Just like the word “abyte” (which is another term for “big data”), a newly popular unit of data in the big data era is the petabyte. This is a measurement, not an exact definition, of a storage space of 1 Terabyte or one Terabyte. In terabytes, a smaller number of gigabytes could be stored.
The petabyte is increasingly challenging task for IT managers and IT administrators. More companies are relying on their IT infrastructure to store and retrieve their critical data. IT systems are rapidly approaching physical limitations in terms of storage capacity. Storage capacities continue to grow as a result of the increased use of mobile devices, cloud computing, and hybrid networks. These technologies are also expanding the availability of cloud-based services, which are inherently more reliable and easier to use than legacy applications and software. This calls for greater focus on management attention in developing and implementing effective storage policies.
Managing data warehouses and their underlying infrastructure are vital aspects of this initiative. Big data warehouses are constructed using a variety of approaches. Historically, data warehouses were constructed using a single server but as networks evolved, multiple servers were required. With virtualization, flexibility was added by allowing multiple servers to be co-located without reconfiguring other VLAN’s or moving workstations to a new location. With the evolution of cloud computing, it has become much easier to manage the use of virtualization in data warehouses.
As more companies require more storage than the traditional method of shared hosting allows, there is an increased need for reliable, flexible, and fast access to this additional storage. Virtualization, while making provisioning and managing storage easier, also increases the overhead associated with operating the necessary analytic tools. The combination of these two forces is causing a fundamental change in how IT managers approach the design and construction of data warehouses and analytics pipelines. This shift is being called the New Analytics Centric Management Approach (NAM).
Companies that have opted for NAM are already seeing a marked improvement in both the speed at which analytical tools can be accessed and the quality of the resulting data stream. Faster access speeds result in fewer interruptions to the user when the required information is obtained. Quality is improved as the same data is processed more efficiently, reducing the time needed to retrieve the relevant results from the server. In essence, the data produced by NAM is far more relevant and valuable to end users. This improvement in efficiency will lead to a shift from a traditional view of data warehouses and analytics towards a new perspective called perpetual analytics.
Perpetual analytics is a concept that goes beyond the question of efficiency by moving beyond the traditional view of answering the question of “how much data do I need?” Instead of looking at the quantity of available data to determine the appropriate amount, Perpetual Analytics identifies the value of data in relation to the amount of effort that would be required to maintain a particular data warehouse or analytics pipeline. A typical solution might include the collection of historical data over a certain period of time in order to establish a more thorough picture of the data’s changing characteristics. With this knowledge, managers can establish how much resource they would need to maintain a particular data warehouse over the course of a given time period.
As this idea becomes more widely understood, managers will begin to realize that they must carefully manage the resources within their data warehouses and analytics pipelines in order to meet their goals. This will increasingly challenge them to determine which data resources are most critical to achieving their objectives. In addition to the challenge of managing the resources, NAM will also become an increasingly difficult question to answer as organizations discover new ways to collect more data in less time.
As organizations move closer to leveraging their hardware and networking infrastructures to deliver this new data, a two-page response to the original question of what is a Petabyte of data will become increasingly important. Organizations may find that they have multiple analytic processes running at the same time or that they have only a small amount of capacity to store a great deal of data. In either case, a two page reframing to answer the original question of what is a Petabyte of data will prove to be extremely effective. Data management through a higher level of automation and better resource planning is the real winners here.