Data visualization is at the center of every stage in the data analytics life cycle. It is especially important for exploratory analysis and for communicating results. In either case, the goal is to transform data into a format that's efficient for human consumption. The approach of delegating the transformation to client-side libraries does not scale to large datasets. The transformation has to happen on the server side, sending only the relevant data to the client for rendering. Most of the common transformations are available in Apache Spark out of the box. Let's have a closer look at these transformations.
Summarizing and visualizing is a technique used by many Business Intelligence (BI) tools. Since summarization will be a concise dataset regardless of the size of the underlying dataset, the graphs look simple enough and easy to render. There are various ways to summarize the data such as aggregating, pivoting, and so on. If the...