What is Big Data?

An article by ewoodesign    Comments Off

The most basic definition of bigdata can be complex, massive data sets that are difficult to handle with normal database management applications. The actual size of big data is ever changing. As of this post, a single big data set may contain anywhere from one terabyte up to several petabytes.

Big data is derived from obtaining additional detail about large, related data sets. The original data sets have like types of information, instead of being unrelated, small data sets. Big data subjects can include, but are definitely not limited to, any the following:

• Online social media

• Medical records

• Internet documents

• Scientific research

• Internet searches

• Genomics

Big data is extremely important, because the detail gleaned from it can reveal similarities and future trends. This information is valuable to every market sector, since it allows deductions and predictions to be made about the direction of business, medicine, social media, and so on. Having access to this information up front can help companies reduce effort, time, and money that may have otherwise been spent on pointless research and marketing endeavors.

While big data provides us with endless opportunities, it also comes with great challenges. Storing, sharing, processing, and analyzing big data takes more time and effort than these activities do with smaller data sets. Software tools that were adequate to manage data in the past are no longer able to keep up. This has technology gurus coming together to brainstorm and create more robust solutions and faster applications for consumers of big data and big datapreparation.

Besides the issues with managing big data, the main challenges face those who try to capture big data. Consider the following obstacles:

• Amount – The enormous volume of data derived from large, related data sets is overwhelming. The data must be loaded, contained, and backed up. Only some relational databases management systems can contain big data, much less optimize it in tables. Therefore, new ways to deal with the influx of data are constantly being considered.

• Speed – The constant stream of information that defines big data needs to be handled quickly. The data arrives non-stop and in real time, and companies want to be able to access specific details about this data within seconds. So, parallel processing and solid-state disks are preferred to using shared storage systems.

• Variety of the data – While big data consists of like topics, the formats and sources of information may vary widely. These disparate formats and sources must be handled rapidly, so that the actual data can be processed and available for consumers quickly. Several of the ways thought-leaders are dealing with the variety of data include crowdsourcing, natural language processing, cluster analysis, neural networks, and machine learning.

Big data allows us amazing opportunities to get ahead of the trend curve, but it brings up many challenges. However, great minds are working to solve these issues every day. Studies agree that big data will continue to grow at a monumental rate. The determining factor of how useful this data is to us depends on how well we can keep up with big data.