What’s your stack? I know there’s a lot of adjusting to client needs but I’m curious what technologies people are using here

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Tableau, Python, SQL (Oracle by default), SAS

+Trying to learn either Alteryx or Tableau Prep

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@DA Learn Alteryx before Tableau Prep. Very few clients are using Tableau Prep since it's so new and has limited functionality. Once you have the basics of Alteryx down, Prep will be super easy to pick up.

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Rmarkdown for exploratory analysis
tidyverse for data wrangling work
xgboost and caret for machine learning
for deep learning I switch to python
Deployment option varies, usually it's connect to some dashboard tool or excel. Rarely a rshiny app

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What stack is used depends on what solutions your customer wants.. or else it will look like if you have a hammer everything is going to look like a nail...

So the question by itself faulted and incorrect..

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I've never had a chance to have a stack. It changes with each engagement and I have to be flexible.

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@K1 I disagree. Obviously every component is flexible and needs to be considered, but if you’re on a team with any kind of specialization then you will have preferred tools of the trade that you are familiar with.

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Ours is typically something along the lines of:
- SciPy stack for analysis/machine learning
- Jupyter for interactive development and communication
- Spark/Hive for big structured data
- MongoDB for NoSQL stuff
- Matplotlib for viz (I know)
- Flask for serving models
- Docker for deployment and env management

Azure SQL, Power BI, R/Python

@slalom 1 The choice between Alteryx and Tableau Prep isn't up to me at this point

OP , is it meets the needs of the project certainly.. u misunderstood my comments.. if we know what type of tool then which vendor tool is a personal or client choice. I have seen many a times for example where the Hadoop stack being incorrectly used because either we want to sell that expertise or the client asks for it and we clearly know it is not the right tool. Another example is using Qlik or Tableau for enterprise reporting..

We are moving towards databricks with h2o for most analyses with a Tableau or Powerbi (the worst) front end. Being able to simplify on a few tools is helpful for us.

For some of our larger databases we are using athena. I've tried azure data lake analytics (too slow), mapd (fantastic as long as the data fits in memory), and bigquery (huge fan).

For more specialized analyses we have big VMs with tools like gurobi, Julia, etc on them. We will be setting up a R connect server to serve up smaller analyses.

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