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Alteryx Analytics Gives Users Self-Service Data Analytics on Azure

Alteryx Analytics provides analysts the unique ability to easily prep, blend and analyze all of their data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours not weeks.

Analysts can connect to and cleanse data from data warehouses, cloud applications, spreadsheets and more. Analysts can also easily join data together, then perform predictive, statistical and spatial analytics using an intuitive user interface, without writing any code.

To learn more about how Alteryx helps streamline and simplify data analysis, check out the datasheet.

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  • Nicolas CaudronNicolas Caudron

    Link to Alteryx Designer on the Azure Marketplace
    https://azure.microsoft.com/en-us/marketplace/partners/alteryx/alteryx-designer/

  • Deactivated UserDeactivated User

    Comment removed at user's request.

  • Håkan SöderbomHakan​Soderbom2 Let's start a conversation

    The Microsoft Data Insights Summit just ended and there were two questions that came up repeatedly from participants at the conference:

    • Can I run Alteryx Server on Azure?
      Yes, it is easy to install Alteryx Server on a virtual machine in Azure. At this time we don't provide a provisioned vm on the Azure Marketplace but it is easy to deploy the Alteryx Server manually. In any case, our recommendation is to start with Alteryx Designer that is available on the Azure Marketplace. Server only becomes necessary for cross-team collaboration and *really* big data volumes.
    • Doesn't Power BI Desktop compete with Alteryx Designer?
      Since both Alteryx Designer and Power BI Desktop do data ingestion it is easy to think there is overlap, however there is almost no overlap at all. A more relevant comparison is between SSIS and Alteryx Designer. However the big difference is that Alteryx Designer is focused on the self-service analyst with an easy-to-use drag-and-drop interface, i.e. there is no scripting or coding. Alteryx Designer is also more focused on experimenting with the data, including working with the data in a group, testing different workflows - the visual workflow is perfect for analysts working together whereas scripting isn't usually suitable for group work.

     

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