To understand big data, it helps to see how it stacks up — that is, to lay out the components of the architecture. A big data management architecture must include a variety of services that enable companies to make use of myriad data sources in a fast and effective manner.
Here's a closer look at what's in the image and the relationship between the components:
Interfaces and feeds: On either side of the diagram are indications of interfaces and feeds into and out of both internally managed data and data feeds from external sources. To understand how big data works in the real world, start by understanding this necessity.
What makes big data big is that it relies on picking up lots of data from lots of sources. Therefore, open application programming interfaces (APIs) will be core to any big data architecture.
In addition, keep in mind that interfaces exist at every level and between every layer of the stack. Without integration services, big data can't happen.
Redundant physical infrastructure: The supporting physical infrastructure is fundamental to the operation and scalability of a big data architecture. Without the availability of robust physical infrastructures, big data would probably not have emerged as such an important trend.
To support an unanticipated or unpredictable volume of data, a physical infrastructure for big data has to be different than that for traditional data. The physical infrastructure is based on a distributed computing model. This means that data may be physically stored in many different locations and can be linked together through networks, the use of a distributed file system, and various big data analytic tools and applications.
Security infrastructure: The more important big data analysis becomes to companies, the more important it will be to secure that data. For example, if you are a healthcare company, you will probably want to use big data applications to determine changes in demographics or shifts in patient needs.
This data about your constituents needs to be protected both to meet compliance requirements and to protect the patients' privacy. You will need to take into account who is allowed to see the data and under what circumstances they are allowed to do so. You will need to be able to verify the identity of users as well as protect the identity of patients.
Operational data sources: When you think about big data, understand that you have to incorporate all the data sources that will give you a complete picture of your business and see how the data impacts the way you operate your business.
Traditionally, an operational data source consisted of highly structured data managed by the line of business in a relational database. But as the world changes, it is important to understand that operational data now has to encompass a broader set of data sources.
