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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• The visualization that Alteryx provides can be improved much more than where it stands tonight.
• It doesn't seem to be able to process Big Data as efficiently as I would have liked.
• There are a lot of things that you need to learn.

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• SageMaker does not allow you to schedule training jobs.
• SageMaker does not provide a mechanism for easily tracking metrics logged during training.
• We often fit feature extraction and model pipelines. We can inject the model artifacts into AWS-provided containers, but we cannot inject the feature extractors. We could provide our own container to SageMaker instead, but this is tantamount to serving the model ourselves.

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• Definitely, an increase in employee efficiency. With the right tools and hardware, Alteryx can reduce the processing time significantly.
• Automation and/or automatic report scheduling frees up a lot of time that can be used elsewhere.
• Licensing updates are somewhat not handled easily and quite often create issues with modules to run slowly and/or not being active.

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• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

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