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Big data is not a single technology but a combination of old and new technologies that helps companies gain actionable insight.
Big data is the capability to manage a huge volume of disparate data, at the right speed, and within the right time frame to allow real-time analysis and reaction. Big data is typically broken down by three characteristics:
  • Volume: How much data
  • Velocity: How fast that data is processed
  • Variety: The various types of data
Big data incorporates all data, including structured data and unstructured data from e-mail, social media, text streams, and more. This kind of data management requires that companies leverage both their structured and unstructured data.
To understand how big data works in the real world, it is important to start by understanding this necessity. In fact, what makes big data big is the fact that it relies on picking up lots of data from lots of sources. 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.
Big Data Stack
The supporting physical infrastructure is fundamental to the operation and scalability of a big data architecture. In fact, without the availability of robust physical infrastructures, big data would probably not have emerged as such an important trend. 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. Redundancy is important because we are dealing with so much data from so many different sources.
It is important to understand that you have to incorporate all the data sources that will give you a complete picture of your business. New emerging approaches to data management in the big data world, including document, graph, columnar, and geospatial database architectures. Collectively, these are referred to as NoSQL, or not only SQL, databases.
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