When it comes to choosing a big data analytics package, your decision likely depends on how well its visualizations convey information and whether or not it integrates well with existing architectures.
That being said, there are environments that hold unstructured information well and there are those that fail to store it with any semblance of efficiency. While we typically focus on data analytics' extensive capabilities, this blog is going to give you a rundown of what you should look for in a big data architecture.
They're behaving like applications
As corporate IT infrastructures are comprised of physical assets and collections of applications, it's appropriate to view them as intelligent, proactive entities that follow direct orders. As of late, IT professionals have been asserting that hardware has become less important, because software capable of virtualizing and provisioning everything from servers to routers can now enhance the way commodity hardware operates.
For example, consider how virtualization can be applied to a router within a company's local area network (LAN). Routers connect LANs to the greater Internet. What virtualization does is take a router and segregate it into multiple logical (or virtual) routers. Each virtual router can be ordered to handle a specific kind of traffic such as video, audio, text and so on.
Essentially, this makes the most out of a router's physical resources and allows other assets within the network to aggregate an organize data at a higher speed.
Is hardware meaningless?
Although software-defined networking, storage and computing profoundly affects a company's ability to handle large amounts of data, that doesn't mean physical assets don't have as equal of an impact.
For instance, Hadoop, an open-source solution designed to support big data, has a number of hardware requirements. Cloudera outlined the server accessories needed to run Hadoop's DataNode/TaskTracker component, which both stores and processes information:
- 12-24 3 terabyte hard disks on a Just a Bunch Of Disks (JBOD) configuration
- 2 quad-/hex-/octo-core central processing units, operating at 2.5 gigahertz (GHz) at the least
- 64-512 gigabytes (GB) of random access memory
- Bonded GB Ethernet (GbE) or 10GbE
This is just one example of how enterprises need to meet particular hardware requirements to satisfy big data needs.
The holistic perspective
Smart Data Collective contributor Aaron Aders recommended organizations take a comprehensive look at their existing infrastructures and identify any vulnerabilities that may hinder data flow, such as inadequate bandwidth or storage capacity. After these problems are assessed, analytics engines can be applied to make as much sense of your data as possible.