What is the Biggest Unit of Data in the World?

What is a newly popular unit of data in the big data era? The answer may surprise you. It is the petabyte, a measurement of the amount of data storage that can be stored on a single device.

a newly popular unit of data in the big data era is the petabyte

This may not sound particularly earth shattering but it really is important to think about as business users are increasingly challenging every existing conventional notion of data security. For example, there is an increasing recognition that information stored, processed and shared across wide distributed networks and devices has a number of inherent risks. In addition, the amount of data being stored is increasing at a very fast rate. Therefore, data security is a major challenge and must be addressed with agility and grace in the modern world.

Data in its various forms is inherently highly complex. It consists of both structured and unstructured data. When large data elements are processed efficiently and effectively they provide a robust solution for any problem solving objective. These capabilities are now available in an RDF (Resource Description Framework) based Software called RDF-GC.

There is a tremendous opportunity to apply highly advanced analytical processing technologies to a variety of data warehouses. This is the primary objective of many modern data warehouse initiatives. Data science provides a foundation for building highly secure data warehouses that are capable of providing a tremendous amount of insight into strategic business choices. While this is a relatively new area of endeavor for many in the analytics community, it is a fast growing field and advancing technology is contributing greatly to this focus. As more capabilities and applications are obtained it will no doubt become even more popular.

The second objective is to enable an analyst to process terabytes of data with only a few queries or a few hundred keystrokes. This is what is becoming known as a “2-page ref” in the Data Analytics community. A “2-page ref” is a term used to describe a data warehouse that is very lean in structure but allows a significant amount of analytics capabilities through a few selected data warehouses.

Companies and individual user access points are leveraging the new capabilities of the terabytes of information at their disposal through a series of tools that are emerging across the community. The two primary tools being considered most relevant to the consumer front end user are mapreduce and visual store. Mapreduce is a general purpose framework that allows users to efficiently extract large amounts of data from large amounts of sources and to then create and evaluate many different types of business intelligence visualizations. In essence it is a framework that allows data scientists to answer the age-old question of how does a data warehouse actually work?

Visual store allows a data scientist to rapidly visualize complex algorithms, managing tens to hundreds of thousands of nodes. It is necessary for a modern data warehouse to be able to rapidly visualize large volumes of unprocessed data, which is why many of these tools are being called “supercomputers” by the likes of Amazon and Netflix. The lack of mature management means that many of these tools lack a mechanism to handle extreme spikes in traffic, something that has become a serious problem in recent times due to the influx of small and medium enterprise applications onto the cloud. This issue will only be exacerbated by the fact that most of the biggest names in cloud computing have had to deal with this issue already with AWS, so there is a real urgency for these organizations to ensure that they have the ability to scale appropriately.

So what is the answer to the question of what is the biggest piece of data in the world? The answer is most certainly going to continue to evolve, becoming more difficult to define as the underlying technologies behind them become more important. It is becoming increasingly difficult to write programs for handling large workloads given the fact that the underlying technologies are continuously developing. The next few months will see many more technologies emerge to deal with this pressing problem.