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A solution to last week's challenge can be found here. GIPHY   Video games have become a ubiquitous part of culture over the past few decades. What started as giant consoles became miniaturized for use at home. As popularity increased and technology advanced, more titles were offered by various companies. Whether you wanted to be a frog hopping across the street, stack odd shapes falling from the sky, or defeat the dreaded space invaders, there was a game for you.    Over time, many companies innovated with different categories of games which expanded or create new genres. These games were made available for a variety of platforms - the hardware running the games.    This week's challenge is to parse the provided video game dataset into columns. ... View more

  Hi Community,   We posted the solution JSON file to Cloud Quest #42. Check it out and let us know what you think! Send suggestions to academy@alteryx.com or leave a comment below!     Let’s dive into this week's quest!   Download the provided ZIP file containing your starting data and workflow files. Upload Start Cloud Quest 43.json to your Alteryx One library. Reconnect USA Flights 2021.csv and Airport Codes.csv to the Input Data tools in your starting workflow.   For more detailed instructions on how to import and export Designer Cloud workflow files, check out the pinned article Cloud Quest Submission Process.         Scenario: Nobody likes getting stuck in an airport waiting for a delayed flight. Using the dataset USA Flights 2021.csv, which includes a random sample of flights in 2021, analyze how delays played out during the summer months (June 21 through September 22, 2021). Use the other provided datasets to complete the following:   Tasks 1. Identify the five airlines that had the most delays during this time. 2. Identify which airline had the single longest delay and the date it happened. 3. Identify the five airports with the highest number of delayed flights. 4. Count how many flights were canceled that summer.   Helpful Info [CRS_Dep_Time] = Scheduled Departure Time (local, hhmm format) [Dep_Time] = Actual Departure Time (local, hhmm format) [Dep_Delay] = Minutes between scheduled and actual departure. Negative numbers mean the flight left early.   Hint: Try using the DateTimeParse() function to convert flight dates to Alteryx date data. Data source: https://www.transtats.bts.gov/DL_SelectFields.aspx?gnoyr_VQ=FGK&QO_fu146_anzr=b0-gvzr Image: generated by Google Gemini, September 16, 2025.     Earn Cloud Quest badges:   After completing your quest, head back to your Analytics Cloud library: Download your workflow solution file. In your reply, attach both your JSON solution file and a screenshot of your workflow. Keep submitting—every solution gets you closer to earning more Cloud Quest badges!     Here’s to a successful quest! - The Academy Team     Download Start File ... View more

We hope you enjoyed last week's challenge. The solution has been posted here. For the second challenge lets look at removing characters and splitting data into columns based on delimiters.   Many products will export textual data with delimiters such as quotes. This is done so that strings can contain delimiters or control characters within them. Having more than one type of delimiter can be hard for ETL programs to interpret.  In the input text file, there are two different delimiters (double quotes, single quotes) and they surround different data types.   Use Alteryx to strip out the delimiters as superfluous and format the data as represented in the output.   You may notice that we have started classifying the exercises into beginner, Intermediate and advanced.  This classification is used by Alteryx internally to sequence exercises as users advance.    Update 11/23/2015: The solution has been uploaded. ... View more

Here at Alteryx we know the importance of growing our experience at solving data and business problems. Almost weekly (~40+ times a year) we share an Alteryx challenge with our internal Alteryx users and then we all develop our own solutions.  Later we review our solutions in small groups.  Since many Alteryx users have expressed an interest in having us share these exercises, we have created this new section of the Community. The intent is to share and exercise about once a week on Monday. Some of the exercises will be easy and some you will find more challenging.  The goal is to expose everyone to more of the things Alteryx can do and hopefully everyone will take away an idea or two on how to approach different challenges.  The following week we will post a new challenge as well as an example solution to the previous week's challenge.  We hope you enjoy the exercises. Exercise #1 Join to Range: A company in Australia has source data which is made up of a series of postal codes (eg. 2000, 2001, 2002 etc.) amongst some other data fields. They have a separate reference table which contains postcode ranges (eg. 2000 to 2002) which they would like to use to match/filter their main data. Each Customer Record needs to be joined to the Lookup table based on a Postal Area Ranged region. Then finally summarize the customer data by Region, Sales Rep, and Responder, then a count of customers.   Check and see what the result should look like by looking at the data labeled 'Output'.  Your mission is to take the input files and blend them so your result matches the output shown.  Good luck!   UPDATE 11/16/2015: I have posted a solution to challenge #1 from last week.  It is a good example to show the usefulness of the generate rows tool.  Please keep in mind that this is just one solution using Alteryx to solve the problem, there can be many other solutions and approaches to the problem.  Hopefully you had fun and learned something new in the process. ... View more

Hi Maveryx,   A solution to last week’s challenge can be found here. The inspiration for this challenge was submitted by Nathan Purvis (@DataNath)  and was based on a Community discussion. Nathan, we appreciate your active participation in the Community!   March 31 marks the end of the first quarter of the year. During the following 2 weeks of April, your company is dedicating its time to evaluating their operational processes from the first quarter and suggesting improvements aimed at enhancing efficiency in the upcoming months.   The provided dataset contains information about the working shifts of all five employees, starting from their first day of work in January 2023 (not necessarily January 1), and spanning through March 31, 2023.   Your tasks for this challenge are as follows:   Build a table that displays each employee's total shifts during this period, the number of shifts that exceed 4 hours, shifts below 4 hours, and total non-work days. For each employee, calculate the percentage of shifts that had fewer than 4 hours. Bonus Question: In the event the company decides to expand its operations without bringing on new hires, which employee(s) (Employee_ID) should be considered for additional working hours?   If you need a little help, you can review these lessons in Academy:   Summarizing Data Joining Data   Enjoy the challenge!   ... View more