First, let me say that you can easily bind or use your large datasets to common SVG objects using the functions available in the D3.js libraries.

The data can even be in a variety of formats, most commonly JSON, comma-separated values (CSV), or geoJSON, but, if required, JavaScript functions can be written to read other data format.

However, large isn't big in the sense of big data. Realistically, binding a CSV file of 500 records cannot be likened to binding it to a file of 500,000 records.

So, can D3 really help in the context of big data?

Since it is low-level, D3 may seem like a bad fit for big data visualization projects. The D3.js libraries just won't work with gigabytes of data, but once you perform some preprocessing on the data, D3 can help make sense of the results.

In fact, in each of the previous chapters of this book, Chapter 2, Access, Speed, and Storage with Hadoop, (where we loaded data into a Hadoop environment and then used Hive to manipulate that data into workable...