Engine Works Blog

Under the hood of Alteryx: tips, tricks and how-to's.
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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.

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These days, if you're following any of the "big data" trends, you've almost certainly encountered a dictionary's worth of new terms that seek to explain what big data is and why it matters to you. Terms like "distributed", "MapReduce", "in-memory", and "shuffle", among many others. All combined, these terms create a kind of standard language that allows us to describe the problem space that big data presents in a way that is universally meaningful. That's great, right? Except that if you're really new to the whole big data thing (or even just the regular data thing) to begin with, the suggestion that any of these terms are "universally meaningful" is likely going to annoy you, at the very least. I hear you, believe me.

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Last Friday was a very busy day for several of us at Alteryx in the wake of the announcement that Microsoft and Revolution Analytics had agreed to have Microsoft acquire Revolution Analytics. In this post I won't go into the Alteryx angle of this story, other than to say we think this is a net positive. Instead, I wanted to provide a few words of appreciation for what Revolution Analytics has done for both R based technology and for their non-technology contributions to the R community since its creation (as REvolution Computing) in 2007.

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Time series data is used in many of the industries we work with here at Alteryx. In some cases, we start with data that’s been measured by day and find that it’s a little too granular. The obvious solution here is to start rolling up the daily data into larger groups and experimenting with different aggregation levels, but this can quickly become difficult and/or tedious depending on the kind of aggregation you’re trying to do.

 

With that in mind, I decided to make a tool that would handle a lot of the work for you. The Date Aggregator allows you to take per-day time series data and roll it up into all sorts of different intervals.

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People say you can spend upwards of 60% of your time “preparing” your data rather than analyzing it. Alteryx Designer has great, user-friendly tools to try to cut into that estimate, but the tools are purposefully generic so that they can be applied to as many situations as possible. If you find yourself applying the same sequence of tools over and over to get your data clean, perhaps a macro is in order.

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If you’re one of those people who develops all your macros flawlessly the first time, congratulations: you don’t need the X-Ray Browse Macro. (And we’d love to get your resume.) This macro is designed for the rest of us: humans.

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Have you ever noticed that you can't do a left join or a right join in the traditional sense in a single step with Alteryx?

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There was a recent question on the Alteryx forum: How to use the percentile in summarize.The question misunderstands the percentile function in the summarize and is looking for something slightly different, although with similar math.  So what does the percentile in the Summarize tool do?

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In Part 1 of this series, I talked about the development of a web scraping module that would download historical stock data using a Python script and then host it in a cloud database using Quandl's "Quandl Toolbelt" application. Once I got this version of the module up and running, I realized that there were two big areas for improvement...

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We are very excited to be able to announce the launch of the Macro District in the Alteryx Analytics Gallery! 

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new_icons_downscale.pngThe process of expanding the capabilities and improving the performance of Alteryx's predictive analytics tools continues with Alteryx's 9.1 release. Specifically, we have added new tools for data investigation, predictive modeling, and time series analysis, and have made improvements and added new features to a number of existing tools as well, including the AB testing tools. Here, these additions, improvements, and enhancements are described in more detail.

 

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Web Scrape Icon

What if the data has yet to be fully collected, much less structured, and you need to get to it without driving yourself crazy? More often than not, this is the case when it comes to trying to pull data off the internet and turn it into something a little more analytics-friendly, a process known as “web scraping”. It turns out that between the Download tool and all its great data manipulation tools, Alteryx actually makes for a really good web-scraping platform in addition to its wealth of other uses.

 

 

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Is Fuzzy Matching a mystery to you? You're not alone. Do you wish it were easier? Of course--who doesn't like things to be easier? Let's learn why and how we use this powerful methodology, and meet a new friend that makes it easier than ever: the Fuzzy Unique Macro!

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Summertime, and the livin’ is easy. Fish are jumpin’ and the cotton is high. 

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As with any real-world data source, nothing is perfect. Things can be dynamic, unsorted, or even encrypted, so it is useful to have the tools and skills to work around these issues. Alteryx comes in handy, as it gives its users the customization and data modification tools to make this problem-solving go a lot quicker.

 

There are three main topics covered:

  •  Automating the download of multiple files
  • Unzipping compressed data
  • Formatting the data to be read correctly into Alteryx
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Join us at the Solutions Center throughout the conference, where we will have short, conversational demonstrations every 30 minutes. These sessions will be enlightening and loaded with tips, best practices, and wonderful nuggets of information on how you can leverage Alteryx to work smarter and produce better solutions.

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The 9.0 release of Alteryx Predictive Analytics adds several new tools to our predictive analytics workbench, but much of the development effort for this release has been devoted to improvements that are under the hood.

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Batch macros can seem very intimidating, but they are really quite easy to build once you understand how they work.

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A variety of questions have come up from Alteryx users that all have the same answer:

  • The Alteryx Gallery will only allow me to upload about 100MB of data, how do I upload more?
  • I need to change my data daily/hourly/monthly for my Gallery module, how do I do that without re-uploading my entire app every time?
  • How do I persist data from run to run in a Gallery module?
  • How do I share data in Alteryx Desktop with coworkers/clients/partners without sending huge files around?

The answer to all of these questions is to use the Amazon S3 tools. What is S3?

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I get a lot of the credit for Alteryx – the product. Having been the original author and still the lead architect that makes sense, but there are a whole host of people who deserve the credit as much or more than I do.

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Back in July Alteryx released Alteryx for Visual Analytics; a version of Alteryx Desktop specifically tailored to deliver data natively to Tableau Software.  Since then I have begun to explore what Tableau is all about and in my opinion the two products are a beautiful pairing:  Alteryx allows you to do all of the preparation, heavy lifting, cleaning, blending and reformatting of your source data; and Tableau provides you with beautiful interactive visualizations and dashboards of that data.

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A few months ago I wrote about an experiment using TomTom’s Geocoder API.  Well, after a bit of time and a few requests, we decided to conduct a new experiment and try building out a reverse geocoder, only this time it would be specifically built for the desktop (no API involved). 

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As I am drawing to a close in exploring interactive charting in Alteryx, I wanted to create a macro for the people who say “Can’t I just have it now?”

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I have been working on a blog post that is going to integrate a variety of different aspects of Alteryx together to paint a larger story. As I have been building towards it, I have run into a variety of challenges that have grown into blog posts in their own right. It started with Weighted Medians and continued on with Downloading from TIGER. Today I ran into a very common problem – I needed to process records in groups. The solution I outline is extensible to any macro; it should be a useful technique for any data artisan.

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Continuing the series prototyping HTML based visualizations, I want to look at spatial data. One of the strengths of Alteryx is that spatial data is just data. It doesn’t claim to be a GIS product, it just assumes that every organization has spatial data and would like to process it with the rest of their data and specifically they don’t want to have to use multiple products to do it.

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Food Deserts, areas that are a longer than normal distance to grocery stores, have been an ongoing topic in politics and demographics for a while now. I first heard about the concept when Rahm Emanuel started talking about it in his run for mayor of Chicago. I have continued to see articles and blog posts about it and every time I think that Alteryx would make it much easier to create a more nuanced analysis.

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In the last 2 weeks, I have introduced (as a prototype) the idea of embedding HTML visualizations into Alteryx. If you have not read them you can read Part 1: JSON Data Output and Part 2: HTML5 Visualizations. In last week’s post, I explored a generic HTML 5 output tool and a sample visualization based on NVD3. Although I was able to return a very nice visual from NVd3, all interactivity was lost. This week I want to show a prototype with the interactivity working the way it should in NVD3.

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Last week I introduced potentially new visualizations for Alteryx based on HTML5 and its associated parts like JavaScript and SVG. I started with a macro to write JSON data files from Alteryx. This week I continue the prototype with a macro to actually render HTML 5 into an Alteryx report.

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I am working on a prototype for Alteryx of some new charting/visualization ideas. For most prototypes we do the audience would be strictly internal, but this time I am going to try something different. As I move forward developing the prototype, I am going to be sharing it with the readers of this blog in order to get as much feedback as I can as early as possible in the process.

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I love maps.  A picture is truly worth a thousand words.  Creating a great map is writing a story.  Sometimes that story tells the viewer what to think and sometimes it conveys information that lets them reach their own conclusion.

 

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