How does governance work in Alteryx? From data access and data management, to data security, we've got the answers to your most pressing questions on how to best govern your analytic applications in Alteryx.
In the blog, “Top 10 Alteryx Server FAQs”, one of the top frequently asked questions on managing an Alteryx Server, is how to monitor the deployment. Whether it’s to adhere to a data governance program, to track your usage and ROI or how to best optimize your Alteryx Server, administrators need and want monitoring and reporting capabilities. To help with this, we are pleased to announce the availability of a new Alteryx Server Usage Reporting app. This workflow provides valuable data and insights to user access content, scheduled jobs and job analysis information about your server and can be visualized in either a Tableau Workbook or to a set of PDF and Excel files. You can begin your download by going to downloads.alteryx.com
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.
This post demonstrates tuning a Private Server installation by measuring total throughput for various configurations. We’ll look at performance implications of scaling “up” versus “out,” and how a low-cost hardware upgrade may have a significant positive impact on performance.
You’ve blended your data. Cleansed it. Trained your model. Made more models. Tweaked them. Compared them. You’ve picked THE BEST model. It’s perfect. Now what? You need to get the rest of your organization using your model in a live production environment.