Today's blog continues yesterday's discussion of Big Data Immutability.
Big Data managers must do what seems to be impossible; they must learn how to modify data without altering the original content. The trick is accomplished with identifiers and time-stamps attached to event data (and yes, it's all discussed at greater length in my book, Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information).
In today's blog, let's just focus on the concept of a time-stamp. Temporal events must be given a time-stamp indicating the time that the event occurred, using a standard measurement for time. The time-stamp must be accurate, persistent, and immutable.
Time-stamps are not tamper-proof. In many instances, changing a recorded time residing in a file or data set requires nothing more than viewing the data on your computer screen and substituting one date and time for another. Dates that are automatically recorded, by your computer system, can also be altered. Operating systems permit users to reset the system date and time. Because the timing of events can be altered, scrupulous data managers employ a trusted time-stamp protocol by which a time-stamp can be verified.
Here is a description of how a trusted time-stamp protocol might work. You have just created a message, and you need to document that the message existed on the current date. You create a one-way hash on the message (a fixed-length sequence of seemingly random alphanumeric characters). You send the one-way hash sequence to your city's newspaper, with instructions to publish the sequence in the classified section of that day's late edition. You're done. Anyone questioning whether the message really existed on that particular date can perform their own one-way has on the message and compare the sequence with the sequence that was published in the city newspaper on that date. The sequences will be identical to each other.
Today, newspapers are seldom used in trusted time stamp protocols. Cautious Big Data managers employ trusted time authorities and encrypted time values to create authenticated and verifiable time-stamp data. It's all done quickly and transparently, and you end up with event data (log-ins, transactions, quantities received, observations, etc.) that are associated with an identifier, a time, and a descriptor (e.g., a tag that explains the data). When new events occur, they can be added to a data object containing related event data. The idea behind all this activity is that old data need never be replaced by new data. Your data object will always contain the information needed to distinguish one event from another, so that you can choose the event data that is appropriate to your query or your analysis.
key words: Big Data, mutable, mutability, data persistence, time stamp, time stamping, encrypted time stamp, data object, time-stamping an event, archiving, dystopia, George Orwell, newspeak, persistence, persistent data, saving data, time-stamp
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