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Big data era, people say the big data systems must use the scalable data structures and store.

I didn't get the point here. I need some examples.

What are the good examples of (1) scalable and (2) non-scalable data structure respectively?

Can any one give me an idea on it?

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If I understand correctly, scalable structures refer to structures that can grow with the input grows without concern for memory. Off the top of my head I'd imagine scalable structures such as a tree structure that can grow and search elements quickly, generally O(log(n)) even tho they're a bit more memory intensive.

Where a non scalable structure would be something like an array or any type of structure that has its memory statically allocated to it. While it takes longer to search, generally O(n) but has a predictable memory size. I hope this helps. Note that trees (or other structures that often are dynamically allocated) can be written as array based such as an array based heap that has both static memory allocation but O(log(n)) search capabilities.

It would make sense for big data to use scalable structures since the amount of data is by definition- big and they likely need to query it in an efficient way. I hope I understood your question and this helps.

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According to me the scalble and non-scalble data can be illustrated as follows: Non-Scalble data: The Non-Scalble data is nothing but the data for a software, algorithm, ect . Which is not in that format , need change in which that software is family .. that type of data we call it as a Non-Scalble data for that respective software, Algorithm, ect ....

Scalable Data: The scalable data is just data which has no specific format , in others words it's continuously changing but still that respective software, Algorithm, ect is accumulating or understand that; that type of data is called as the sacalab data for that respective software, Algorithm, ect.

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    Aug 31, 2023 at 22:53

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