The term “petabytes” is one that may not ring a bell for computer users. This is simply because this phrase is new to them. In fact, they cannot even begin to imagine how such a small amount of data can be of use. But the fact is that petabytes is a term that is far more practical in the context of big data and storage.
The meaning of the term is: A Terabyte is 1 trillion bytes. Hence, it can be seen as the amount of information recorded by the human mind in a lifetime. Today, as the world continues to grow busier and with each passing day, the amount of data produced is increasing even more. This has forced the storage of data centers to increase as well. This is making data security and managing the resources more difficult for organizations.
However, this problem is not as tough as it sounds. Large data elements such as large databases, streaming audio and video content, large multimedia files, web-based emails, and the like can easily fit into the memory of a single computer. This makes managing and accessing this information less demanding and less time consuming. With this technology, any organization can now have a single server with various different applications running on it that processes data in a highly efficient manner. Large companies and government agencies are making use of this in-memory analytics in order to increase efficiency and reduce costs.
What’s more? It can break down large data elements into multiple computers. This is done with the aid of high-speed internet connection and compact machines. The advantage of using multiple computers for processing is that you can use each one at a time for different purposes. If a particular query requires more computation, it can be performed on a computer with more capacity while the result can be computed on a second computer having lower capacity.
However, petabytes in itself do not guarantee high quality analytics. There are many tools and softwares that are available in the market today that help petabytes be categorized into meaningful chunks. The availability of terabytes enables the data scientists to combine the big data infrastructure with advanced analytical tools.
On the other hand, what defines a good data warehouse is how it answers questions. Data scientists use several different types of analytic tools in order to answer questions that these questionnaires pose. These tools include online or streaming analytics, predictive analytics, and the like. Online or streaming analytics refers to the ability of the user to query the information through the Internet whereas predictive analytics and the like concern themselves with past data streams and their forecastability.
When speaking of a data warehouse, one cannot leave out the term defragmentation. Defragmentation is perhaps one of the most important among the different types of analytics that are used in the data warehouses. Defragmentation is the process of reorganizing large files and database files so that they can be accessed more quickly by multiple users. Perpetual analytics refers to the ability of the software to analyze the same data repeatedly without any loss of information or detail. A two page segmentation is a type of continuous analytics where the same set of data is accessed by two different users.
In a nutshell, the data warehouse describes a way of managing the huge amount of data that is produced in the enterprise. Today, many companies are investing in this concept of managing data warehouses. To answer the question, a 2 page segmentation is more than adequate as a business data warehouse will manage and house each of the 30 inch screen of the laptops of every user in the company. This gives us the answer as to why a newly popular unit of data in the big data era is the petabyte file.