Mashup on-premise and in-cloud data with diverse formats and structures into high performance, analytic-ready data blocks for both business intelligence and machine learning. SQL databases, JSON/REST, Salesforce.com, and Google Analytics are just a few example sources.
Mashup and visualization are coupled together as one web app. The mashup engine enables you to quickly profile data and verify data manipulations via data visualization. And while building visualization dashboards, you can also quickly add data transformations for more efficient visual output.
Data mashup is not only a powerful tool for professional designers. With proper governance in place, it is also a safe, yet dynamic self-service tool for data knowledgeable users.
For online analytics, MetaDash's visualization dashboards are quick to build in a web app. Dashboards are automatically wired with deep built-in interactions and customization to enable end user analysis.
For content accessed online and distributed offline, document reporting goes far beyond simple dashboard exporting. It enables finely formatted, page-oriented design to ensure full information availability in the absence of interactivity.
Business users can be empowered with far more self-service than dashboard interactivity and customization.
Machine learning with MetaDash is designed to mashup seamlessly with human developed analysis. By facilitating mashup within a single visual user interface, MetaDash enables business users to become their own "citizen" data scientists based on professionally built models. For data scientists, productionalizing machine learning for business users has been a big barrier. With MetaDash's fully integrated machine learning + business intelligence engine, productionalizing is greatly simplified.
Driven by MetaDash's mashup software, the data mashup engine automatically generates native queries and API calls as analytic-ready data blocks. Furthermore, data blocks can be enabled as high performance, compressed cache blocks for fast in-memory acceleration. This is especially helpful when raw data volume is huge or when different data sources exhibit large performance disparity. It is common to see ten times performance gains.
MetaDash's data mashup engine is designed to scale to big data with open source Apache Spark. Combining highly efficient data blocks with Spark's well-known scalability, data processing capability is just a function of the cluster size. MetaDash's data mashup engine can even be dropped into existing Spark clusters to share computing power with other applications.
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