What Is Big Data Technology?

what is big data technology

What Is Big Data Technology?

A software tool to analyze, manipulate and interpret the huge amount of unstructured and structured data which cannot be processed traditionally or manually is known as Big Data Technology. This helps in formulating predictions and taking preventive steps so many risks can be avoided. Experts opine that predicting the future is close to impossible considering the number of factors which can influence its evolution or direction. However, with the use of advanced Big Data technologies a cautious approach towards the changing times can be adopted to minimize the uncertainties.

The tools available for handling extremely complex data structures are generally referred to as supercomputers or clusters. It has a significant impact on the ability to deliver, store and process data. Large companies with extensive collection of data require extremely complex systems in order to access and analyze them. The main goal behind developing these systems is to enable companies to make better decisions in terms of product deployment, in order to reduce operational cost and at the same time increase sales performance.

Today’s internet users have become obsessed with the concept of social media marketing. It is necessary to tap into the enormous potential of this concept has to offer. Social media allows users to communicate and interact with each other and with companies that they may not be able to directly connect with through traditional means such as the web or emails. There are several tools used for generating social media signals; the most prominent among them are Twitter, Facebook and Google+ among others.

Businesses today are increasingly relying on Big Data technology to derive intelligence from their existing databases and personal data. In doing so, it becomes possible for them to extract actionable insights from their databases. To create such analytics, expertise is required to manage the databases. Big data management entails building and maintaining large-scale, complex databases, as well as using advanced techniques for data visualization and analysis. In addition, it involves the creation of applications that can be run on mobile devices, tablets and laptops.

Companies that need to use big data technology for their own needs may either opt to outsource their data management requirements or to integrate Google Analytics with their own web analytics systems. For instance, PrestoWeb, a web analytics company, works with Yahoo! Answers and other community websites in order to deliver dynamic and customised queries powered by the Google Presto query engine.

In essence, Big Data can be described as data sets that span multiple dimensions in dimension, space and time. There are numerous dimensions and there are several dimensions that need to be analyzed. A typical database consists of table, rows and columns. Big Data can be classified into text, data, information and metadata. Text data is all of the common data such as incoming emails, user names and passwords; while metadata consists of such things as topic categories, names of individuals, descriptions of topics, names of companies, and so on. In fact, metadata has become very important because it now enables us to access the contents of large quantities of unstructured big data sets without the aid of database management systems.

Data visualization is also a key feature of big data technologies. In simple terms, visualizing data enables users to visualize patterns, relationships among the elements in the database. This in turn allows users to make insightful analysis and predictions. Data visualizations are typically accomplished using both raster and vector formats, but some software vendors have developed libraries specifically for the raster format.

Another interesting aspect of data visualization is the concept of unstructured data lakes. Unstructured data lakes are graphical arrangements of unprocessed data in which users can explore the lakes in terms of what is common or typical. Typically, unstructured data lakes contain only two or three major categories which are highly relevant to the user’s query. Examples of unstructured data lakes are consumer price index (CPI) baskets, real time stock quotes, financial index baskets, health insurance data, and commodity market data.