KNIME data cleaning challenges

Learn to tackle data cleaning challenges by applying data wrangling techniques to messy real-world data. Transfer knowledge to your own projects and have fun applying it while growing your skills.

tackle a KNIME data cleaning challenge by generating complete date ranges per project, joining with costs, and filling gaps with the previous value using a loop and missing value steps.

Explore splitting a long sentence into 100-character lines in KNIME data cleaning challenges by building a workflow with cell splitter, length, moving aggregation, and group by to concatenate lines.

Execute an approximate match in KNIME by concatenating two tables, sorting by start, filling missing values from the previous value, and applying a rule-based filter to map to lower threshold.

Build a KNIME workflow to compute, for each customer, how many stay days are weekends versus weekdays by generating daily date ranges, extracting weekday names, classifying weekends, and aggregating results.

Learn to check for missing flow variables in KNIME using a Python note and a try-catch note to route results based on variable availability.

Explore the available information challenge in KNIME: read data, split by pipes, split comma-separated values into lists, remove brackets with regex, apply one-hot encoding, and group to show availability.

Perform an approximate date lookup in knime by using a dictionary to map input dates to record numbers within date ranges, using group by, group loop, and row filter.

Explore a knime text processing challenge that removes target phrases from a source table using a dictionary replacer and entity-aware dictionary tagging to handle multi-word terms.

Map descriptions to keywords in a KNIME workflow using joins, wildcard matches, string manipulation, duplicate filtering, and aggregation to attach keywords back to each description.

Learn to clean misspelled data in Knime by using similarity search to map source to reference names, configure Levenshtein distance, and join results to replace misspellings with correct names.

Learn to fix date conversion issues in knime by using string manipulation and regex to normalize date formats and convert them to dates with the string to date time node.

Knime data cleaning challenge demonstrates building a molecule workflow to count atoms per molecule. It uses string manipulation, regex, cell splitter, and pivot to produce a zero-filled final table.