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To fully understand big data, you need to know its history. The movement began about fifteen years ago with the emergence of data that were too large to be processed with traditional techniques such as relational databases and SQL queries.
Search engines were faced with massive volumes of unstructured data. Faced with these challenges, Google created the Big Table, a compressed database, in 2001, and the MapReduce algorithm, which it published in 2004.
Doug Cutting then realized the potential of this technology. Hadoop, which is now a benchmark in the big data world, has released its open-source prototype.
With its highly distributed file system, Hadoop processes large volumes of unstructured data. Although the topic of big data emerged in the early 2000s, the term "Big data" was only heard on the Web in mid-2010.
Therefore, big data processes traditional storage from various sources and produced in real time. Represents large volumes of structured or unstructured data that are difficult to manage with processing solutions.
Big data is the meeting point between the proliferation of unstructured data, the need to analyze this data, and the advancement of technology.
It's hard to talk fully about big data. This varies from one industry to another as the means and volumes of managed data are not the same. Therefore, banks have much larger databases than construction companies.
Moreover, due to the continuous advancement of technology, big data will not be the same in 2030 as it was defined in 2015.
But in general, we can say that for the coming years, big data will take place in a range ranging from tens of terabytes to a few petabytes and even exabytes, depending on the industry.
However, volume is only an auxiliary element. The possibilities of analyzing this big data are attracting much more attention.
Analysis of bulky data often leads to numerical results that are difficult to interpret because they are not very meaningful. It is not always easy to make sense of even very simple number tables.
The main purpose of data visualization is to transform data into a relevant, simple, informative and educational visual form. (called Data Visualization or Dataviz in Anglo-Saxon countries)
It is therefore a matter of constructing a graph, an image, or a diagram adapted to the information one wants to convey.
Computer resources now make it very easy to produce two- or three-dimensional graphical representations, whether in color or not.
With the advent of big data and the growing emphasis on data analytics, it makes it possible to communicate, understand and improve big data analytics results. There is a boiling point in these visualization techniques (pictures, diagrams, animations).
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Types of Big Data Visualization
Among the many visualization possibilities, let's talk about the most used in the context of big data.
These are histograms, curves, sectors, pie charts, areas, bubbles, etc. found in most traditional data manipulation tools, including office automation such as Excel.
These tools are still suitable for presenting the results of analyzes related to voluminous data of the Big Data type.
These charts allow decision makers to quickly analyze data and reduce the time it takes to understand the basics. The relevant notations are therefore simple.
This type of visualization is very common in business, especially for internal or external communication. Marketing, finance, management control and general management departments make extensive use of these visualization tools in the form of indicators to convey turnover or net income.
With the explosion in data volume, we have been witnessing a search for creativity in the presentation of data with more aesthetic and even artistic visualizations for years. The rise of data journalism contributes to this phenomenon, which finds its inspiration in computer graphics.
Find out more about Dr. Ayavefe and his work here:
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