Icon

Data Export

Data Merging Labs

Data Transformation Labs

Data Cleaning Labs

Lab 6: Cleaning Missing Ratings in Movie Table

Lab 2: Filtering Incomplete User Data

List File/Folder Node

Lab 4: Removing Duplicate Movies

Compress/Decompress files/folder Node

Lab 1: Correcting Incorrect Rating Types

Lab 8: Fixing Numeric Columns in Fooditemsize

Lab 13: Removing Irrelevant Columns from Payment Data

Lab 14: Dropping Columns with Too Many Missing Values

Lab 7: Validating Payment Dates

Lab 9: Formatting User Names

Lab 10: Standardizing Movie Genres

Lab 11: Filtering Out Unrated Movies

Lab 12: Filtering Failed Payment Records

Lab 2: Deriving Total Booking Cost with Math Formula

Lab 3: Splitting User's Name

Lab 4: Loyalty Points Categorization

Lab 5: Structuring Food Orders for Analytics 

Lab 1: Standardizing Movie Ratings and Titles

Lab 6: Creating Booking Identifiers

Lab 7: Creating Booking Cost Categories

Lab 8: Normalizing Ticket Scans and Downloads

Lab 9: Deriving Show Timings and Day Segments

Lab 10: Normalizing Movie Ratings

Lab 2: Enriching Bookings with Movie Details

Use the Value Lookup node to add movie titles to booking records by looking up show details.

Lab 1: Joining Food Orders to Bookings for Revenue Insights

Use the Joiner node to link Foodorder and Booking tables creating a unified view for analyzing per-booking food spend.

Lab 3 : Stacking Filtered Bookings for Trend Analysis with Concatenate

Use Concatenate to stack two filtered subsets of the Booking table (recent vs. older bookings) for time-based trend analysis.

Lab 4 : Outer Joins for Comprehensive Booking Analysis with Joiner

Use Joiner with full outer join to merge Booking and Show tables, identifying unmatched records for data integrity checks.

Lab 5 : Multi-Table Join for Food Revenue per Movie with Joiner

Use Joiner to combine Foodorder, Booking, and Show tables, linking to Movie for per-movie food revenue analysis. This merges UNOX's Level 2/3 tables (Foodorder → Booking → Show → Movie), revealing trends like "top food-selling films" for inventory management.

Convert Rating Data Type
String to Number
Categorize ShowSegment
Rule Engine
Fill Missing valuesas Mean
Missing Value
Read movie.csv
CSV Reader
Extract Hour of the Day
Date&Time Part Extractor
Remove Duplicate Movies
Duplicate Row Filter
Remove Duplicate Movies
Duplicate Row Filter
Normalize Ratingson a scale of 0-5
Normalizer
Read booking.csv
CSV Reader
Convert Rating Data Type
String to Number
Re-organize Columns
Column Resorter
Calculate GST
Math Formula
Calculate Final CostTotal + GST
Math Formula
Export thedata inexcelformat
Excel Writer
Remove Duplicate Movies
Duplicate Row Filter
Fill Missing valuesas Mean
Missing Value
Export the data in CSVformat
CSV Writer
Read movie.csv
CSV Reader
Handle missing ratings.
Missing Value
Read user.csv
CSV Reader
Filter out rows missing both email and phone.
Rule-based Row Filter
Filter Older Bookings (Before 2025-03-01)
Rule-based Row Filter
To label all recent bookings as part of the "Recent" period
Constant Value Column Appender
Rename Columns
Column Renamer
Sort bytotal_cost
Sorter
Derive scan_status based on download and scan information
Rule Engine
Read booking.csv
CSV Reader
Categorize Booking Category
Expression
Convert to Date&Time
String to Date&Time
Convert to Date&Time
String to Date&Time
Remove any duplicatebookings
Duplicate Row Filter
Sort tickets by scan status
Sorter
Convert booking_datetime to Date&Time
String to Date&Time
Read ticket.csv
CSV Reader
Read booking.csv
CSV Reader
Read foodorder.csv
CSV Reader
Filter Recent Bookings (After 2025-03-01)
Rule-based Row Filter
Read ticket.csv
CSV Reader
combine both filtered datasets vertically
Concatenate
Read foodorderitem.csv
CSV Reader
To label all older bookings as part of the "Older" period
Constant Value Column Appender
Convert to Date&Time
String to Date&Time
Convert to Date&Time
String to Date&Time
Remove any duplicatebookings
Duplicate Row Filter
Categorize a User intoMembership Category
Rule Engine
Add INR to total_cost
Expression
Read user.csv
CSV Reader
Calculate total_item_cost
Math Formula
Rename price_at_timeto Unit Price
Column Renamer
Read movie.csv
CSV Reader
Change Column Names
Column Renamer
Remove Duplicate Movies
Duplicate Row Filter
Remove duplicate movie entries based on title.
Duplicate Row Filter
Split into first &Last Name
Cell Splitter
Convert Rating Data Type
String to Number
Read booking.csv
CSV Reader
Read movie.csv
CSV Reader
Read movie.csv
CSV Reader
booking.csv
CSV Reader
Fill Missing valuesas Mean
Missing Value
Convert Rating Data Type
String to Number
Sort as perMovie Ratings
Sorter
Convert string show_datetimeto date time
String to Date&Time
Read show.csv
CSV Reader
Calculate Total Revenue =total booking cost+total food order cost
Expression
Readshow.csv
CSV Reader
Read membership.csv
CSV Reader
This filters out sensitiveor unnecessary details.
Column Filter
Normalize name formatting
String Manipulation
visualize revenue trends across booking periods
Pie Chart
Read payment.csv
CSV Reader
Readbooking.csv
CSV Reader
Convert transaction_datetime to KNIME Date/Time
String to Date&Time
this output shows shows that have no bookings
Row Filter
Keeps only columns that are sufficiently complete.
Missing Value Column Filter
Rename Columns
Column Renamer
Read payment.csv
CSV Reader
Readfoodorder.csv
CSV Reader
String Manipulation
Convert stringto date time
String to Date&Time
Convert Rating to Decimal
String to Number
Convert string order_datetimeto date time
String to Date&Time
Read user.csv
CSV Reader
Removes duplicate keys
Duplicate Row Filter
Read movie.csv
CSV Reader
Combines Booking and Show data while retaining all records
Joiner
To aggregate total food sales per movie, identifying top food-revenue films
Column Resorter
Read payment.csv
CSV Reader
Select Failed Transactions
Row Filter
Convert string booking_datetimeto date time
String to Date&Time
This will keep only valid, non-null rating rows.
Row Filter
Convert string booking_datetimeto date time
String to Date&Time
Read movie.csv
CSV Reader
To enrich the data with show timing and movie linkage
Joiner
Fill Missing Ratingas Mean
Missing Value
Readbooking.csv
CSV Reader
Remove any duplicatebookings
Duplicate Row Filter
String to Number
To associate each food order with its corresponding booking details
Joiner
Create a Booking Identifier
Expression
To visualize which movies generate the highest food sales
Bar Chart
To attach movie information (title, genre) to every food order, enabling movie-level analysis
Joiner
Read payment.csv
CSV Reader
Unzipthefiles
Decompress Files
Create a ConstantColumn
Constant Value Column (deprecated)
Compress files
Compress Files/Folder
Table Writer
Table Reader
Compress folders
Compress Files/Folder
Lists theno. of filespresent in the folder
List Files/Folders
Connecting tounox db
MySQL Connector
DB Updater
Lists theno. of folderspresent in the folder
List Files/Folders
Joiner
Remove any duplicate bookings
Duplicate Row Filter
shows only bookings that don’t have a matching show
Row Filter
Fill Missing valuesas Mean
Missing Value
Read movie.csv
CSV Reader
Rename Columns
Column Renamer
Convert show_datetimeto KNIME Date/Time
String to Date&Time
booking.csv
CSV Reader
Value Lookup
show.csv
CSV Reader
Read movie.csv
CSV Reader
Convert rating column to numeric
String to Number
Join booking &foodorder onbooking_id
Joiner
Convert rate column to numeric
String to Number
Replace missing or invalid numeric ratings
Missing Value
Round Final_Cost upto 2 decimal places
Number Rounder
Read fooditemsize.csv
CSV Reader

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: gazal
Created at: 2025-10-29
On NodePit since: 2026-06-09
Last update: 2026-06-09
Created with KNIME version: v5.8.0

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!