Two years ago we had a rare family outing to the Dallas Museum of Art (my son is teenager and he’s into sport after all). It had an excellent exhibition of modern art and DMA allowed taking pictures. Two hours and dozen of pictures later my weekend was over but thanks to Google Photos I just stumbled upon those pictures again. Suddenly, I realized that two paintings I captured make up an illustration of one of the most important concepts in big data.
There are multiple papers, tutorials and web pages about MapReduce and to truly understand and use it one should study at least a few thoroughly. And there are many illustrations of MapReduce structure and architecture out there.
But the power of art can express more with less with just two paintings. First, we have work by Erró Foodscape, 1964:
It illustrates variety, richness, potential of insight if consumed properly, and of course, scale. The painting is boundless emphasizing no end to the table surface in all 4 directions. If we zoom in (you can find better quality image here) it contains many types of food and drinks, packaging, presentations, varying in colors, texture and origin. All these represent big data so much better than any kind of flowchart diagram.
The 2d and final painting is by Wayne Thiebaud Salads, Sandwiches, and Desserts, 1962:
Should we think of how MapReduce works this seemingly infinite table (also fittingly resembling conveyor line) looks like a result of split-apply-combine executed on Foodscape items. Indeed, each vertical group is combination of the same type of finished and plated food ready to serve and combined into variably sized sets (find better quality image here).
And again, I’d like to remind of importance of taking your kids to museum.