Altair SLC vs. IBM Analytics Engine

WPS Analytics allowed our company's business users to keep using their existing programs while getting similar results with much less learning time. It's a great accomplishment when software for business users is changed—excellent relationships with providers and other salespeople. As a SAS user, I needed an interpreter for the SAS language, and WPS has more packages and a lower price than any other company I could find, so I decided to go with
it.

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• Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster.
• But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion.

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• WPS Analytics is powered with high performance data processing engine.
• Reusable data models save finances and time.
• Self-service data analyzation portals.

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• Jobs with Spark, Hadoop, or Hive queries are rapidly attained
• Can collect, organize and analyze your data accurately
• You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries.

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• Currently, I barely encounter challenges when using WPS Analytics although I don't use it on a daily basis. There's no functionality or feature that needs more improvement.

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• Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration.
• Bundling of the Cloud Object Storage should be included with the Analytics Engine.
• The inability to add your own Hadoop stack components has made some transfers a little more complex.

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I use a lot of data discovery and visualization tools at workstations to help me gain deep insights into data. WPS Analytics doesn't vary much from all the DVD tools that I have listed, I always go for the best.

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We initially wanted to go with Google BigQuery, mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM. Apache Spark was also in the running, but here IBM's domination in the industry made the choice a no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.

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• Using machine learning and natural language to analyze data saves time and effort.
• Standard reporting and business intelligence help us reduce workload and make data-driven decisions.
• Drag-and-drop workflow saves time when different languages like SQL and SAS make it easier to code and prepare data.

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• This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place.
• IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI.
• The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners.

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