow, then right-click the Row Filter and open its output table to see your filtered rows.
You see a live data table of your filtered rows in KNIME — produced by two wired nodes, with no code written.
Why two boxes and a wire already count as a real pipeline
You did not write a script — you assembled one. The CSV Reader and the Row Filter are each a self-contained step, and the wire between them is the data flowing from one step to the next. That is the whole idea of KNIME: a workflow is a visual recipe you can read top-to-bottom, hand to a colleague, and re-run to get the exact same result.
A KNIME workflow is a chain of nodes wired on a canvas, where each node does one step and passes its output table to the next. Nothing is hidden in code, so the workflow is readable by non-programmers and reproducible every time it runs. Because the full Analytics Platform is free, open-source and runs locally, you can build and share these recipes with no account and without your data leaving your machine.
- ?If a collaborator opens your two-node workflow on their own machine, what do they need in order to reproduce your filtered table?
- ?Why is wiring a CSV Reader into a Row Filter easier to hand off than the equivalent few lines of Python?
- ?Where does your data physically live while this workflow runs, and why does that matter for unpublished research?
Make the workflow do one useful thing for your own data
A two-node toy is enough to learn the mechanics, but the point of KNIME is a workflow that earns its keep. Extend yours so it produces something you would actually keep.
Point the CSV Reader at a real CSV from your work and tune the Row Filter so the output table is genuinely useful — a clean subset you would hand to a colleague.
- 1 Swap in a real research CSV (a messy instrument export is ideal).
- 2 Adjust the Row Filter criteria until the output table contains exactly the rows you want.
- 3 Re-run with the green play button and open the output table to confirm the result.
- 4 Save the workflow so it can be re-run or shared as a reproducible recipe.
A saved two-node KNIME workflow that reads one of your own CSVs and outputs a filtered table you would actually use.