#knime

Discover how to create visualisations and interactive views in KNIME Analytics Platform. This tutorial explains how to turn a standard data-analysis workflow into a dashboard that users can explore through filters, widgets, charts and controls.

You will learn how to use KNIME View nodes to build bar charts, line plots, scatter plots, histograms, box plots, heatmaps and detailed table views. The video also shows how Widget nodes let users select regions, choose product categories, adjust numerical ranges and refresh results without rebuilding the workflow.

The tutorial covers importing data, checking data types, handling missing values, preparing date fields, grouping records, sorting time-based data and selecting the correct chart for each analytical question. You will also see how to place visualisations and controls inside a KNIME component, organise them through the composite-view layout editor and create a clear dashboard.

Key topics covered:

• Creating interactive charts and dashboard views • Using filters, sliders and selection widgets • Building bar, line and scatter visualisations • Designing reusable KNIME components • Enabling refresh and workflow re-execution • Creating coordinated charts and detailed tables • Improving dashboard layout and accessibility • Solving empty views, incorrect date ordering and slow performance • Extending a dashboard into a KNIME Data App

This tutorial is useful for students, researchers, data analysts and business professionals who want to analyse information visually without developing one full application. It is also suitable for KNIME assignments, portfolio projects and business-reporting workflows.

Watch the full video to learn how to transform raw data into an interactive analytical experience that supports better insights and decisions.

📊 Mastering Interactive Dashboards in KNIME: A Visual Guide

Choosing the right visualisation is essential for turning data into clear and meaningful insights.

This visual guide explains how to select suitable charts and build effective interactive dashboards in KNIME.

Key areas covered include:

✅ Bar charts for comparing categories ✅ Line plots for tracking trends ✅ Scatter plots for studying relationships ✅ Histograms and box plots for examining distributions ✅ Heatmaps for two-dimensional intensity ✅ Table Views for detailed investigation ✅ Generic ECharts Views for advanced customisation

The guide also highlights the main elements of KNIME dashboard interaction:

🔹 View Nodes to visualise analytical patterns 🔹 Widget Nodes to collect user selections 🔹 Composite Views to combine charts and controls 🔹 Dynamic re-execution to update results through filters and refresh controls

A successful dashboard should prioritise important information, use consistent colours and formats, and provide sensible default settings before users apply filters.

Interactive dashboards can transform raw data into meaningful business insights. Blog: https://assignmentonclick.com/advanced-visualisation-interactive-views-knime In my latest KNIME tutorial, I explored how to create advanced visualisations and interactive views using View nodes, widgets, filters, flow variables, and components. Podcast: https://open.spotify.com/episode/4q9CmgNsFhTtSvtXuY3suC?si=FzPLq1dlROqvE1X0TskR-g The tutorial covers:

✅ Interactive bar charts, line plots, and scatter plots ✅ User-controlled filters and range sliders ✅ Composite dashboard views ✅ Dynamic workflow re-execution ✅ Dashboard layout and design principles ✅ Common errors and practical solutions

KNIME makes it possible to build powerful data-analysis dashboards with minimal coding while giving users greater control over how they explore information.

📊 Mastering Data Visualisation in KNIME starts with choosing the right chart and preparing the data correctly.

A bar chart is ideal for comparing categories, a line chart helps reveal trends over time, and a pie chart works best when presenting a small number of parts within a whole.

However, effective visualisation begins before the chart node. KNIME tools such as Missing Value, GroupBy, Sorter and String to Date&Time help clean, aggregate and organise data so that the final chart is accurate and meaningful.

Strong visual storytelling also requires:

✅ Clear, insight-led chart titles ✅ A zero baseline for bar charts ✅ Chronological ordering for line charts ✅ Limited categories in pie charts ✅ Consistent colours, labels and number formats ✅ Removal of unnecessary visual clutter

The goal is not simply to create an attractive chart. It is to help the audience understand what happened, why it matters and what action should be taken next.

Data visualisation becomes more effective when the right chart is matched with the right question. Blog: https://assignmentonclick.com/basic-charts-in-knime-bar-line-pie In KNIME, bar charts are ideal for comparing categories, line charts help reveal trends over time, and pie charts are useful for showing simple part-to-whole relationships. However, strong visual storytelling also depends on accurate data preparation, clear labels, logical sorting and meaningful chart titles. Podcast: https://open.spotify.com/episode/3FL82FVNfoaQ1kToT8TUbM?si=UhBcw5STS7ieYnDwEed_QQ A well-designed KNIME workflow can transform raw data into clear, actionable insights without requiring extensive coding.

Learn how to perform trend analysis in KNIME using dates, time-based data and visual workflows. This practical tutorial explains how to prepare date columns, sort observations by time, group data into useful periods and identify patterns without complex code.

In this video, you will learn how to:

• Import CSV or Excel data into KNIME • Convert text values into Date and Date&Time formats • Check date quality and find invalid or missing values • Sort records in the correct chronological order • Extract year, quarter, month, week, weekday and hour fields • Create year-month and quarter labels for accurate grouping • Aggregate sales, revenue, orders or other measures by time period • Detect gaps and missing dates in a time series • Calculate absolute and percentage changes between periods • Apply moving averages to smooth short-term fluctuations • Build clear line charts for trend interpretation • Recognise upward, downward, stable and seasonal patterns • Avoid common errors when analysing time-based data

This tutorial is suitable for students, beginners, researchers and professionals who want to build data-analysis skills using KNIME Analytics Platform. The workflow can be adapted for sales performance, website traffic, customer activity, operating costs, inventory levels, financial data and sensor observations.

The recommended workflow follows a simple structure:

Reader Node → Date Conversion → Data Validation → Sorter → Date Extraction → GroupBy → Moving Aggregation → Line Plot → Export

By the end of the video, you will understand how dated records can be transformed into evidence about growth, decline, seasonality and unusual changes. You will also learn why sorting, time granularity, missing periods and correct aggregation are essential for reliable trend analysis.

📊 Mastering Trend Analysis in KNIME: From Raw Data to Visual Insights

Trend analysis becomes much easier when time-based data is prepared and structured correctly.

This workflow presents a clear four-phase approach for analysing trends in KNIME:

🔹 Phase 1: Data Preparation and Sorting Import the dataset, convert text into Date and Date&Time formats, and arrange records chronologically.

🔹 Phase 2: Aggregation and Granularity Extract year, month, week or day fields and use the GroupBy node to summarise data at the correct time level.

🔹 Phase 3: Smoothing and Calculation Apply moving averages, calculate absolute and percentage changes, and identify missing periods that may distort the analysis.

🔹 Phase 4: Visualisation and Interpretation Create a line plot, detect change points, distinguish trend from seasonality and interpret the wider direction of the data.

A reliable KNIME trend-analysis workflow follows this sequence:

Reader Node → Date Conversion → Sorter → Date Extraction → GroupBy → Moving Aggregation → Line Plot