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5 Approaches to Scalable Storage Solutions

Thinking about the scalable storage solutions market, I’ve ended up dividing it in 5 categories:

  1. NoSQL databases

    • they abandoned all relational model concepts, building everything from scratch
    • most of the solutions were born to solve practical problems
    • they make a set of trade offs to achieve scalability
  2. New age RDBMS implementations and appliances

    • they preserve almost completely the relational concepts, but are building everything from scratch
    • sometimes they are required to make trade offs to achieve scalability
    • they try to preserve existing APIs or connectivity models
  3. Classical RDBMS

    • these well know solutions are either moderately scalable or not so cost effectively scalable
  4. Scalability layers on top of Classical RDBMS

    • they reuse an existing RDBMS or engine and create different layers on top in an attempt to achieve scalability
  5. Repurposed/Repositioned solutions

    • the products in this category have started in a different space, usually caching or grid computing
    • benefiting from Moore’s law, they usually tout the “RAM is the new disk slogan”
    • most of the time they use (memory) replication to provide durability
    • some have added disk persistency as additional layer of the original product
    • having all data in memory, they are usually able to mimic various access patterns (including SQL or key-value APIs)

Any other categories I’ve left out?

While not dismissing any of these, I’m more interested in solutions in categories 1 and 2 above.

Original title and link: 5 Approaches to Scalable Storage Solutions (NoSQL databases © myNoSQL)