KNIME for Beginners: No-Code Data Cleaning & ETL Case Study

Name: KNIME for Beginners: No-Code Data Cleaning & ETL Case Study
Rating: 4.7 (1480 reviews)

Learn KNIME step-by-step to clean messy data, build workflows, and solve a real consulting case — without coding.

Created byDan We

Last updated 3/2026

English

German [Auto],English [Auto],

What you'll learn

Clean and transform messy real-world datasets using KNIME
Build visual workflows for data preparation (no coding required)
Perform ETL (extract, transform, load) processes step by step
Solve a real consulting case using structured data analysis
Combine and reshape data from multiple sources
Automate repetitive data preparation tasks
Prepare datasets for Power BI, Tableau, or further analysis
Understand when to use KNIME instead of Excel or Python

Course content

5 sections • 57 lectures • 8h 59m total length

Welcome and Introduction1:11
One important thing before you start0:25

Important please read this Courseupdate Info0:31
1 Introduction to KNIME Interface Deep Dive23:05
Explore the Knime interface, create folders and workflows in local space, and build, configure, and run nodes in the workflow editor to manage data.
Download the course resources0:02
2 Modern UI vs Classic Interface3:06
Explore knime's classic interface and the modern user interface, learn how to switch between them, access the knime explorer, use fuzzy search to find notes, and manage workflows.
Transition in KNIME 50:22
Test
3 A brief look into the case study data sources2:23
4 Case Study Importing and cofiguring Excel Data14:22
KNIME crash course teaches importing 12 Excel files, consolidating transactional and product data into one table, and enriching with web currency data for a case study.
5 Efficiently importing multiple Files in KNIME17:56
6 Introduction to Loops in KNIME10:46
Master loops in KNIME to read multiple files by iterating file paths with a list files and folders node, a loop start and end, and a variable-driven CSV reader.
7 Datacleaning and Saving Files to disk12:14
8 Dataenrichment Extracting Currency information from the web12:01
Extract currency information from the ECB web feed, convert the XML to JSON, and pull the currency symbol and exchange rate using jsonpath, then save to an Excel file.
9 Final Cleaning steps handling missing values7:32
10 Combining our cleaned data part 118:01
11 Combining our cleaned data part 26:17
Learn how to combine data in Knime using joined nodes to map country and product name, add list price and production cost, and export the result to a CSV.
12 Data Transformation and Cleaning with KNIME 115:38
Learn data transformation and cleaning with KNIME, removing duplicates and invalid quantities, calculating revenue, and joining USD exchange rates to prepare a final dataset for Power BI or Tableau.
13 Data Transformation and Cleaning with KNIME 212:49
Learn data cleaning and transformation in Knime, calculate profit, join exchange rates, convert to euro, and export a csv for bi visualization.
14 Connecting KNIME with Power BI and Tableau8:52
Learn how KNIME exports data to Tableau and Power BI by installing Tableau and Power BI extensions, using writer and server nodes, and publishing data sources to servers.
15 Visualizing Our Data in Power BI8:02
16 A glimpse into Data Visualization in KNIME5:34
Learn how to visualize data directly in KNIME with bar charts, configuring categories, sums, and profits, and explore interactivity, axes settings, and image export.
KNIME Q and A How to sort bar charts in the bar plot Node4:27
14 Machine Learning in KNIME customer churn prediction21:07
Learn to build a no-code machine learning workflow in KNIME to predict customer churn using training and validation data, a random forest model, and confusion matrix based evaluation.
17 Generative AI in KNIME9:49
Explore generative AI in Knime with built-in chi assistant after free signup. Learn to retrieve web data via the webpage retriever and XPath nodes, then filter columns and rows.

0 Switch between old and new interface in KNIME1:46
Learn how to switch between the classic user interface and the modern UI in KNIME version 5, and how to work in either interface as needed.
1 Getting Started with KNIME 520:34
Start KNIME analytics platform version five by creating a workflow in your local space, using the node repository, and configuring and running nodes to build and monitor data.
Resources download0:02
2 Lets understand the case study data3:07
Prepare the 2022 energy drink transaction data from CSV files, cover product details, regions, sales reps, and pricing, and learn data cleaning and region lookup before the case study.
3 Importing Case Study data into KNIME14:52
Knime crash course for beginners teaches importing case study data into Knime using Excel reader and CSV reader, consolidating twelve monthly transactions with product info and managers, and data cleaning.
4 Importing Data more efficientlly into KNIME13:53
5 Introduction to Loops in KNIME12:22
Discover KNIME loops for reading multiple data sources with list files and folders, table row to variable loop, and CSV reader, using a path flow variable.
6 Cleaning and saving the data12:19
Knime teaches cleaning the sales area and sales rep data by replacing abbreviations with region names via a region dictionary and value lookup, then saving via csv and excel writers.
7 Extracting Exchange rates from the Web with KNIME13:05
Enrich data in Knime by fetching exchange rates with a get request, converting XML to JSON, extracting USD rate via JSON path, and exporting results to Excel.
8 Combining the different files together18:21
Learn to join transaction data with cleaned sales reps in KNIME using the joiner, matching on sales area and product name to map sales managers.
9 Combining our data together Part 214:09
Combine transactions with product information in KNIME using a join node and missing value handling. Enrich with sales manager and pricing details, then export to csv.
10 Dataprep Part 1 Cleaning our data with KNIME20:35
11 Dataprep Part 2 Cleaning our data with KNIME12:42
Learn how to join currency exchange data in knime, convert us dollar revenue to euros using the exchange rate, and save the results for visualization with a csv writer.
12 KNIME with Power BI and Tableau7:20
13 Introduction to data visualization with KNIME and Power BI7:42
Visualize cleaned KNIME data in Power BI by importing a CSV, using the Power Query editor, and creating bar charts and country maps of revenue by product.

Important Note before we start0:29
Download all resources for this section here0:04
Install the latest Table to pdf node for the next lecture2:35
Learn how to install the latest Table to PDF node in KNIME, resolve deprecation issues, and use KNIME hub or node pit to update and restart for the next video.
3 KNIME Workflow Automation Time or Event Trigger16:08
4 Knime Workflow Automation Excecution Time and Workflow Bottlenecks4:11
5 KNIME terminate workflow with loops with breakpoint16:12
Explore KNIME's breakpoint node and how it terminates a looped workflow when a file is empty, using list files and folders, variable loops, and transfer files to copy data.
7 UPDATE Case Conditions in KNIME including Flow Variables17:58
Master KNIME's case switch start and end nodes, configure ports and flow variables, and build pivoted and calculated sales workflows using math formulas, single selection configuration, and grouping by product.
8 Handling no data in KNIME10:41
Explore how KNIME's empty table switch handles no data by routing flows to appropriate outputs, enabling conditional transfers only when the table contains data, and preventing partial workflow execution.
9 Error Handling with Try Catch Nodes in KNIME7:28
10 Excourse Java IF Switch with KNIME an Introduction6:18
Explore the KNIME Java if table node, configuring data inputs and integer settings to switch between zero and one ports using equals and return logic.
11 KNIME Error Handling for API requests13:25
Explore error handling in KNIME with try catch for API requests, using get requests to json placeholder and alternate results for failed URLs.
12 Workflow interaction invoke another workflow in KNIME update13:24
Learn to call another KNIME workflow inline using a table-based approach, supply a sample data table, and return transformed results to the original workflow.
13 Databases in KNIME an Introduction10:55
14 Databases in KNIME Create Database Tables9:56
Learn how to create and populate a SQLite database in KNIME using DB writer node, fetch latest tables, and read results with the DB table selector and DB reader.
15 Database Operations in KNIME8:53
16 Database Operations in KNIME part 210:58
Congratulations, thanks a lot and one favor to ask0:21

Requirements

No coding required
KNIME can be downloaded for free
Your interest in data preparation and data analytics, visual analytics is key!
This crashcourse starts from scratch - we learn the basics and build a solid foundation during the case study
Tableau Desktop is optional (but not required)
Microsoft Power BI Desktop is optional (but not required)

Description

Learn how to clean, transform, and prepare real-world data using KNIME without writing a single line of code.

In this hands-on course, you will work on a real consulting-style case study based on an energy drinks dataset. You will learn how to turn messy, unstructured data into clean, analysis-ready datasets that can be used for reporting and decision-making.

You will build KNIME workflows step by step and learn how to automate data preparation processes. By the end of the course, you will be able to prepare data for tools such as Power BI or Tableau and apply structured data transformation techniques in real-world scenarios.

What makes this course different

This course is built around a practical business scenario rather than isolated feature demonstrations. You will not just learn KNIME concepts, but apply them step by step to solve a realistic data problem, similar to how data analysts and consultants work in practice.

What you will learn

Clean and transform messy real-world datasets using KNIME
Build visual workflows for data preparation without coding
Perform ETL processes (extract, transform, load) step by step
Combine and reshape data from multiple sources
Automate repetitive data preparation tasks
Prepare datasets for Power BI, Tableau, or further analysis
Apply basic machine learning workflows in KNIME
Explore generative AI features in KNIME
Understand when to use KNIME instead of Excel or Python

Why KNIME

KNIME is a powerful and free data analytics platform that allows you to build workflows using a visual, drag-and-drop interface. It combines the flexibility of programming with the simplicity of no-code tools, making it an ideal starting point for beginners.

Hands-on learning approach

This course focuses on learning by doing. You will start from the basics and work through a complete case study, building your own workflows step by step and applying everything directly in practice.

Additional topics covered

Data visualization with Power BI and Tableau
Introduction to predictive analytics in KNIME
Practical workflow design and best practices

Who this course is for

Absolute beginners who want to learn data preparation without coding
Excel users who want a more powerful and automated way to clean and transform data
Aspiring data analysts who want hands-on experience with real data problems
Professionals who want to move from manual data preparation to automated workflows
Anyone interested in KNIME as a no-code alternative to Python

Who this course is for:

Absolute beginners who want to learn data preparation without coding
Excel users who want a more powerful and automated way to clean data
Aspiring analysts who want hands-on experience with real data problems
Professionals who want to move from manual Excel work to automated workflows
Anyone curious about KNIME as a no-code alternative to Python

KNIME for Beginners: No-Code Data Cleaning & ETL Case Study

What you'll learn

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Course content

Welcome to the World of KNIME2 lectures • 2min

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KNIME Case Study new interface15 lectures • 2hr 53min

More KNIME fundamental concepts (classic interface used!)17 lectures • 2hr 30min

More KNIME learning options1 lecture • 1min

Requirements

Description

Who this course is for: