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By Harry, Jr Katzan

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High-Cardinality Storage Cardinality refers to the number of unique values a piece of data can take on. Formally, cardinality refers to the size of a set and can be applied to the various dimensions of a dataset as well as the entire dataset itself. This high cardinality often manifests itself in a “long tail” feature of the data. For a given dimension (or combination of dimensions) there is a small set of different states that are quite common, usually accounting for the majority of the observed data, and then a “long tail” of other data states that comprise a fairly small fraction.

As mentioned at the beginning of this chapter, it is assumed that there is some knowledge of the languages discussed in this chapter to get the most of out of the rest of this book. The rest of this book discusses each component described in this chapter in more depth. Chapter 3 begins by discussing the coordination server ZooKeeper, which was mentioned in the section on horizontal scalability. This is a critical piece of software used by several other packages in this book, making it deserving of its own chapter.

These servers are usually application specific, so this book does not spend much time on this part of the environment except to describe the mechanisms for writing directly to the data-flow component. Data Flow Collection, analysis, and reporting systems, with few exceptions, scale and grow at different rates within an organization. indd 05:35:28:PM 06/12/2014 Page 17 17 18 Part I ■ Streaming Analytics Architecture remains stable, but depth of analysis grows, then the analysis infrastructure needs more resources despite the fact that the amount of data collected stays the same.

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