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With version 10.0, Alteryx extends our in-database capabilities to include support for platforms that came up in discussions with customers, prospects and partners as related to their ‘big data’ use cases, specifically Amazon Redshift, Cloudera Impala, Teradata and Spark. With the emergence of ‘data lakes’ as a single repository hosting multiple datasets, users face the need to blend and analyze larger volumes of data that would be inefficient to move around, making it the perfect use case for Alteryx in-database analytics.
I had been with Alteryx for about two months when I started hearing people talk about CEF [Chromium Embedded Framework]. I did a little digging on my own, and true to its name, found that CEF was a framework for placing chromium based browsers within applications. Shortly after, I learned I would have a chance to use this technology myself for the upcoming release of Alteryx.
It seems that much of the vast appeal of Alteryx to so many people is the degree to which workflows remove the programming part of data analysis. This makes it easier to focus on the data while trusting that whatever method is being used to analyze the data is done effectively and efficiently. Additionally, the visual clarity of workflows lends itself well to anyone looking at a new workflow trying to grasp what's going on.
That's the good news. The bad news is I build and maintain the content for the Crew macos in my own time and as eagle eyed follows will have noted have not released a new version for a whole year now...
So to the topic of this post: Feeling inspired by the 10.0 release and the fanatastic new community we have here I am going to release the 2015 Q3 release. Which by my reckoning gives me to the end of this month (or there abouts ;) ). I already have a few bug fixes done locally and a few more listed to fix on the github page https://github.com/AdRiley/ChaosReignsWithin-Alteryx-Macros/issues but if you know of any others then please let me know in the comments below. Even better, send me a fixed version of the macro and get yourself on the credits for the next release!
Developing resource-intensive workflows can be a challenge when testing changes involves running the workflow through iterations that may take several minutes or more before being able to see results. This post walks through using the Cache Dataset macro to develop workflows in a smarter way, avoid repeated long run times, and speed up the process of blending and analyzing larger sets of data.