Understanding the BigData Visualization Layer
Visualization is an integral part of most data-intensive applications, as it’s not possible to understand their outcomes without visualizing the datasets. This is also the case for the wave of BigData applications, which cope with very large volumes of data. In most cases, data visualization aims at providing ergonomic and user-friendly representations of data-driven outcomes. However, in BigData applications, visualization has two additional goals: First, to boost the identification of insights such as non-obvious or hidden patterns of knowledge, and second, to ease navigation and browsing of very large datasets. As such data visualization in BigData is an integral part of data analysis, which helps end-users of BigData applications to identify knowledge patterns, predict trends and present insights to stakeholders. The visualizations incorporate the outcome of tabular and spatial data in visual formats that are typically more appealing for stakeholders, while at the same time facilitating the representation of ideas.
The importance of visualization has given rise to the introduction of a wide array of diagrams and charts that visualize different aspects and insights present in the data. Likewise, a large number of tools that facilitate the creation of various charts from the source data have emerged. The use of such tools is essential in order to create effective representations of the datasets, while at the same time these tools also enable story creation and story-telling based on large amounts of raw data.
In one of our earlier posts, we presented popular methodologies for developing and deploying data mining applications, such as methodologies based on CRISP-DM (Cross Industry Standard Process for Data Mining) and KDD (Knowledge Discovery in Databases). The activities specified in these methodologies include:
To facilitate data understanding and application-level visualization, data scientists and other stakeholders employ a large number of different diagrams.
There are many different types and diagrams for visualizing datasets. Most of us are quite familiar with the basic diagrams that are part of popular spreadsheet applications, such as histograms, line charts and bar charts. For example, a histogram illustrates datasets based on rectangles that have heights proportional to the count of the data and widths equal to the range of intervals where the data belong. They are suitable for visualizing the distribution of the data. Likewise, line charts are used to depict the evolution of data parameters in relation to other parameters.
Beyond these basic diagrams, BigData projects take advantage of additional types of visualizations, which are effective in consolidating and summarizing very large datasets. These additional diagrams have their roots in both statistics and data mining. Some prominent examples follow:
The above list of visualization types is certainly non-exhaustive. A large number of additional diagrams are used in BigData systems for different purposes and applications.
Note also that all giant vendors offer the advanced tool for data visualization. Prominent examples include the business intelligence tools from Tableau, Google and Oracle, which offer extreme versatility not only in terms of input data sources and formats but also in terms of supported data visualizations.
Visualization is an integral and important part of any non-trivial BigData project. Understanding and deploying the best ways to visualize data is something that could set one apart from competitors. This requires however learning and mastering data visualization types beyond conventional diagrams, and using the right data visualization tools for optimal productivity. While this incurs a significant learning curve, it’s certainly an investment that pays off!
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