Alteryx Analytics vs Amazon SageMaker

• Alteryx provides an integrated workflow management environment for data blending, analytics, and reporting. It is also very extensible to be connected to a variety of data connections including major databases (Oracle, etc.), analytics tools (R), and visualization tools such as Tableau through its plugins. Our department used Oracle and Tableau already and it was well integrated with Alteryx.
• The workflow authoring environment in Desktop is easy to use so that most of the users (if they are a bit skilled data developers or analysts) may not need the training to use its basic functions. Drag and drop interface and icons are intuitive to understand.
• When running Alteryx workflows, it is easy to monitor and track what's the progress by seeing percentages. It is helpful to balance and manage bandwidths of database servers, especially when to populate large data sets.

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• SageMaker is useful as a managed Jupyter notebook server. Using the notebook instances' IAM roles to grant access to private S3 buckets and other AWS resources is great. Using SageMaker's lifecycle scripts and AWS Secrets Manager to inject connection strings and other secrets is great.
• SageMaker is good at serving models. The interface it provides is often clunky, but a managed, auto-scaling model server is powerful.
• SageMaker is opinionated about versioning machine learning models and useful if you agree with its opinions.

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• Excel reporting - reports in PDF are fairly straightforward, but trying to output results in Excel can prove to be tricky/ugly.
• Chart creation - current chart tool is very basic; needs to be a way to customize the look/feel of a chart more, as well as an easier way to incorporate "big" data that can be easily reported on/viewed.

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• Amazon SageMaker is a great tool for a data scientist, although surprisingly, comparing different machine learning models with SageMaker is not as easy as one would think. I think Amazon needs to team up with a data scientist who does ensemble modeling.
• Because SageMaker is targeted for machine learning models, other models a data scientist might use require more effort to get them incorporated. My guess is Amazon is moving to make SageMaker a more complete tool.
• SageMaker can take a long time to run on larger data sets. That's the case with every big data science tool I've used, but SageMaker doesn't seem to be as quick as other tools.

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• The ease of use of this tool allows your data team to get off the ground quickly and efficiently.

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• SageMaker was a positive return on investment for those analysts that wanted to use new tools and had the aptitude to implement machine learning models with new resources. Such individuals were quick to improve their productivity with SageMaker.
• SageMaker was not a useful tool for analysts used to running reports on small data. Machine learning is a new area, and analysts that are only marginally interested in machine learning will find SageMaker not worth the effort.
• When used in combination with other machine learning tools, SageMaker is a perfect addition because it complements the Amazon ecosystem while the other tools provide more simple model development environments.

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