Month-End Report Update

Welcome to the November Challenge everyone! 🍂

A new month means new creative boundaries to push and another chance to show the community what you’ve got. Whether you’re a returning creator or jumping in for the very first time, we can’t wait to see your take!

Important Dates:

  • Submission Deadline: November 30, 2026

  • Next Challenge Launch: December 7, 2026

Without further ado, here is this month’s challenge!

Challenge Description: Every month, finance asks you to update the company’s year-to-date sales tracker. On the first of the month, you receive an export of last month’s orders from the company database. The tracker is a protected Excel template that already holds the accumulated totals through the end of the previous month. Only the yellow cells can be edited. The totals row is a live formula, and the formatting and color scale must stay as they are.

Your job is to take September’s orders (Sep 1–30, 2026), add them on top of the existing year-to-date totals, and write the updated figures back into the template. The result goes out as the October version of the report.

Your workflow should answer:

  • What are the updated YTD totals per product line after adding September, and what is the new grand total?

  • How do you get the numbers into the right cells without hardcoding a cell reference for each row?

  • What counts as “not breaking” the template, and how do you make sure you don’t?

  • How do you know the file you wrote is correct?

Bonus: The “Data accumulated” line under the title still says August 31. Update it so it matches the data you just added, and build the text from the data instead of typing it in.

Hint: Think about what should happen if the workflow runs twice, and what happens to a product line that appears on only one side of a join.

Datasets: Month-End Report Data on KNIME Community Hub

  • ytd_tracker_SEPTEMBER_START.xlsx (YTD tracker through August 31)

  • erp_export_september2026.db (SQLite, table orders, 81 orders)

Both are AI-generated sample data based on the structure of the Sample Sales Data on Kaggle