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Hi Community members,
A solution to last week’s challenge can be found here.
This challenge was submitted by Yoshiro Fujimori (@yoshiro_fujimori). Thank you, Yoshiro, for this great challenge!
Many data analysts run multiple Alteryx workflows daily. Monitoring when each workflow runs, how long it takes, and whether it encounters any issues is crucial for troubleshooting and audit readiness. Sound familiar? Then this challenge is for you!
For this week’s challenge, you are provided with a series of Alteryx log files. Configure the Directory tool provided in the workflow start file to gather these files. Also, note that to read files from a directory, you need to use a Dynamic Input tool.
Your task is to analyze these logs, extract key operational metrics, and from these metrics, determine which workflow ran the longest.
Task 1: For each log file, extract:
Workflow name
Start time
Duration (in seconds)
Number of warnings
Number of errors
Number of conversion errors
Task 2: Across all log files, compute summary statistics:
Total number of jobs (log files)
Total combined run time (in seconds)
Total number of warnings
Total number of errors
Total number of conversion errors
Sort by the Seconds field in descending order so you can detect which workflow ran the longest.
Hint 1: Logs may vary in structure slightly; pay close attention to how time and errors are logged.
Hint 2: When joining the log files and the extracted data files, be aware of field names. You need to create new fields for this.
Need a refresher? Review the following lessons in Academy to gear up:
Sorting Data
Multi-Row Formula
Summarizing Data
Good luck!
The Academy Team
Download Start File
Download Alteryx Log Files
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The link to the solution for last challenge #34 is HERE. For this challenge let’s practice some data cleansing.
Use Case: There is a series of data cleansing processes we need to do on our data. Please solve each per the instructions.
Objective:
Remove leading zeroes
Trim leading zeroes and/or descriptive text at the end
If the data value ends with ID, remove the ID
If more than 8 chars, remove anything after 8. If only 6, add “SC” to the front.
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For this week’s challenge we can look at using Alteryx to automate a repetitive task. The link to last week’s challenge (challenge #18) is HERE.
Use Case: Customer has 100's of xls files with 1 common sheet available in all workbooks. Through one process, the user would like to read across all of the xls files and return the values contained in specific cells - Row 2, Column 3 and Row 8, Column 2 for each sheet within each XLS workbook.
The result should be a table OR browse tool containing 3 columns: XLS File, Row2_ Column3, and Row8_Column2.
You will only have 2 xls files for this challenge, Book1 and Book2, but keep in mind that the use case is for 100s of Excel files with the same schema. You won’t want to use 2 input tools since that would not scale to 100’s. Also, for all data consumption, please check the box for First Row Contains Data. This is because in the headers for an Excel file are in row #1.
Good luck and keep it simple, this should be an easy challenge!
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The link to last week’s challenge (challenge #19) is HERE. For this week’s challenge we need to clean up an unformatted text file with unstructured data.
Use Case: A sales executive got a series of leads from the DMA conference. Unfortunately it’s a text file that we want to restructure into a tabular form to load into Salesforce.
The result should be a table formatted the same as the output sample.
As always there are many ways to approach the same problem in Alteryx. I am looking forward hear about some of your solutions.
Enjoy!
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You never know where one will come across an interesting challenge.
The following was posted as a response to a post called "XML Parsing for Beginners".
"I have a file (a sample is included below) that contains information about an item. My task is to generate a list that associates the item identifier to the specific attributes, for example from this sample, I'd be looking for something along this sort of output table:
item_identifier | grade_level | subject | Depth of Knowledge | Bloom's Taxonomy | etc etc"
So, basically a table that has all of the <source><langstring> as the title and the <taxon><entry> as the value. Then to make things a little more complex, when you get to the <source><langstring>Standard, there are multiples with the same name but different values, and I need all the values."
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