MetaDash's Style Intelligence can natively access big data stores such as Cassandra, Hbase, MongoDB. This is because Style Intelligence's big data deployment is natively built upon Apache Spark/Hadoop. It can not only be dropped into an existing big data environment, but it can also be deployed with its self-managed environment
MetaDash's Style Intelligence drops into an existing Apache Spark installation. This bring-the-software-to-the-data approach eliminates costly big data movement for analytics and reporting. Style Intelligence can also be deployed with its own built-in Spark cluster.
In this case, only minimal expertise in Spark is required. The cluster is mostly configured and administered by Style Intelligence behind the scenes to maximize data processing and mashup performance.
Big data platforms have made data lakes possible where data, mostly in raw format, is stored for future analysis. Data lakes have become a viable, sometimes even preferred, alternative to data warehouses. Since they operate off raw data by design, the approach for data analytics comes from a very different direction. MetaDash's native Spark integration makes data lakes accessible like all other data sources.
MetaDash's machine learning component gives business users direct access to machine learning models. For data scientists, it provides a platform to quickly productionalize models that have succeded experimentally. As part of MetaDash's mashup engine, machine learning output can be readily mashed up with human designed analysis.
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