I don't know if GEICO has consistency issues but big data does and I get the feeling the IT media and software vendors are trying to hide that under a rock.
Big data does a few things really well. Specifically, write data and scale. Sounds awesome, huh? Well, hold on. In order to do this it has many, many copies of the data stored all over the place. Sometimes the copy A is updated and sometimes copy B is updated. The point is to be able to write data at amazing scale and speed and distributing the data all over the place achieves this very difficult task.
But, but ...
Don't worry, eventually these copies of data are synchronized to represent a single version of the truth.
But, but ...
The word eventually scare you? It should. You see, in order to write data at all times and scale infinitely, something had to give. That something is the synchronization of distributed data.
Put simply, big data distributes multiple copies of a database, writes to whichever copy is available and synchronizes the data when it can.
What the ...
I know, right? I'm curious if there is a distributed system out there that has ever reached consistency. If the point is to write at all costs because you have such a high volume of updates, when is there time to synchronize?
For those who prefer a more technical definition of eventual consistency:
Eventual consistency is a consistency model used in distributed computing that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value.[1]
How does this necessity of invention sit with you?
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