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A number of unique issues must be solved when analyzing big and very high-dimensional data and/or big data with discrete variables of very high cardinality. Why?
Today’s volumes make it impractical to move data to other storage locations, or to fit the entire dataset into memory for computation, for instance.
This white paper describes an approach that can help, one that combines parallelized data preparation and analysis steps performed in the data store, followed by machine learning performed asynchronously in a dedicated in-memory computing platform.
Glean the details here and learn how your team can perform the data pre-processing and analysis of big and wide data efficiently and effectively.