There are many data visualization libraries available for the data scientist to create narratives. Plotly, matplotlib, seaborn, ggplot2, D3.js, … the list is long. However, when the data gets large and the memory gets low these libraries struggle to keep up.
These simple concepts allow for the creation of a variety of
powerful big-data visualizations. Several well explained examples are available
in the github repository.
Salt is a visualization library that leverages Apache Spark to
create big-data visualization. It is built around 2 concepts:
1) Dimension reduction: i.e. transforming the data space into the a smaller visualization space.
2) Data aggregation: values in the visualization space which are
close to each other are grouped via a collection of seven sample aggregators.
You will need the Docker, a Java compiler, Gradle (automation
system builder) and Node + npm to be installed on your local in order to run
these examples.
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