I've seen a lot of clients trying to use Alteryx for data engineering. I've always seen the tool used more for data prep and some quick cleansing for reporting, but not really engineering work. Any thoughts on using the tool for this?

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Same here. A tool that is somewhat good at wrangling, somewhat bad at data science and average at visualization.

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This guy dashboards

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I’ve been out industry for a while but it felt like everybody started manually coding again in python rather than using low code tools. Is this still the case?

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M1: We have our own share of challenges today!

My opinion, at least with pwc is that they’re trying to make workers be “techy” and use alteryx as a tool to teach everyone how to wrangle data. I personally don’t like this low code no code approach but for most of the roles in PwC it gets its job done. It’s kinda clunky to use though and I can’t imagine industries use it as much since the licensing is also very expensive

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Bad data quality from the clients is what keeps most of us employed.

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I think there are two areas where alteryx shines.
1) geospatial analysis. It just works for this which is nice. It has to be one of the best tools for making a large number of isochrones.
2) packaged data sets. The geospatial data sets that are ready to use are very attractively priced for what they are.

It's ok for simple analysis. If they had something similar to KNIMEs loops I'd like it much more.

That said I spend most of my time in programming languages or SQL.

I do sometimes use alteryx to take a snippet of code and package it in a macro so non-technical coworkers can use it. It's good if the macro isn't too complicated. It can be annoying add there isn't any way to propgate the output of the code downstream like alteryx likes. It's a huge gap versus knime which is more like a repl.

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@KPMG1 sure. Except I find those macros annoying. Knime is much more flexible. It has proper loops, while statements, conditional branching, you can pass variables etc. It is generally slower than alteryx unless you use work for data processing but there is no reason not to use spark. It isnt as nice when it comes to geospatial or packaging up macros but it's better for everything else imo.

I have trouble finding the use case for it, to be honest.

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We use it for data ingestion and extraction when we can’t output queries to excel due to memory issues but yea not sure about anything beyond that

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What do you mean by data engineering? Not clear based on the responses that people agree to what that means.
For me Alteryx is a great data cleaning and prep tool. It is not good at visualization but integrates well with tableau so that isn’t an issue. It is also below average at modeling so the only reason to model in it is proof of concept models.
In a word, it’s a great tool for consultants that move from client to client, get client data in different forms and formats, and need to show something understandable to clients.
I don’t know what data engineering I would do in it? Why would I set up things in production in it or set up the pipes to move data for it? I just don’t see why I would use it.

I've used it for data engineering and orchestration before. Probably not my first choice but the project had a large geospatial component and since it could do both we ended up using it. It worked reasonably well and 5 years later it's still running jobs.

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