This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
Holy Smokes!? What a nice surprise to have such a positive response to my previous blog post 5 Useful Design Patterns in Alteryx You Need to Master. I had so many positive responses, both online and offline, around how users either had never really thought about Alteryx in this way or the fact that these design patterns are so simple and effective. The overwhelming request that came out from those conversations was that they wanted to see more and since I am not one to want to disturb the Alteryx Force, I will comply.
Alteryx can work with data in Hadoop in multiple ways, including HDFS, Hive, Impala, and Spark. Many of these offer multiple configuration options. The goal of this article is to present the various options available at a high level, note some performance observations between them, and encourage you to perform similar analyses to understand what works best in your environment.
Many customers strive to create a culture of analytics at their organization. Part of this process is designing a system that allows different users to collaborate in one single platform - a language as we call it. More and more, customers are turning to Alteryx to be their common language for decision making.
As we develop workflows, it is inevitable that we will need to make additional modifications downstream of our data. To see the updates, we need to run the workflow. Depending on the data, this can take a long time. Let’s be honest, even waiting a couple of minutes to re-run the workflow is too much time... Ain't nobody got time for that.
I've learned a lot about computers, business, and collaboration through visiting the grocery store. Read about one of my trips here: The Grocery Store is a Fascinating Place. What more can be learned from a trip to the grocery store? How about Data Science!
If you google (a verb) "Date Frustration," the 7th article is Date Conversion Frustration - Alteryx Community. As a follow-up to my previous blog post, Marquee Crew's Guide to Dates, I'll provide you with some tools to better handle incoming date fields and help to teach you how to convert strings to dates. Even with tools like the DATETIME macro, this can be challenging. Hopefully, you'll avoid the frustration and skip directly to happily ever after.
We can't get enough of our Alteryx ACEs - especially when they weigh in on analytics culture, bridging the gaps between the business and IT, getting started with Alteryx, and sharing their best tips and tricks. What do you get when you have 4 ACEs?... 2 pockets of pocket rockets!
A colleague of mine heard me tell this story recently. It’s a story I’ve told countless times, but never put on paper. I’ve told it in keynote addresses, one-on-one with customers, and others have adapted it for themselves. But this is the original story. This is the true story. This is a powerful story. This is a personal story. As of this writing, the actual event occurred five years ago, the last week of August. It’s story about IT, Lines of Business, Analytics, and Data. This is a story about one man's visit to a grocery store.
Alteryx Server has increasingly become more popular as analytics leaders look to scale-out Alteryx to tackle bigger projects, larger datasets and to put self-service data analytics into the hands of more decision makers. But can the Alteryx Server be deployed in a virtual environment? How many users can it handle? How does the Alteryx Server scale? I recently sat down with the Alteryx Server product management and development leads to answer these questions and more. Learn more about the top 10 Alteryx Server frequently asked questions.
How should I scale Alteryx Server? This is probably one of the most frequently asked questions we get with Alteryx Server. And if you are new to Alteryx Server, you might not be aware of the flexibility and all the options you have when it comes to scalability. In this post, I'll discuss scaling-up vs. scaling-out, and the different scalability options for adding additional worker, gallery and database nodes.