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Engine Works Blog

Under the hood of Alteryx: tips, tricks and how-tos.
Alteryx
Alteryx

Recently, there has been much talk around Creating a Culture of Analytics. The goal: use analytics to make better decisions as a company. 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. Dean Stoecker, the CEO of Alteryx, often states that Alteryx is a “Code-free and code-friendly platform,” and this is something our customers truly take to heart, turning Alteryx into the go-to platform that allows coders and non-coders to interact.

 

What does a Common Language across the enterprise look like? I would like to discuss two unique styles that I have seen from Alteryx customers:

 

  1. Language Agnostic: Many customers are agnostic to its users being code-free or code-friendly. Each user has the freedom to decide the best way to solve their use case. Alteryx is used as the glue that holds everything together, allowing users to collaborate in one platform.
  2. Defined Language: Other customers are code free enterprises at their core. All analysis is performed using the GUI in Alteryx Designer, making work easily replicable and understandable across their user bases. The code-friendly nature of Alteryx is used to expand on the GUI capabilities, performing advanced tasks such as custom model comparisons in R.

 

No matter which style our customers choose, they love Alteryx because they are not bound to one language or one way of doing things. Their users can focus on solving actual use cases in a common language and continue to build a culture of analytics in their organization.

 

The common language of Alteryx allows our customers to continually innovate with new technologies from a single framework. One great use case for this would be for reading Parquet files – a compressed columnar data format – from Hadoop.

 

Currently, the Input Tool only reads .csv and .avro file formats from Hadoop. With the Python Tool, users can leverage the code-friendly nature of Alteryx to connect to Hadoop, read the Parquet file in as a Pandas DataFrame, and pass the data to Alteryx.

 

David Hare discusses how to use the WebHDFS API to load Parquet files into Alteryx in his Parquet, will it Alteryx? blog post. This example will show you how to load Parquet using Spark via the Livy API:

 

livy_Api.PNG

 

The above code could easily be wrapped up into a custom tool using the Python SDK, allowing code-free users to access the same code.

 

The resulting data is a list of NYC addresses that we can easily graph in Alteryx:

 

addresses.png

 

Now, this data has ~20,000 addresses, too many to get useful information out of one graph. Perhaps we are interested in just addresses in Times Square. Code-friendly users could modify the Spark code, using the SparkSQL LIKE statements to extract only the Times Square addresses from Hadoop:

 

sparksql2.png

 

Code-free users can use the Regex Tool in Designer to graphically perform the same task without having to know Spark structure or syntax:

 

parsing.png

 

table.png

 

Either way you do it, the result is the same, a map of addresses in Times Square:

 

ts.png

 

This is the power of using Alteryx as a Common Language. Users can solve problems with the technology they see fit with one central tool, Alteryx. Customers will often turn their work into Analytic Apps for end-users, requiring no Alteryx or coding knowledge to consume the information.

 

As you begin building a culture of analytics, look to use Alteryx as the common piece for all of your disparate data sources, technologies, and analysis tools. With the Parquet example, typically companies will use one tool to extract the Parquet, one to prepare the data, and another one to visualize it. All of this is now accomplished via your Common Language, Alteryx.

Comments
Alteryx
Alteryx

Great write up @AndrewKramer, couldn't agree more. I'll add my few nuggets here:

 

When it comes to digital transformation, I've seen Alteryx has the great impact on an organization efficiency and effectiveness. When people from different line of business interact, work together and fully leverage their unique strengths, skills, and especially business acumen to contribute to common data challenges and opportunities. When everyone is able to contribute not only are you able to apply the best from each person, group toward the challenges but this will also fuel cultural change because everyone feels more engaged, more valuable and there’s also a greater sense of community and team


I've worked with many orgs, and often times I see them stuck in their 'status-quo' tasks. I ask them "why do you do this process the way you do it?" 9/10 I hear, this is how I've been taught to do it or this is how we've always done it. The Alteryx platform helps tackle these types of inefficiencies. 

 

The concept, that the Alteryx platform acts as an accelerator may sound grandiose or skeptical, but this is exactly what happens when the platform is deployed. In and of itself, this kind of paradigm fuels cultural change along and enables both speed and sophistication at the same time.

 

A perfect example is these use cases by JPMorgan Chase and Signet Jewelers where both witnessed this exact digital transformation within their organization with the Alteryx platform.

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