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. There are also many visuals illustrating MapReduce structure and architecture - just try image search.
But power of art expression can do more with less: with just two paintings we can illustrate:
- variety, richness, and scale of big data
- structure and order MapReduce imposes
- its split-apply-combine nature.
First, we have a painting by Erró Foodscape, 1964:
It illustrates variety, richness, potential of insight if consumed properly, and of course, scale. The painting is explicitly 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, colors, and texture. All these represents big data so much better than any kind of flowchart diagrams.
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 result of split-apply-combine executed on Foodscape items. Indeed, each vertical group is combination of the same type of finished and plated food combined into variably sized groups and ready to serve (better quality image here).
As with any art there is more about MapReduce that was left out of the picture. That's why we still refer to papers, books, and Wikipedia. And again, I'd like to remind of importance of taking your kids to museum.