Big data is an evolutionary term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be extracted to obtain information.
Large data is often characterized by three Vs: the extreme volume of data, the large variety of data types, and the speed at which data should be processed. Although large data does not equal any specific volume of data, the term is often used to describe terabytes, petabytes, and even exabytes of data captured over time.
Breaking the 3 Vs of big data
Such voluminous data can come from countless different sources, such as commercial sales records, the results collected from scientific experiments or real-time sensors used in the Internet of Things (IoT). The data can be raw or preprocessed using independent software tools before the analyzes are applied.
Data can also exist in a wide variety of file types, including structured data, such as SQL database stores; unstructured data, such as document files; or data transmission from sensors. In addition, big data can include multiple simultaneous data sources, which otherwise could not be integrated. For example, a large data analysis project may attempt to measure the success of a product and future sales by correlating past sales data, return data, and online buyer review data for that product.
Finally, speed refers to the length of time in which large volumes of data must be analyzed. Each large data analysis project will ingest, correlate and analyze the data sources, and then provide a response or result based on a general query. This means that human analysts must have a detailed understanding of the available data and have some sense of what answer they are looking for.
Speed is also significant, since data analysis expands in fields such as machine learning and artificial intelligence, where analytical processes mimic perception through the search and use of patterns in the collected data.
Big data in infrastructure demands
The need for large data rates imposes unique demands on the underlying computing infrastructure. The computing power needed to quickly process large volumes and varieties of data can overload a single server or server cluster. Organizations must apply the appropriate computing power to big data tasks to achieve the desired speed. This can potentially demand hundreds or thousands of servers that can distribute the work and operate collaboratively.
Reaching that speed in a cost-effective way is also a headache. Many business leaders are reluctant to invest in an extensive server and storage infrastructure that can only be used occasionally to complete big data tasks. As a result, public cloud computing has emerged as a primary vehicle for hosting large data analysis projects. A public cloud provider can store petabytes of data and scale thousands of servers long enough to complete the big data project. The business only pays for the storage and calculation time actually used, and the cloud instances can be disabled until they are needed again.
To further improve service levels, some public cloud providers offer large data capabilities, such as highly distributed Hadoop computing instances, data warehouses, databases, and other cloud-related services. Amazon Web Services Elastic MapReduce (Amazon EMR) is an example of big data services in a public cloud.
The human side of big data analytics
Ultimately, the value and effectiveness of big data depends on the human operators in charge of understanding the data and formulating the appropriate queries to manage big data projects. Some large data tools have specialized niches and allow less technical users to make several predictions from everyday business data. However, other tools are emerging, such as Hadoop devices, to help companies implement an adequate computing infrastructure to address large data projects, minimizing the need for hardware and knowledge of distributed computing software.
But these tools only address limited use cases. Many other large data tasks, such as determining the effectiveness of a new drug, may require extensive scientific and computational experience.