What is Big Data and How Does It Work?
The landscape of data handling has evolved over the last few decades, becoming a complex stream of information integrated heavily into our daily lives; Sensor outputs, social media, mobile communication, and web interfaces are just a few examples from the exhaustive list of data outlets we consume. What was once deemed “data processing” now has taken on many names, one of which is has been coined “Big Data”. But when does data become “Big”? The term itself is not necessarily just indicative of size, but rather the ingestion, manipulation, storage, and structure applied to large data sets that may not be suitable for typical relational database solutions.
Understanding Big Data
To properly understand Big Data, you must first understand that it is comprised of data sets, which are essentially “groups” of structured data that is interrelated, often organized in database tables. To be considered Big Data, typically the size of the data sets must be large enough to surpass the capabilities of an environment’s relational database management systems, although a common benchmark for defining a need for big data is if it meets the criteria of the “3Vs” model: volume, variety and velocity. Volume, of course, is how much data is being processed. Variety defines the many forms and formats of data that are collected. Velocity is the speed at which the data is being collected through various platforms.
A common reference to big data usage is platforms like Facebook or Twitter, each of which respectively handle hundreds of millions of active users, with massive collections of data being processed and shared daily. While these are both excellent examples of use cases for big data, there are many more applications that are not quite as apparent. As mentioned previously, big data can be described as large data sets that surpass your environment’s capabilities. As each environment is different, so are the bottlenecks that may necessitate a big data solution. It’s important to note that big data, in most cases, will not replace your relational database systems, but rather be utilized to complement them. So even if your organization is not regularly processing petabytes of high-velocity data, like Facebook and Twitter, a big data solution could be a valuable asset for offloading resource-intensive tasks that free up availability for other processes while possibly mitigating the need for upgrading your hardware resources.