Data gravity occurs when the volume of data in a warehouse increases and the number of uses also increases. In some cases, copying or moving data can be troublesome and expensive. Therefore, data tends to pull services, applications and other data into its warehouse. Primary examples of data gravity are data warehouses and data lakes. Data in these systems is inert. Scalable volumes of data often break existing infrastructure and processes, requiring risky and expensive fixes. Therefore, the best practice is to move design processing to data, not the other way around.
Connectivity analytics is an emerging discipline that helps to explore the interrelated connections and effects between people, products, processes, machines, and systems within a network by mapping these connections and continuously monitoring the interactions between them.
Dark Data is the name given to data that companies collect but is not used, analyzed, or evaluated.
Julia is an open-source, dynamic, high-level programming language designed for high-performance scientific computation and data analysis. Started in 2012 at MIT by Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman, Julia is designed with the philosophy of “write fast code, run fast”.
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