Levels of Abstractions in Big Data

Levels of Abstractions in Big Data:


Mikio L. Braun:

Many of the tools like Hadoop or NoSQL data bases are quite new and are still exploring concepts and ways to describe operations well. It’s not like the interface has been honed and polished for years to converge to a sweet spot. For example, secondary indices have been missing from Cassandra for quite some time. Likewise, whether features are added or not is more driven by whether it’s technically feasible than whether it’d make sense or not. But this often means that you are forced to model your problems in ways which might be inflexible and not suited to the problem at hand. (Of course, this is not special to Big Data. Implementing neural networks on a SQL database might feasible, but is probably also not the most practical way to do it.)

While an interesting read I’m not sure I really got it—my understanding is that the author’s advise is that disregarding your backend storage or Big Data architecture, you should always think of your data and processing tools in terms of higher concepts as data structures, operations on data structures, and processing algorithms.

Original title and link: Levels of Abstractions in Big Data (NoSQL database©myNoSQL)


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