Newly Popular Unit of Data In The Big Data Era Is The Petabyte
A terabyte is a measurement of space that is used to describe a certain amount of data that can be stored on one storage device. Petabytes of data may at times seem like an impossible storage capacity, but the truth is that terabytes are far more than enough space to store anything that you want to store. A petabyte is equal to 22 megabytes. That amount of space is equivalent to about five hundred million gigabytes of data, or about three hundred times the capacity of the United States Library.
So what is a petabyte and how is it important to the enterprise? The answer lies in the ever-increasing challenge of managing the ever-growing amount of data that is being put online every day. Data is no longer restricted to the files that you may keep on your computer, but also includes the images, videos, audio, and even text that are added by e-mail, instant message, and social networking websites. Managing this data requires a lot of computing power, and this is precisely what a processor does. On the devices that these processors are powering, a new unit of measurement known as a processor is used.
This processor is a complex piece of hardware, and managing this processor can be a daunting task for IT managers and executives. This is why a new unit of measurement has been introduced to help manage this workload. The question that needs to be answered is whether the existing definition of a petabyte should be changed? Should the petabyte be changed to a smaller more precise device? Or is the current definition of a petabyte sufficient?
The answer to this question will have a profound impact on how people manage their data. It is unlikely that any data scientist would support changing the definition of a petabyte. This is because there is a fundamental difficulty with the question posed in the title. This difficulty is related to the difficulty of answering a question with a single number. The problem posed by asking “how much data are you handling?”
As a result of this difficulty many data scientists believe that it is not important to measure the amount of data that is being handled at any one time. Instead, the question should be more accurately stated as, “how much data are you processing per second?” This change in the question would make the answer much more meaningful to an IT manager. The question is usually asked of IT managers when deploying a dashboard or map-reduce project. Once the infrastructure is in place and running then the question becomes more specific. In other words the question becomes, “How much analytics are you using per second?”
The answer to the question may change over time. However the core answer will remain consistent. Persistent analytics will lead to perpetual analytics. Persistent analytics will lead to robust, truly meaningful answers to the question posed in the title. To get to a truly meaningful answer to the question of how much data are you handling per second you need to deploy a robust streaming analytics system.
Streaming analytics provide an accurate answer to the question of how much data are you storing per second. However it is not enough to just make an easy to understand 2-page report. A true false diff solution uses both streaming analytics and true false diff reporting to answer the question. A true false diff report for a two-page report typically looks something like this:
In my opinion the right solution uses both streaming analytics and true false diff reports. In order for users to understand how much data are they keeping in their data warehouses they should be able to use both metrics at the same time. The current industry standard for storage is Terabytes. If your company only stores a few Terabytes you can probably use streaming analytics with a terabyte or less report. If your business holds multiple terabytes you should use true false diff metrics as well.